Imogene: identification of motifs and cis-regulatory

Cis-regulatory modules (CRMs) and motifs play a central role in tissue and condition-specific gene ex-pression. Here we present Imogene, an ensemble of statistical tools that we have developed to facil-itate their identification and implemented in a pub-licly available software. Starting from a small train-ing set of mammalian or fly CRMs that drive sim-ilar gene expression profiles, Imogene determines de novo cis-regulatory motifs that underlie this co-expression. It can then predict on a genome-wide scale other CRMs with a regulatory potential simi-lar to the training set. Imogene bypasses the need of large datasets for statistical analyses by making central use of the information provided by the se-quenced genomes of multiple species, based on the developed statistical tools and explicit models for transcription factor binding site evolution. We test Imogene on characterized tissue-specific mouse de-velopmental CRMs. Its ability to identify CRMs with the same specificity based on its de novo created motifs is comparable to that of previously evaluated ‘motif-blind ’ methods. We further show, both in flies and in mammals, that Imogene de novo generated motifs are sufficient to discriminate CRMs related to different developmental programs. Notably, purely relying on sequence data, Imogene performs as well in this discrimination task as a previously reported learning algorithm based on Chromatin Immunopre-cipitation (ChIP) data for multiple transcription fac-tors at multiple developmental stages