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Nucleic Acids Res. 2012 Feb;40(4):e26. doi: 10.1093/nar/gkr1081. Epub 2011 Dec 1.

Statistical evaluation of improvement in RNA secondary structure prediction.

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1
Department of Biochemistry and Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, NY, USA.

Abstract

With discovery of diverse roles for RNA, its centrality in cellular functions has become increasingly apparent. A number of algorithms have been developed to predict RNA secondary structure. Their performance has been benchmarked by comparing structure predictions to reference secondary structures. Generally, algorithms are compared against each other and one is selected as best without statistical testing to determine whether the improvement is significant. In this work, it is demonstrated that the prediction accuracies of methods correlate with each other over sets of sequences. One possible reason for this correlation is that many algorithms use the same underlying principles. A set of benchmarks published previously for programs that predict a structure common to three or more sequences is statistically analyzed as an example to show that it can be rigorously evaluated using paired two-sample t-tests. Finally, a pipeline of statistical analyses is proposed to guide the choice of data set size and performance assessment for benchmarks of structure prediction. The pipeline is applied using 5S rRNA sequences as an example.

PMID:
22139940
PMCID:
PMC3287165
DOI:
10.1093/nar/gkr1081
[Indexed for MEDLINE]
Free PMC Article
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