title: Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses.
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.
authors.affiliation: School of Mathematics, Shandong University, Jinan, Shandong, China.
authors.forename: Bingqiang
authors.initials: B
authors.lastname: Liu
authors.affiliation: School of Mathematics, Shandong University, Jinan, Shandong, China.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.affiliation: School of Mathematics, Shandong University, Jinan, Shandong, China.
authors.forename: Guojun
authors.initials: G
authors.lastname: Li
authors.affiliation: Systems Biology and Biomedical Informatics (SBBI) Laboratory University of Nebraska-Lincoln, Lincoln, NE 68588-0115, USA.
authors.forename: Hanyuan
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Biology, University of South Dakota, Vermillion, SD 57069, USA.|Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA.|BioSNTR, Brookings, SD, USA.
authors.forename: Erliang
authors.initials: E
authors.lastname: Zeng
authors.affiliation: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Department of Plant Science, South Dakota State University, Brookings, SD, 57006, USA.|BioSNTR, Brookings, SD, USA.
authors.forename: Qin
authors.initials: Q
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods; D004926:Escherichia coli / Q000235:genetics*; D029968:Escherichia coli Proteins / Q000235:genetics; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks; D016680:Genome, Bacterial / Q000235:genetics; D008957:Models, Genetic; D059372:Nucleotide Motifs / Q000235:genetics; D009876:Operon / Q000235:genetics*; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D018085:Regulon / Q000235:genetics*; D015203:Reproducibility of Results
chemical_list: D029968:Escherichia coli Proteins