This is a weak form of in-silico search, in which the consensus sequence for the motif is compared to genomic positions and the number of mismatches (between candidate site and consensus) is used as a measure of site-quality.

DNA-arrays (or DNA-chips or microarrays) are flat slabs of glass, silicon or plastic onto which thousands of multiple short single-stranded (ss) DNA sequences (corresponding to small regions of a genome) have been attached. After performing a mRNA extraction in induced and non-induced cells, the mRNA is again reverse transcribed, but here the reaction is tweaked, so that the emerging cDNA contains nucleotides marked with different fluorophores for controls and experiment. Targets will hybridize by base-pairing with those probes that resemble them the most. The array can then be stimulated by a laser and scanned for fluorescence at two different wavelengths (control and induced). The ratio or log-ratio between the two fluorescence intensities corresponds to the induction level.

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