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Statistical Evaluation of Improvement in RNA Secondary Structure Prediction

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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.

There has been an explosion in our understanding of roles for RNA in cellular processes and gene expression in recent decades. RNA can form complex three dimensional structure either alone or with proteins to catalyze RNA splicing , catalyze peptide bond formation , guide protein localization, and tune gene regulation.

Prediction of RNA secondary structure, the set of base pairing interactions between A-U, G-U and G-C, facilitates the development of hypotheses that connect structure to function. It also underlies a number of applications such as non-coding RNA detection, RNA tertiary structure prediction, siRNA design, miRNA target prediction and structure design. There are many algorithms that have been developed to apply to specific situations with their own strengths and limitations.

The article is published online in Nucleic Acids Research and is free to access.