Validating regulatory predictions from diverse bacteria with mutant fitness data.
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 expe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5443562?pdf=render |
| _version_ | 1818683274116988928 |
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| author | Shiori Sagawa Morgan N Price Adam M Deutschbauer Adam P Arkin |
| author_facet | Shiori Sagawa Morgan N Price Adam M Deutschbauer Adam P Arkin |
| author_sort | Shiori Sagawa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T10:32:08Z |
| format | Article |
| id | doaj.art-1a87ee12d3024f3e9f1c5f57f6fea057 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T10:32:08Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-1a87ee12d3024f3e9f1c5f57f6fea0572022-12-21T21:52:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01125e017825810.1371/journal.pone.0178258Validating regulatory predictions from diverse bacteria with mutant fitness data.Shiori SagawaMorgan N PriceAdam M DeutschbauerAdam P ArkinAlthough 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.http://europepmc.org/articles/PMC5443562?pdf=render |
| spellingShingle | Shiori Sagawa Morgan N Price Adam M Deutschbauer Adam P Arkin Validating regulatory predictions from diverse bacteria with mutant fitness data. PLoS ONE |
| title | Validating regulatory predictions from diverse bacteria with mutant fitness data. |
| title_full | Validating regulatory predictions from diverse bacteria with mutant fitness data. |
| title_fullStr | Validating regulatory predictions from diverse bacteria with mutant fitness data. |
| title_full_unstemmed | Validating regulatory predictions from diverse bacteria with mutant fitness data. |
| title_short | Validating regulatory predictions from diverse bacteria with mutant fitness data. |
| title_sort | validating regulatory predictions from diverse bacteria with mutant fitness data |
| url | http://europepmc.org/articles/PMC5443562?pdf=render |
| work_keys_str_mv | AT shiorisagawa validatingregulatorypredictionsfromdiversebacteriawithmutantfitnessdata AT morgannprice validatingregulatorypredictionsfromdiversebacteriawithmutantfitnessdata AT adammdeutschbauer validatingregulatorypredictionsfromdiversebacteriawithmutantfitnessdata AT adamparkin validatingregulatorypredictionsfromdiversebacteriawithmutantfitnessdata |