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dc.contributor.authorWu, Y
dc.contributor.authorShi, B
dc.contributor.authorDing, X
dc.contributor.authorLiu, T
dc.contributor.authorHu, X
dc.contributor.authorYip, KY
dc.contributor.authorYang, ZR
dc.contributor.authorMathews, DH
dc.contributor.authorLu, ZJ
dc.date.accessioned2016-03-14T08:27:33Z
dc.date.issued2015-07-13
dc.description.abstractRecently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.en_GB
dc.description.sponsorshipNational Key Basic Research Program of China [2012CB316503]; National High-Tech Research and Development Program of China [2014AA021103]; National Natural Science Foundation of China [31271402]; Tsinghua University Initiative Scientific Research Program [2014z21045]; Hong Kong Research Grants Council Early Career Scheme [419612 to K.Y.]; National Science Foundation [1339282 to D.H.M.]; Computing Platform of the National Protein Facilities (Tsinghua University). Funding for open access charge: National Natural Science Foundation of China [31271402].en_GB
dc.identifier.citationVol. 43, Iss. 15, pp. 7247 - 7259en_GB
dc.identifier.doi10.1093/nar/gkv706
dc.identifier.othergkv706
dc.identifier.urihttp://hdl.handle.net/10871/20685
dc.language.isoenen_GB
dc.publisherOxford University Press (OUP): Policy C - Option Ben_GB
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/26170232en_GB
dc.relation.urlhttp://nar.oxfordjournals.org/content/43/15/7247en_GB
dc.rightsThis is the final version of the article. Available from Oxford University Press via the DOI in this record.en_GB
dc.subjectAlgorithmsen_GB
dc.subjectModels, Chemicalen_GB
dc.subjectModels, Statisticalen_GB
dc.subjectMolecular Probe Techniquesen_GB
dc.subjectNucleic Acid Conformationen_GB
dc.subjectRNAen_GB
dc.subjectSoftwareen_GB
dc.subjectThermodynamicsen_GB
dc.titleImproved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data.en_GB
dc.typeArticleen_GB
dc.date.available2016-03-14T08:27:33Z
dc.identifier.issn0305-1048
exeter.place-of-publicationEngland
dc.descriptionPublisheden_GB
dc.descriptionEvaluation Studiesen_GB
dc.descriptionJournal Articleen_GB
dc.descriptionResearch Support, Non-U.S. Gov'ten_GB
dc.identifier.eissn1362-4962
dc.identifier.journalNucleic Acids Researchen_GB


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