ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.

ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.

ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.

ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.

ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.

ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.

ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.

ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.