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Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792141/ https://www.ncbi.nlm.nih.gov/pubmed/26975728 http://dx.doi.org/10.1038/srep23030 |
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author | Liu, Bingqiang Zhou, Chuan Li, Guojun Zhang, Hanyuan Zeng, Erliang Liu, Qi Ma, Qin |
author_facet | Liu, Bingqiang Zhou, Chuan Li, Guojun Zhang, Hanyuan Zeng, Erliang Liu, Qi Ma, Qin |
author_sort | Liu, Bingqiang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4792141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47921412016-03-16 Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses Liu, Bingqiang Zhou, Chuan Li, Guojun Zhang, Hanyuan Zeng, Erliang Liu, Qi Ma, Qin Sci Rep Article 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. Nature Publishing Group 2016-03-15 /pmc/articles/PMC4792141/ /pubmed/26975728 http://dx.doi.org/10.1038/srep23030 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Liu, Bingqiang Zhou, Chuan Li, Guojun Zhang, Hanyuan Zeng, Erliang Liu, Qi Ma, Qin Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses |
title | Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses |
title_full | Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses |
title_fullStr | Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses |
title_full_unstemmed | Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses |
title_short | Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses |
title_sort | bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792141/ https://www.ncbi.nlm.nih.gov/pubmed/26975728 http://dx.doi.org/10.1038/srep23030 |
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