ChIP-Seq results from two independent experiments identified 165 enriched loci harboring 582 EspR binding peaks. Of these loci, 45% were intergenic and 55% were intragenic. EspR consensus sequence derived from ChIP-Seq peaks was identified using the MEME suite. ChIP-qPCR on 11 selected sites confirmed the peaks obtained from ChIP-Seq analysis. EMSA using ca. 100 bp DNA fragments of the top five binding sites confirmed that EspR bound to these sequences. Direct autorepression at the espR promoter was confirmed by EMSA and DNase I footprinting. EspR-mediated regulation of genes related to the top 10 EspR binding peaks obtained in ChIP-Seq was verified by performing qRT-PCR.
ChIP assay conditions
Mtb H37Rv cultures (50 ml) grown to OD600 0.4–0.6 were treated with formaldehyde (final concentration 1%) for 10 min at 37°C. Cross-linking was quenched by addition of glycine to a final concentration of 125 mM.
ChIP notes
Harvested cells were washed twice with Tris-buffered saline (20 mM Tris-HCl pH 7.5, 150 mM NaCl), re-suspended in 600 µl Immunoprecipitation (IP) buffer (50 mM Hepes-KOH pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, mini-protease inhibitor cocktail (Roche)) and sonicated to shear DNA to an average size of 100–500 bp. Insoluble cellular debris was removed by centrifugation and the supernatant used as input sample in IP experiments. 300 µl of input was incubated with either no antibody (mock-IP) or 20 µl of serum containing rat anti-EspR polyclonal antibodies (kindly generated by Ida Rosenkrands) and then with 50 µl of Dynabeads sheep anti-rat IgG (Dynal Biotech) pre-saturated with 0.1 mg/ml salmon sperm DNA and 1 mg/ml Bovine Serum Albumin (BSA) in IP buffer. Washing and crosslink reversal of protein-DNA complexes, as well as purification of the resulting DNA was carried out as previously described.
Regulated genes for each binding site are displayed below. Gene regulation diagrams
show binding sites, positively-regulated genes,
negatively-regulated genes,
both positively and negatively regulated
genes, genes with unspecified type of regulation.
For each indvidual site, experimental techniques used to determine the site are also given.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.