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

Wang, Jiang; Liu, Tao; Zhao, Bo; Lu, Qixuan; Wang, Zheng; Cao, Yuan; Li, Wuju

doi:10.1093/nar/gkv1127

Cited by 39 publications

(41 citation statements)

References 34 publications

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“…The relevant hits were downloaded and prepared systematically for further manual data curation. Second, RAID v2.0 integrated diverse RNA-associated interactions from other 18 databases, including ChIPBase (10), LncRNA2Target (11), LncRNAdisease (7), miR2Disease (12), miRTarBase (13), MNDR (14), ncRDeathDB (8), NPInter (15), OncomiRDB (16), sRNATarBase (17), StarBase (6), TransmiR (18) and ViRBase (19) as well as five computational prediction databases (DroID (20), EIMMo (21), miRanda (22), miRDB (23) and TargetScan (9)).…”

Section: Data Collectionmentioning

confidence: 99%

RAID v2.0: an updated resource of RNA-associated interactions across organisms

et al. 2016

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With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA–RNA interactions and more than 1.2 million RNA–protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.

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Section: Data Collectionmentioning

confidence: 99%

RAID v2.0: an updated resource of RNA-associated interactions across organisms

et al. 2016

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“…The present estimation of sRNAs known to be expressed by either Escherichia coli and/or Salmonella enterica is around 300 [25]. Among them, a large set of sRNAs are already known as regulators and are trans-acting with mRNAs by base-pairing with 5’ UTR mRNA regions (see sRNATarBase 3 [26] and EcoCyc [27]). As previously mentioned, sRNA regulation are inter-twisted with transcription factor regulation.…”

Section: Case Applicationsmentioning

confidence: 99%

“…At the start of this study, the experimental validated targets of these two sRNAs were compiled using the sRNATarBase 3 [26], EcoCyc [27] and [28, 29] (see Table 1) and depicted using rNAV in the Fig. 1.…”

Section: Case Applicationsmentioning

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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“…The functional annotation of the sRNAs was carried out using target based reverse annotation approaches following a modified protocol as described by Barh et al, 2013 [40,41]. In brief, we identified the validated targets of sRNA using sRNATarBase [42] database and RNApredator [43] was used to predict putative targets in M. tuberculosis, H37Rv. Top 100 targets were used for functional annotation of using DAVID functional annotation tool [44].…”

Section: Functional Annotation Of M Tuberculosis Srnasmentioning

confidence: 99%

A potential link between tuberculosis and lung cancer through non-coding RNAs

Barh¹,

Tiwari²,

et al. 2017

Preprint

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; Tel/Fax: +91 944 955 0032 not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/188375 doi: bioRxiv preprint first posted online Sep. 13, 2017; 2 | P a g e ABSTRACT Pulmonary tuberculosis caused by Mycobacterium and lung cancer are two major causes of deaths worldwide and the former increases the risk of developing lung cancer. However, the precise molecular mechanism of Mycobacterium associated increased risk of lung cancer is not entirely understood. Here, using in silico approaches, we show that hsa-mir-21 and M. tuberculosis sRNA_1096 and sRNA_1414 could play important roles in the pathogenesis of both these diseases. Further, we postulated a "Genetic remittance" hypothesis where these sRNAs may play important roles. The sRNA_1096 could be involved in tuberculosis through multiple infectious processes, and if transferred to the host, it may activate the TLR8 mediated pro-metastatic inflammatory pathway by acting as a ligand to TLR8 similar to the mir-21 leading to lung tumorigenesis and chemo-resistance. Analogous to SH3GL1, it may also regulate cell cycle. On the other hand, sRNA_1414 is probably involved in survivability and drug response of the pathogen. However, it may be a metastatic factor for lung cancer providing EPS8L1 and SORBS1 like functions upon remittance. Further, all these three noncoding RNAs are predicted to act in rifampicin resistance in Mycobacterium. Currently, we are applying robust bioinformatics strategies and conducting experimental validations to confirm our in-silico findings and hypothesis.

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

Cited by 39 publications

References 34 publications

RAID v2.0: an updated resource of RNA-associated interactions across organisms

RAID v2.0: an updated resource of RNA-associated interactions across organisms

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

A potential link between tuberculosis and lung cancer through non-coding RNAs

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