iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

• DOI: 10.1371/journal.pcbi.1003731
• Publication Date: 2014-07-24T14:44:34Z
• Authors: Rekin's Janky; Annelien Verfaillie; Hana Imrichová; Bram Van de Sande; Laura Standaert; Valerie Christiaens; Gert Hulselmans; Koen Herten; Marina Naval Sanchez; Delphine Potier; Dmitry Svetlichnyy; Zeynep Kalender Atak; Mark Fiers; Jean-Christophe M...; Stein Aerts
• Keywords: 0301 basic medicine; Chromatin Immunoprecipitation; QH301-705.5; Wide Identification; Breast Neoplasms; 03 medical and health sciences; Expression Analysis; Cell Line, Tumor; Databases, Genetic; Humans; Gene Regulatory Networks; Nf-Y; Biology (General); Models, Genetic; Sequence Analysis, RNA; Target Genes; Computational Biology; Protein-Dna Interactions; 006; Multiple Sequence Alignment; Genes, p53; 3. Good health; Gene Expression Regulation; Chip-Seq Datasets; Transcription Factor-Binding; Over-Representation; Integrative Genomics Viewer; Research Article; Transcription Factors

Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.