Bayesian Peak Picking for NMR Spectra

Handle URI:
http://hdl.handle.net/10754/552357
Title:
Bayesian Peak Picking for NMR Spectra
Authors:
Cheng, Yichen; Gao, Xin ( 0000-0002-7108-3574 ) ; Liang, Faming
Abstract:
Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Bayesian Peak Picking for NMR Spectra 2014, 12 (1):39 Genomics, Proteomics & Bioinformatics
Journal:
Genomics, Proteomics & Bioinformatics
Issue Date:
Feb-2014
DOI:
10.1016/j.gpb.2013.07.003
PubMed ID:
24184964
PubMed Central ID:
PMC4411369
Type:
Article
ISSN:
16720229
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1672022913001162
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCheng, Yichenen
dc.contributor.authorGao, Xinen
dc.contributor.authorLiang, Famingen
dc.date.accessioned2015-05-06T13:30:58Zen
dc.date.available2015-05-06T13:30:58Zen
dc.date.issued2014-02en
dc.identifier.citationBayesian Peak Picking for NMR Spectra 2014, 12 (1):39 Genomics, Proteomics & Bioinformaticsen
dc.identifier.issn16720229en
dc.identifier.pmid24184964en
dc.identifier.doi10.1016/j.gpb.2013.07.003en
dc.identifier.urihttp://hdl.handle.net/10754/552357en
dc.description.abstractProtein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.en
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1672022913001162en
dc.rightsArchived with thanks to Genomics, Proteomics & Bioinformatics. http://creativecommons.org/licenses/by-nc-sa/3.0/en
dc.subjectMarkov chain Monte Carloen
dc.subjectNuclear magnetic resonanceen
dc.subjectPeak pickingen
dc.titleBayesian Peak Picking for NMR Spectraen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalGenomics, Proteomics & Bioinformaticsen
dc.identifier.pmcidPMC4411369en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX 77843, USAen
kaust.authorGao, Xinen
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