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dc.contributor.authorLiu, Zhi
dc.contributor.authorAbbas, Ahmed
dc.contributor.authorJing, Bing-Yi
dc.contributor.authorGao, Xin
dc.date.accessioned2014-08-27T09:51:08Z
dc.date.available2014-08-27T09:51:08Z
dc.date.issued2012-02-10
dc.identifier.citationLiu Z, Abbas A, Jing B-Y, Gao X (2012) WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering. Bioinformatics 28: 914-920. doi:10.1093/bioinformatics/bts078.
dc.identifier.issn13674803
dc.identifier.pmid22328784
dc.identifier.doi10.1093/bioinformatics/bts078
dc.identifier.urihttp://hdl.handle.net/10754/325433
dc.description.abstractMotivation: Nuclear magnetic resonance (NMR) has been widely used as a powerful tool to determine the 3D structures of proteins in vivo. However, the post-spectra processing stage of NMR structure determination usually involves a tremendous amount of time and expert knowledge, which includes peak picking, chemical shift assignment and structure calculation steps. Detecting accurate peaks from the NMR spectra is a prerequisite for all following steps, and thus remains a key problem in automatic NMR structure determination. Results: We introduce WaVPeak, a fully automatic peak detection method. WaVPeak first smoothes the given NMR spectrum by wavelets. The peaks are then identified as the local maxima. The false positive peaks are filtered out efficiently by considering the volume of the peaks. WaVPeak has two major advantages over the state-of-the-art peak-picking methods. First, through wavelet-based smoothing, WaVPeak does not eliminate any data point in the spectra. Therefore, WaVPeak is able to detect weak peaks that are embedded in the noise level. NMR spectroscopists need the most help isolating these weak peaks. Second, WaVPeak estimates the volume of the peaks to filter the false positives. This is more reliable than intensity-based filters that are widely used in existing methods. We evaluate the performance of WaVPeak on the benchmark set proposed by PICKY (Alipanahi et al., 2009), one of the most accurate methods in the literature. The dataset comprises 32 2D and 3D spectra from eight different proteins. Experimental results demonstrate that WaVPeak achieves an average of 96%, 91%, 88%, 76% and 85% recall on 15N-HSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are considered, WaVPeak significantly outperforms PICKY. The Author(s) 2012. Published by Oxford University Press.
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0
dc.subjectprotein
dc.subjectchemistry
dc.subjectcomputer program
dc.subjectinformation processing
dc.subjectmethodology
dc.subjectnuclear magnetic resonance spectroscopy
dc.subjectwavelet analysis
dc.subjectAutomatic Data Processing
dc.subjectMagnetic Resonance Spectroscopy
dc.subjectProteins
dc.subjectSoftware
dc.subjectWavelet Analysis
dc.titleWaVPeak: Picking NMR peaks through wavelet-based smoothing and volume-based filtering
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.identifier.pmcidPMC3315717
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionThe Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen 361000, China
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.dateFOA2018-06-13T15:23:26Z


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.