Abstract

The identification and characterization of functional cis-regulatory elements in eukaryotic genomes remains a key challenge in genome biology. We present a computational framework to analyse a human, mouse, or fly gene network or gene signature and to confidently identify cis-regulatory modules and transcription factor (TF) binding sites. The framework uses Hidden Markov Models to identify motif clusters, combined with comparative genomics cues, rank statistics to identify enriched motifs, and a “motif2TF” step to prioritize candidate transcription factors (TF) for each enriched motif. The Drosophila version of our method (called i-cisTarget) utilizes large collections of motifs (>6000 position weight matrices) and of ‘regulatory tracks’ (> 500 data sets) as cues, including the entire modEncode and BDTNP data sets. The human version of our method (called iRegulon) works as a Cytoscape plugin and thereby integrates cis-regulatory sequence analysis with network biology.

To illustrate our methods, we show two case studies, one in Drosophila retinal determination and the second in human cancer. For the Drosophila case study we have performed cross-species transcriptomics by next-generation sequencing across three Drosophila species and obtained a highly conserved core set of eye-specific genes. Motif and CRM discovery unveiled a regulatory network downstream of the transcription factor Glass, which we validated by RNA -Seq in glass mutant eyes and by in vivo enhancer-reporter assays. For the human case study we have performed RNA -Seq and ChIP-Seq analysis for p53 in a breast cancer cell line and show how iRegulon successfully identifies known and novel p53 binding sites and target genes. Encouraged by these results, we applied iRegulon to more than twenty thousand cancer gene signatures obtained both from signature databases and from bi-clustering 91 large cancer gene expression datasets, and defined for each TF a context-free “meta-targetome”. In conclusion, i-cisTarget and iRegulon are next-generation motif discovery methods that confidently identify master regulators and bona fide direct targets from sets of co-regulated genes.