Logo image
Back
Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses
Journal article   Open access   Peer reviewed

Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses

Bingqiang Liu, Chuan Zhou, Guojun Li, Hanyuan Zhang, Erliang Zeng, Qi Liu and Qin Ma
Scientific reports, Vol.6(1), pp.23030-23030
03/15/2016
DOI: 10.1038/srep23030
PMCID: PMC4792141
PMID: 26975728
url
https://doi.org/10.1038/srep23030View
Published (Version of record) Open Access

Abstract

Regulons are the basic units of the response system in a bacterial cell, and each consists of a set of transcriptionally co-regulated operons. Regulon elucidation is the basis for studying the bacterial global transcriptional regulation network. In this study, we designed a novel co-regulation score between a pair of operons based on accurate operon identification and cis regulatory motif analyses, which can capture their co-regulation relationship much better than other scores. Taking full advantage of this discovery, we developed a new computational framework and built a novel graph model for regulon prediction. This model integrates the motif comparison and clustering and makes the regulon prediction problem substantially more solvable and accurate. To evaluate our prediction, a regulon coverage score was designed based on the documented regulons and their overlap with our prediction; and a modified Fisher Exact test was implemented to measure how well our predictions match the co-expressed modules derived from E. coli microarray gene-expression datasets collected under 466 conditions. The results indicate that our program consistently performed better than others in terms of the prediction accuracy. This suggests that our algorithms substantially improve the state-of-the-art, leading to a computational capability to reliably predict regulons for any bacteria.

Details

Metrics

34 Record Views
16 Times Cited - Web of Science
Logo image