Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks

Thébault, Patricia; Bourqui, Romain; Benchimol, William; Gaspin, Christine; Sirand‐Pugnet, Pascal; Dutour, Isabelle

doi:10.1093/bib/bbu045

Cited by 10 publications

(7 citation statements)

References 79 publications

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“…For example, this database has been used to develop sRNA target prediction models, including sRNATarget ( 19 ), sTarPicker ( 20 ) and bistaRNA ( 22 ). Thébault employed part of the data set as a benchmark to evaluate prediction models, RNAup, IntaRNA and ssearch ( 23 ). Li incorporated the 138 sRNA-target interactions from sRNATarBase2.0 into another database BSRD ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

sRNATarBase 3.0: an updated database for sRNA-target interactions in bacteria

Wang¹,

Liu²,

Zhao³

et al. 2015

Nucleic Acids Res

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Bacterial sRNAs are a class of small regulatory RNAs of about 40–500 nt in length; they play multiple biological roles through binding to their target mRNAs or proteins. Therefore, elucidating sRNA targets is very important. However, only targets of a few sRNAs have been described. To facilitate sRNA functional studies such as developing sRNA target prediction models, we updated the sRNATarBase database, which was initially developed in 2010. The new version (recently moved to http://ccb1.bmi.ac.cn/srnatarbase/) contains 771 sRNA-target entries manually collected from 213 papers, and 23 290 and 11 750 predicted targets from sRNATarget and sTarPicker, respectively. Among the 771 entries, 475 and 17 were involved in validated sRNA–mRNA and sRNA–protein interactions, respectively, while 279 had no reported interactions. We also presented detailed information for 316 binding regions of sRNA-target mRNA interactions and related mutation experiments, as well as new features, including NCBI sequence viewer, sRNA regulatory network, target prediction-based GO and pathway annotations, and error report system. The new version provides a comprehensive annotation of validated sRNA-target interactions, and will be a useful resource for bacterial sRNA studies.

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Section: Introductionmentioning

confidence: 99%

sRNATarBase 3.0: an updated database for sRNA-target interactions in bacteria

Wang¹,

Liu²,

Zhao³

et al. 2015

Nucleic Acids Res

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“…Information Visualization has now been established as a fruitful strategy to tackle the problem posed by the abundance of information (for an overview on biological data visualization, the reader can refer to [16, 17]).…”

Section: Methodsmentioning

confidence: 99%

“…Combining bioinformatics and visualization help biologists in the analysis of such sRNA-mediated regulatory networks by supporting the exploration and visualization of the huge number of predicted sRNA targets produced by existing bioinformatics tools [16]. …”

Section: Introductionmentioning

confidence: 99%

rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining

Bourqui¹,

Dutour²,

Dubois³

et al. 2017

BMC Bioinformatics

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BackgroundBacterial 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.ResultTo 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.ConclusionThe 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 materialThe online version of this article (doi:10.1186/s12859-017-1598-8) contains supplementary material, which is available to authorized users.

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“…To support exploration awareness, rNAV also provides an exploration tree view that allows users to navigate through the steps of the analysis, select the sub-networks to visualize and compare results. [25] and analyze sRNA-mediated regulatory bacterial networks [50]. Fig.…”

Section: Porgy: a Tulip Perspectivementioning

confidence: 99%