A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human

Bioinformatics
Cheng LiangZhaolei Zhang

Abstract

Interplays between transcription factors (TFs) and microRNAs (miRNAs) in gene regulation are implicated in various physiological processes. It is thus important to identify biologically meaningful network motifs involving both types of regulators to understand the key co-regulatory mechanisms underlying the cellular identity and function. However, existing motif finders do not scale well for large networks and are not designed specifically for co-regulatory networks. In this study, we propose a novel algorithm CoMoFinder to accurately and efficiently identify composite network motifs in genome-scale co-regulatory networks. We define composite network motifs as network patterns involving at least one TF, one miRNA and one target gene that are statistically significant than expected. Using two published disease-related co-regulatory networks, we show that CoMoFinder outperforms existing methods in both accuracy and robustness. We then applied CoMoFinder to human TF-miRNA co-regulatory network derived from The Encyclopedia of DNA Elements project and identified 44 recurring composite network motifs of size 4. The functional analysis revealed that genes involved in the 44 motifs are enriched for significantly higher number of biolo...Continue Reading

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Citations

Jul 5, 2017·Journal of Integrative Bioinformatics·Sepideh SadeghVolkhard Helms
Jun 6, 2018·IEEE Transactions on Nanobioscience·Jiawei LuoNguyen Hoang Tu
Dec 14, 2017·Bioinformatics·Mitra AnsariolaDavid Koslicki
Oct 1, 2019·Briefings in Bioinformatics·Pingjian DingChee-Keong Kwoh
Aug 2, 2020·IET Systems Biology·Sabyasachi Patra, Anjali Mohapatra
Sep 21, 2019·IET Systems Biology·Sabyasachi Patra, Anjali Mohapatra

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