Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. Researchers at the KU Leuven Center for Human Genetics 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. They 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. They validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and they compare iRegulon with existing motif discovery methods. Next, the researchers use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, they 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 they map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, the researchers 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.
Availability – iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.