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Kwon, Taejoon
TaejoonLab
Research Interests
  • Systems biology, computational biology, functional genomics

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MSblender: A Probabilistic Approach for Integrating Peptide Identifications from Multiple Database Search Engines

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Title
MSblender: A Probabilistic Approach for Integrating Peptide Identifications from Multiple Database Search Engines
Other Titles
MSblender: A Probabilistic Approach for Integrating Peptide Identifications from Multiple Database Search Engines
Author
Kwon, TaejoonChoi, HyungwonVogel, ChristineNesvizhskii, Alexey I.Marcotte, Edward M.
Keywords
TANDEM MASS-SPECTRA; SHOTGUN PROTEOMICS; PROTEIN IDENTIFICATION; MS/MS; SENSITIVITY; ALGORITHM; MODEL
Issue Date
2011-07
Publisher
AMER CHEMICAL SOC
Citation
JOURNAL OF PROTEOME RESEARCH, v.10, no.7, pp.2949 - 2958
Abstract
Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses
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DOI
10.1021/pr2002116
ISSN
1535-3893
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BME_Journal Papers
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