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Title: Validating regulatory predictions from diverse bacteria with mutant fitness data

Abstract

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomics predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.

Authors:
 [1]; ORCiD logo [2];  [2];  [2];  [3]
  1. Univ. of California, Berkeley, CA (United States). Dept. of Molecular and Cell Biology; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Genomics and Systems Biology
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Genomics and Systems Biology
  3. Johns Hopkins Univ., Baltimore, MD (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1393228
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 12; Journal Issue: 5; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES

Citation Formats

Sagawa, Shiori, Price, Morgan N., Deutschbauer, Adam M., Arkin, Adam P., and Bader, Joel S. Validating regulatory predictions from diverse bacteria with mutant fitness data. United States: N. p., 2017. Web. doi:10.1371/journal.pone.0178258.
Sagawa, Shiori, Price, Morgan N., Deutschbauer, Adam M., Arkin, Adam P., & Bader, Joel S. Validating regulatory predictions from diverse bacteria with mutant fitness data. United States. doi:10.1371/journal.pone.0178258.
Sagawa, Shiori, Price, Morgan N., Deutschbauer, Adam M., Arkin, Adam P., and Bader, Joel S. Wed . "Validating regulatory predictions from diverse bacteria with mutant fitness data". United States. doi:10.1371/journal.pone.0178258. https://www.osti.gov/servlets/purl/1393228.
@article{osti_1393228,
title = {Validating regulatory predictions from diverse bacteria with mutant fitness data},
author = {Sagawa, Shiori and Price, Morgan N. and Deutschbauer, Adam M. and Arkin, Adam P. and Bader, Joel S.},
abstractNote = {Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomics predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.},
doi = {10.1371/journal.pone.0178258},
journal = {PLoS ONE},
issn = {1932-6203},
number = 5,
volume = 12,
place = {United States},
year = {2017},
month = {5}
}

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