sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.

<h4>Background</h4>Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000...

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Autores principales:	Xiaomin Ying, Yuan Cao, Jiayao Wu, Qian Liu, Lei Cha, Wuju Li
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/30308ba5b97347919ce2191897373967
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spelling	oai:doaj.org-article:30308ba5b97347919ce21918973739672021-11-18T06:49:33ZsTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.1932-620310.1371/journal.pone.0022705https://doaj.org/article/30308ba5b97347919ce21918973739672011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21799937/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.<h4>Results</h4>Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.<h4>Conclusions</h4>sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/.Xiaomin YingYuan CaoJiayao WuQian LiuLei ChaWuju LiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 7, p e22705 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaomin Ying Yuan Cao Jiayao Wu Qian Liu Lei Cha Wuju Li sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.
description	<h4>Background</h4>Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.<h4>Results</h4>Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.<h4>Conclusions</h4>sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/.
format	article
author	Xiaomin Ying Yuan Cao Jiayao Wu Qian Liu Lei Cha Wuju Li
author_facet	Xiaomin Ying Yuan Cao Jiayao Wu Qian Liu Lei Cha Wuju Li
author_sort	Xiaomin Ying
title	sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.
title_short	sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.
title_full	sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.
title_fullStr	sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.
title_full_unstemmed	sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.
title_sort	starpicker: a method for efficient prediction of bacterial srna targets based on a two-step model for hybridization.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/30308ba5b97347919ce2191897373967
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_version_	1718424342699704320

sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization.

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