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Computational prediction of regulatory, premature transcription termination in bacteria

A common strategy for regulation of gene expression in bacteria is conditional transcription termination. This strategy is frequently employed by 5′UTR cis-acting RNA elements (riboregulators), including riboswitches and attenuators. Such riboregulators can assume two mutually exclusive RNA structur...

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Detalles Bibliográficos
Autores principales: Millman, Adi, Dar, Daniel, Shamir, Maya, Sorek, Rotem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
RNA
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5314783/
https://www.ncbi.nlm.nih.gov/pubmed/27574119
http://dx.doi.org/10.1093/nar/gkw749
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author Millman, Adi
Dar, Daniel
Shamir, Maya
Sorek, Rotem
author_facet Millman, Adi
Dar, Daniel
Shamir, Maya
Sorek, Rotem
author_sort Millman, Adi
collection PubMed
description A common strategy for regulation of gene expression in bacteria is conditional transcription termination. This strategy is frequently employed by 5′UTR cis-acting RNA elements (riboregulators), including riboswitches and attenuators. Such riboregulators can assume two mutually exclusive RNA structures, one of which forms a transcriptional terminator and results in premature termination, and the other forms an antiterminator that allows read-through into the coding sequence to produce a full-length mRNA. We developed a machine-learning based approach, which, given a 5′UTR of a gene, predicts whether it can form the two alternative structures typical to riboregulators employing conditional termination. Using a large positive training set of riboregulators derived from 89 human microbiome bacteria, we show high specificity and sensitivity for our classifier. We further show that our approach allows the discovery of previously unidentified riboregulators, as exemplified by the detection of new LeuA leaders and T-boxes in Streptococci. Finally, we developed PASIFIC (www.weizmann.ac.il/molgen/Sorek/PASIFIC/), an online web-server that, given a user-provided 5′UTR sequence, predicts whether this sequence can adopt two alternative structures conforming with the conditional termination paradigm. This webserver is expected to assist in the identification of new riboswitches and attenuators in the bacterial pan-genome.
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spelling pubmed-53147832017-02-21 Computational prediction of regulatory, premature transcription termination in bacteria Millman, Adi Dar, Daniel Shamir, Maya Sorek, Rotem Nucleic Acids Res RNA A common strategy for regulation of gene expression in bacteria is conditional transcription termination. This strategy is frequently employed by 5′UTR cis-acting RNA elements (riboregulators), including riboswitches and attenuators. Such riboregulators can assume two mutually exclusive RNA structures, one of which forms a transcriptional terminator and results in premature termination, and the other forms an antiterminator that allows read-through into the coding sequence to produce a full-length mRNA. We developed a machine-learning based approach, which, given a 5′UTR of a gene, predicts whether it can form the two alternative structures typical to riboregulators employing conditional termination. Using a large positive training set of riboregulators derived from 89 human microbiome bacteria, we show high specificity and sensitivity for our classifier. We further show that our approach allows the discovery of previously unidentified riboregulators, as exemplified by the detection of new LeuA leaders and T-boxes in Streptococci. Finally, we developed PASIFIC (www.weizmann.ac.il/molgen/Sorek/PASIFIC/), an online web-server that, given a user-provided 5′UTR sequence, predicts whether this sequence can adopt two alternative structures conforming with the conditional termination paradigm. This webserver is expected to assist in the identification of new riboswitches and attenuators in the bacterial pan-genome. Oxford University Press 2017-01-25 2016-08-29 /pmc/articles/PMC5314783/ /pubmed/27574119 http://dx.doi.org/10.1093/nar/gkw749 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle RNA
Millman, Adi
Dar, Daniel
Shamir, Maya
Sorek, Rotem
Computational prediction of regulatory, premature transcription termination in bacteria
title Computational prediction of regulatory, premature transcription termination in bacteria
title_full Computational prediction of regulatory, premature transcription termination in bacteria
title_fullStr Computational prediction of regulatory, premature transcription termination in bacteria
title_full_unstemmed Computational prediction of regulatory, premature transcription termination in bacteria
title_short Computational prediction of regulatory, premature transcription termination in bacteria
title_sort computational prediction of regulatory, premature transcription termination in bacteria
topic RNA
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5314783/
https://www.ncbi.nlm.nih.gov/pubmed/27574119
http://dx.doi.org/10.1093/nar/gkw749
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