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1. Kwon T, Choi H, Vogel C, Nesvizhskii AI, Marcotte EM: MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines. J Proteome Res; 2011 Jul 1;10(7):2949-58
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines.
  • 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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  • (PMID = 21488652.001).
  • [ISSN] 1535-3907
  • [Journal-full-title] Journal of proteome research
  • [ISO-abbreviation] J. Proteome Res.
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R01 GM076536; United States / NCI NIH HHS / CA / CA126239-01; United States / NIGMS NIH HHS / GM / R01 GM088624; United States / NCI NIH HHS / CA / R01-CA126239; United States / NCI NIH HHS / CA / R01 CA126239; United States / NIGMS NIH HHS / GM / R01 GM067779-07; United States / NIGMS NIH HHS / GM / R01 GM067779; United States / NIGMS NIH HHS / GM / GM067779-07; United States / NIGMS NIH HHS / GM / R01 GM094231; United States / NIGMS NIH HHS / GM / R01 GM094231-01; United States / NIGMS NIH HHS / GM / GM094231-01; United States / NCI NIH HHS / CA / R01 CA126239-01; United States / NIGMS NIH HHS / GM / R01-GM094231; United States / NIGMS NIH HHS / GM / GM088624-02; United States / NIGMS NIH HHS / GM / R01 GM088624-02
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
  • [Publication-country] United States
  • [Chemical-registry-number] 0 / Escherichia coli Proteins; 0 / Peptides; 0 / Saccharomyces cerevisiae Proteins
  • [Other-IDs] NLM/ NIHMS292806; NLM/ PMC3128686
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