iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections
KU Leuven · VIB-KU Leuven Center for Cancer Biology
Abstract
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…
Citation impact
- FWCI
- 18.92
- Percentile
- 100%
- References
- 141
Authors
15Topics & keywords
- ENCODE
- Computational biology
- Gene regulatory network
- Gene
- Biology
- Inference
- UniGene
- Computer science