Linked Life Data

19813282

Source:http://linkedlifedata.com/resource/pubmed/id/19813282

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
21
pubmed:dateCreated
2009-10-19
pubmed:abstractText
Tandem mass spectra contain noisy peaks which make peak picking for peptide identification difficult. Moreover, all spectral peaks can be shifted due to systematic measurement errors. In this paper, a novel use of an isotope pattern vector (IPV) is proposed for denoising and systematic measurement error prediction. By matching the experimental IPVs with the theoretical IPVs of candidate fragment ions, true ionic peaks can be identified. Furthermore, these identified experimental IPVs and their corresponding theoretical IPVs are used in an optimization process to predict the systematic measurement error associated with the target spectrum. In return, the subsequent spectral data calibration based on the predicted systematic measurement error enhances the data quality. We show that such an integrated denoising and calibration process leads to significantly improved peptide and protein identification. Different from the commonly employed chemical calibration methods, our IPV-based method is a purely computational method for individual spectra analysis and globally optimizes the use of spectral data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1097-0231
pubmed:author
pubmed:copyrightInfo
Copyright 2009 John Wiley & Sons, Ltd.
pubmed:issnType
Electronic
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3448-56
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Isotope pattern vector based tandem mass spectral data calibration for improved peptide and protein identification.
pubmed:affiliation
Computer Science, University of Missouri, 110 Life Sciences Building, Columbia, MO 65211, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't