WaVPeak: Picking NMR peaks through wavelet-based smoothing and volume-based filtering

Handle URI:
http://hdl.handle.net/10754/325433
Title:
WaVPeak: Picking NMR peaks through wavelet-based smoothing and volume-based filtering
Authors:
Liu, Zhi; Abbas, Ahmed; Jing, Bing-Yi; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Motivation: 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Liu 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.
Publisher:
Oxford University Press
Journal:
Bioinformatics
Issue Date:
10-Feb-2012
DOI:
10.1093/bioinformatics/bts078
PubMed ID:
22328784
PubMed Central ID:
PMC3315717
Type:
Article
ISSN:
13674803
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Zhien
dc.contributor.authorAbbas, Ahmeden
dc.contributor.authorJing, Bing-Yien
dc.contributor.authorGao, Xinen
dc.date.accessioned2014-08-27T09:51:08Z-
dc.date.available2014-08-27T09:51:08Z-
dc.date.issued2012-02-10en
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.en
dc.identifier.issn13674803en
dc.identifier.pmid22328784en
dc.identifier.doi10.1093/bioinformatics/bts078en
dc.identifier.urihttp://hdl.handle.net/10754/325433en
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.en
dc.language.isoenen
dc.publisherOxford University Pressen
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.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0en
dc.subjectproteinen
dc.subjectchemistryen
dc.subjectcomputer programen
dc.subjectinformation processingen
dc.subjectmethodologyen
dc.subjectnuclear magnetic resonance spectroscopyen
dc.subjectwavelet analysisen
dc.subjectAutomatic Data Processingen
dc.subjectMagnetic Resonance Spectroscopyen
dc.subjectProteinsen
dc.subjectSoftwareen
dc.subjectWavelet Analysisen
dc.titleWaVPeak: Picking NMR peaks through wavelet-based smoothing and volume-based filteringen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC3315717en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionThe Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen 361000, Chinaen
dc.contributor.institutionDepartment of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kongen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorAbbas, Ahmeden
kaust.authorGao, Xinen

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