An assessment of bacterial small RNA target prediction programs

RNA Biology
Adrien PainDaniel Gautheret

Abstract

Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. Here, we provide a simple benchmark for bacterial sRNA target prediction based on trusted E. coli sRNA/target pairs. We use this benchmark to assess the most recent RNA target predictors as well as earlier programs for RNA-RNA hybrid prediction. Moreover, we consider how the definition of mRNA boundaries can impact overall predictions. Recent algorithms that exploit both conservation of targets and accessibility information offer improved accuracy over previous software. However, even with the best predictors, the number of true biological targets with low scores and non-targets with high scores remains puzzling.

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Citations

Dec 19, 2015·BMC Genomics·Casey L C SchroederSanjeev K Sahni
Dec 18, 2015·Nucleic Acids Research·Daniel Lai, Irmtraud M Meyer
Oct 17, 2015·Annual Review of Genetics·Lars Barquist, Jörg Vogel
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Jul 10, 2017·Applied Microbiology and Biotechnology·Jiakun QiJianjun Qiao
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Sep 24, 2021·The Journal of Microbiology·Niralee Patel, Mrinalini Nair

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

RNAplex
intaRNA
Pairfold
TargetRNA2
RNAcofold
RNAplfold
Unix
TargetRNA
CopraRNA
RNApredator

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