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dc.contributor.authorAbbas, Ahmed
dc.contributor.authorKong, Xin-Bing
dc.contributor.authorLiu, Zhi
dc.contributor.authorJing, Bing-Yi
dc.contributor.authorGao, Xin
dc.date.accessioned2014-08-27T09:46:17Z
dc.date.available2014-08-27T09:46:17Z
dc.date.issued2013-01-07
dc.identifier.citationAbbas A, Kong X-B, Liu Z, Jing B-Y, Gao X (2013) Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm. PLoS ONE 8: e53112. doi:10.1371/journal.pone.0053112.
dc.identifier.issn19326203
dc.identifier.pmid23308147
dc.identifier.doi10.1371/journal.pone.0053112
dc.identifier.urihttp://hdl.handle.net/10754/325309
dc.description.abstractA common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into p-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013 Abbas et al.
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rightsArchived with thanks to PLoS ONE
dc.subjectprotein
dc.subjectalgorithm
dc.subjectautomatic peak selection
dc.subjectBenjamini Hochberg based algorithm
dc.subjectbioinformatics
dc.subjectmathematical analysis
dc.subjectmathematical computing
dc.subjectnuclear magnetic resonance
dc.subjectprediction
dc.subjectprotein structure
dc.subjectscoring system
dc.subjectAlgorithms
dc.subjectComputational Biology
dc.subjectNuclear Magnetic Resonance, Biomolecular
dc.subjectProteins
dc.subjectSoftware
dc.titleAutomatic Peak Selection by a Benjamini-Hochberg-Based Algorithm
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalPLoS ONE
dc.identifier.pmcidPMC3538655
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Statistics, Fudan University, Shanghai, China
dc.contributor.institutionDepartment of Mathematics, Faculty of Science and Technology, University of Macau, Taipa, Macau
dc.contributor.institutionDepartment of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kong
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personAbbas, Ahmed
kaust.personGao, Xin
refterms.dateFOA2018-06-13T14:53:49Z


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