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.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Xu Z,Almudevar A,Mathews DHdoi
10.1093/nar/gkr1081subject
Has Abstractpub_date
2012-02-01 00:00:00pages
e26issue
4eissn
0305-1048issn
1362-4962pii
gkr1081journal_volume
40pub_type
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