Literature DB >> 20061798

Computational prediction of sRNAs and their targets in bacteria.

Rolf Backofen1, Wolfgang R Hess.   

Abstract

There is probably no major adaptive response in bacteria which does not have at least one small RNA (sRNA) as part of its regulatory network controlling gene expression. Thus, prokaryotic genomes encode dozens to hundreds of these riboregulators. Whereas the identification of putative sRNA genes during initial genome annotation is not yet common practice, their prediction can be done subsequently by various methods and with variable efficacy, frequently relying on comparative genome analysis. A large number of these sRNAs interact with their mRNA targets by antisense mechanisms. Yet, the computational identification of these targets appears to be challenging because frequently the partial and incomplete sequence complementarity is difficult to evaluate. Here we review the computational approaches for detecting bacterial sRNA genes and their targets, and discuss the current and future challenges that this exciting field of research is facing.

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Year:  2010        PMID: 20061798     DOI: 10.4161/rna.7.1.10655

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


  53 in total

1.  Computational prediction of efficient splice sites for trans-splicing ribozymes.

Authors:  Dario Meluzzi; Karen E Olson; Gregory F Dolan; Gaurav Arya; Ulrich F Müller
Journal:  RNA       Date:  2012-01-24       Impact factor: 4.942

2.  A domain-based model for predicting large and complex pseudoknotted structures.

Authors:  Song Cao; Shi-Jie Chen
Journal:  RNA Biol       Date:  2012-02-01       Impact factor: 4.652

3.  sRNATarBase: a comprehensive database of bacterial sRNA targets verified by experiments.

Authors:  Yuan Cao; Jiayao Wu; Qian Liu; Yalin Zhao; Xiaomin Ying; Lei Cha; Ligui Wang; Wuju Li
Journal:  RNA       Date:  2010-09-15       Impact factor: 4.942

4.  Cluster of genes that encode positive and negative elements influencing filament length in a heterocyst-forming cyanobacterium.

Authors:  Victoria Merino-Puerto; Antonia Herrero; Enrique Flores
Journal:  J Bacteriol       Date:  2013-09       Impact factor: 3.490

5.  Comparative genomics boosts target prediction for bacterial small RNAs.

Authors:  Patrick R Wright; Andreas S Richter; Kai Papenfort; Martin Mann; Jörg Vogel; Wolfgang R Hess; Rolf Backofen; Jens Georg
Journal:  Proc Natl Acad Sci U S A       Date:  2013-08-26       Impact factor: 11.205

Review 6.  cis-antisense RNA, another level of gene regulation in bacteria.

Authors:  Jens Georg; Wolfgang R Hess
Journal:  Microbiol Mol Biol Rev       Date:  2011-06       Impact factor: 11.056

7.  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

8.  Quantifying the sequence-function relation in gene silencing by bacterial small RNAs.

Authors:  Yue Hao; Zhongge J Zhang; David W Erickson; Min Huang; Yingwu Huang; Junbai Li; Terence Hwa; Hualin Shi
Journal:  Proc Natl Acad Sci U S A       Date:  2011-07-08       Impact factor: 11.205

9.  Functional characterization of bacterial sRNAs using a network biology approach.

Authors:  Sheetal R Modi; Diogo M Camacho; Michael A Kohanski; Graham C Walker; James J Collins
Journal:  Proc Natl Acad Sci U S A       Date:  2011-08-29       Impact factor: 11.205

10.  Large-scale mapping of sequence-function relations in small regulatory RNAs reveals plasticity and modularity.

Authors:  Neil Peterman; Anat Lavi-Itzkovitz; Erel Levine
Journal:  Nucleic Acids Res       Date:  2014-09-27       Impact factor: 16.971

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