Cargando…
rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining
BACKGROUND: Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional a...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5364647/ https://www.ncbi.nlm.nih.gov/pubmed/28335718 http://dx.doi.org/10.1186/s12859-017-1598-8 |
_version_ | 1782517365382053888 |
---|---|
author | Bourqui, Romain Dutour, Isabelle Dubois, Jonathan Benchimol, William Thébault, Patricia |
author_facet | Bourqui, Romain Dutour, Isabelle Dubois, Jonathan Benchimol, William Thébault, Patricia |
author_sort | Bourqui, Romain |
collection | PubMed |
description | BACKGROUND: Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses. To deal with this issue, sRNA target analyses can be carried out from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by domain experts who need to confront various biological features to prioritize the target candidates. Therefore, novel strategies have to be carried out to improve the specificity of computational prediction results, before proposing new candidates for an expensive experimental validation stage. RESULT: To address this issue, we propose a new visualization tool rNAV 2.0, for detecting and filtering bacterial sRNA targets for regulatory networks. rNAV is designed to cope with a variety of biological constraints, including the gene annotations, the conserved regions of interaction or specific patterns of regulation. Depending on the application, these constraints can be variously combined to analyze the target candidates, prioritized for instance by a known conserved interaction region, or because of a common function. CONCLUSION: The standalone application implements a set of known algorithms and interaction techniques, and applies them to the new problem of identifying reasonable sRNA target candidates. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1598-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5364647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53646472017-03-24 rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining Bourqui, Romain Dutour, Isabelle Dubois, Jonathan Benchimol, William Thébault, Patricia BMC Bioinformatics Software BACKGROUND: Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses. To deal with this issue, sRNA target analyses can be carried out from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by domain experts who need to confront various biological features to prioritize the target candidates. Therefore, novel strategies have to be carried out to improve the specificity of computational prediction results, before proposing new candidates for an expensive experimental validation stage. RESULT: To address this issue, we propose a new visualization tool rNAV 2.0, for detecting and filtering bacterial sRNA targets for regulatory networks. rNAV is designed to cope with a variety of biological constraints, including the gene annotations, the conserved regions of interaction or specific patterns of regulation. Depending on the application, these constraints can be variously combined to analyze the target candidates, prioritized for instance by a known conserved interaction region, or because of a common function. CONCLUSION: The standalone application implements a set of known algorithms and interaction techniques, and applies them to the new problem of identifying reasonable sRNA target candidates. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1598-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-23 /pmc/articles/PMC5364647/ /pubmed/28335718 http://dx.doi.org/10.1186/s12859-017-1598-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Bourqui, Romain Dutour, Isabelle Dubois, Jonathan Benchimol, William Thébault, Patricia rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining |
title | rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining |
title_full | rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining |
title_fullStr | rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining |
title_full_unstemmed | rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining |
title_short | rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining |
title_sort | rnav 2.0: a visualization tool for bacterial srna-mediated regulatory networks mining |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5364647/ https://www.ncbi.nlm.nih.gov/pubmed/28335718 http://dx.doi.org/10.1186/s12859-017-1598-8 |
work_keys_str_mv | AT bourquiromain rnav20avisualizationtoolforbacterialsrnamediatedregulatorynetworksmining AT dutourisabelle rnav20avisualizationtoolforbacterialsrnamediatedregulatorynetworksmining AT duboisjonathan rnav20avisualizationtoolforbacterialsrnamediatedregulatorynetworksmining AT benchimolwilliam rnav20avisualizationtoolforbacterialsrnamediatedregulatorynetworksmining AT thebaultpatricia rnav20avisualizationtoolforbacterialsrnamediatedregulatorynetworksmining |