ArticleScientific reports2016
Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- Bacterial Transcriptional Regulators: A Road Map for Functional, Structural, and Biophysical Characterization.International journal of molecular sciences · 2022Review
- Flexible comparative genomics of prokaryotic transcriptional regulatory networks.BMC genomics · 2020Article
- Genome-scale exploration of transcriptional regulation in the nisin Z producer Lactococcus lactis subsp. lactis IO-1.Scientific reports · 2020Article
- 2CS-CHXApplied and environmental microbiology · 2019Article
- Limits to a classic paradigm: most transcription factors in E. coli regulate genes involved in multiple biological processes.Nucleic acids research · 2019Article
- It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.Briefings in bioinformatics · 2019Review
- RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis.Genes · 2018Article
- Gene Enrichment Analysis Reveals Major Regulators ofFrontiers in microbiology · 2018Article
- An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes.BMC genomics · 2016Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.