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Biophys J. 2017 Jul 25;113(2):330-338. doi: 10.1016/j.bpj.2017.06.039. Epub 2017 Jul 20.

Modeling RNA Secondary Structure with Sequence Comparison and Experimental Mapping Data.

Author information

1
Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, New York; Center for RNA Biology, University of Rochester Medical Center, Rochester, New York.
2
Center for RNA Biology, University of Rochester Medical Center, Rochester, New York; Department of Electrical and Computer Engineering, University of Rochester Medical Center, Rochester, New York; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York. Electronic address: gaurav.sharma@rochester.edu.
3
Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, New York; Center for RNA Biology, University of Rochester Medical Center, Rochester, New York; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York. Electronic address: david_mathews@urmc.rochester.edu.

Abstract

Secondary structure prediction is an important problem in RNA bioinformatics because knowledge of structure is critical to understanding the functions of RNA sequences. Significant improvements in prediction accuracy have recently been demonstrated though the incorporation of experimentally obtained structural information, for instance using selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) mapping. However, such mapping data is currently available only for a limited number of RNA sequences. In this article, we present a method for extending the benefit of experimental mapping data in secondary structure prediction to homologous sequences. Specifically, we propose a method for integrating experimental mapping data into a comparative sequence analysis algorithm for secondary structure prediction of multiple homologs, whereby the mapping data benefits not only the prediction for the specific sequence that was mapped but also other homologs. The proposed method is realized by modifying the TurboFold II algorithm for prediction of RNA secondary structures to utilize basepairing probabilities guided by SHAPE experimental data when such data are available. The SHAPE-mapping-guided basepairing probabilities are obtained using the RSample method. Results demonstrate that the SHAPE mapping data for a sequence improves structure prediction accuracy of other homologous sequences beyond the accuracy obtained by sequence comparison alone (TurboFold II). The updated version of TurboFold II is freely available as part of the RNAstructure software package.

PMID:
28735622
PMCID:
PMC5529333
DOI:
10.1016/j.bpj.2017.06.039
[Indexed for MEDLINE]
Free PMC Article

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