An assessment of bacterial small RNA target prediction programs

An assessment of bacterial small RNA target prediction programs

Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. H...

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Main Authors: Pain, Adrien, Ott, Alban, Amine, Hamza, Rochat, Tatiana, Bouloc, Philippe, Gautheret, Daniel
Format: Online
Language:English
Published: Taylor & Francis 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4615726/
id pubmed-4615726
recordtype oai_dc
spelling pubmed-46157262016-02-03 An assessment of bacterial small RNA target prediction programs Pain, Adrien Ott, Alban Amine, Hamza Rochat, Tatiana Bouloc, Philippe Gautheret, Daniel Review Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. Here, we provide a simple benchmark for bacterial sRNA target prediction based on trusted E. coli sRNA/target pairs. We use this benchmark to assess the most recent RNA target predictors as well as earlier programs for RNA-RNA hybrid prediction. Moreover, we consider how the definition of mRNA boundaries can impact overall predictions. Recent algorithms that exploit both conservation of targets and accessibility information offer improved accuracy over previous software. However, even with the best predictors, the number of true biological targets with low scores and non-targets with high scores remains puzzling. Taylor & Francis 2015-03-11 /pmc/articles/PMC4615726/ /pubmed/25760244 http://dx.doi.org/10.1080/15476286.2015.1020269 Text en © 2015 The Author(s). Published with license by Taylor & Francis Group, LLC http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The moral rights of the named author(s) have been asserted.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Pain, Adrien
Ott, Alban
Amine, Hamza
Rochat, Tatiana
Bouloc, Philippe
Gautheret, Daniel
spellingShingle Pain, Adrien
Ott, Alban
Amine, Hamza
Rochat, Tatiana
Bouloc, Philippe
Gautheret, Daniel
An assessment of bacterial small RNA target prediction programs
author_facet Pain, Adrien
Ott, Alban
Amine, Hamza
Rochat, Tatiana
Bouloc, Philippe
Gautheret, Daniel
author_sort Pain, Adrien
title An assessment of bacterial small RNA target prediction programs
title_short An assessment of bacterial small RNA target prediction programs
title_full An assessment of bacterial small RNA target prediction programs
title_fullStr An assessment of bacterial small RNA target prediction programs
title_full_unstemmed An assessment of bacterial small RNA target prediction programs
title_sort assessment of bacterial small rna target prediction programs
description Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. Here, we provide a simple benchmark for bacterial sRNA target prediction based on trusted E. coli sRNA/target pairs. We use this benchmark to assess the most recent RNA target predictors as well as earlier programs for RNA-RNA hybrid prediction. Moreover, we consider how the definition of mRNA boundaries can impact overall predictions. Recent algorithms that exploit both conservation of targets and accessibility information offer improved accuracy over previous software. However, even with the best predictors, the number of true biological targets with low scores and non-targets with high scores remains puzzling.
publisher Taylor & Francis
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4615726/
_version_ 1613491159260200960

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