Literature DB >> 33103602

PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region.

Krishna Kumar1, Abhijit Chakraborty2, Saikat Chakrabarti1.   

Abstract

Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.

Entities:  

Keywords:  RNA structure; RNA-RNA interaction; RNA-protein interaction; sRNA identification; sRNA target prediction

Mesh:

Substances:

Year:  2020        PMID: 33103602      PMCID: PMC8244778          DOI: 10.1080/15476286.2020.1836455

Source DB:  PubMed          Journal:  RNA Biol        ISSN: 1547-6286            Impact factor:   4.652


  55 in total

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Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

2.  Thermodynamics of RNA-RNA binding.

Authors:  Ulrike Mückstein; Hakim Tafer; Jörg Hackermüller; Stephan H Bernhart; Peter F Stadler; Ivo L Hofacker
Journal:  Bioinformatics       Date:  2006-01-29       Impact factor: 6.937

Review 3.  Experimental approaches for the discovery and characterization of regulatory small RNA.

Authors:  Cynthia Mira Sharma; Jörg Vogel
Journal:  Curr Opin Microbiol       Date:  2009-09-14       Impact factor: 7.934

4.  Assessing computational tools for the discovery of small RNA genes in bacteria.

Authors:  Xiaojun Lu; Heidi Goodrich-Blair; Brian Tjaden
Journal:  RNA       Date:  2011-07-18       Impact factor: 4.942

5.  Genetic screens to identify bacterial sRNA regulators.

Authors:  Pierre Mandin
Journal:  Methods Mol Biol       Date:  2012

6.  Computational identification of noncoding RNAs in E. coli by comparative genomics.

Authors:  E Rivas; R J Klein; T A Jones; S R Eddy
Journal:  Curr Biol       Date:  2001-09-04       Impact factor: 10.834

Review 7.  Regulatory RNAs in bacteria.

Authors:  Lauren S Waters; Gisela Storz
Journal:  Cell       Date:  2009-02-20       Impact factor: 41.582

8.  Structure of Escherichia coli Hfq bound to polyriboadenylate RNA.

Authors:  Todd M Link; Poul Valentin-Hansen; Richard G Brennan
Journal:  Proc Natl Acad Sci U S A       Date:  2009-11-04       Impact factor: 11.205

9.  RNApredator: fast accessibility-based prediction of sRNA targets.

Authors:  Florian Eggenhofer; Hakim Tafer; Peter F Stadler; Ivo L Hofacker
Journal:  Nucleic Acids Res       Date:  2011-06-14       Impact factor: 16.971

10.  Hfq-bridged ternary complex is important for translation activation of rpoS by DsrA.

Authors:  Weiwei Wang; Lijun Wang; Jihui Wu; Qingguo Gong; Yunyu Shi
Journal:  Nucleic Acids Res       Date:  2013-04-19       Impact factor: 16.971

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