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Nucleic Acids Res. 2015 Feb 18;43(3):1859-68. doi: 10.1093/nar/gkv010. Epub 2015 Jan 23.

Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark.

Author information

1
Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 37599, USA Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
2
Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 37599, USA.
3
Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
4
Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
5
Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 37599, USA Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA alain@unc.edu.

Abstract

Ribonucleic acid (RNA) secondary structure prediction continues to be a significant challenge, in particular when attempting to model sequences with less rigidly defined structures, such as messenger and non-coding RNAs. Crucial to interpreting RNA structures as they pertain to individual phenotypes is the ability to detect RNAs with large structural disparities caused by a single nucleotide variant (SNV) or riboSNitches. A recently published human genome-wide parallel analysis of RNA structure (PARS) study identified a large number of riboSNitches as well as non-riboSNitches, providing an unprecedented set of RNA sequences against which to benchmark structure prediction algorithms. Here we evaluate 11 different RNA folding algorithms' riboSNitch prediction performance on these data. We find that recent algorithms designed specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and SNPfold, perform best on the most rigorously validated subsets of the benchmark data. In addition, our benchmark indicates that general structure prediction algorithms (e.g. RNAfold and RNAstructure) have overall better performance if base pairing probabilities are considered rather than minimum free energy calculations. Although overall aggregate algorithmic performance on the full set of riboSNitches is relatively low, significant improvement is possible if the highest confidence predictions are evaluated independently.

PMID:
25618847
PMCID:
PMC4330374
DOI:
10.1093/nar/gkv010
[Indexed for MEDLINE]
Free PMC Article

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