Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 16, Issue 8, Pages 1183-1193Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2009.0018
Keywords
algorithms; alignment; combinatorial proteomics; computational molecular biology; databases; mass spectroscopy; proteins; sequence analysis
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Funding
- NCI NIH HHS [U24 CA126480, U24 CA126480-01] Funding Source: Medline
- NCRR NIH HHS [P41 RR018942-03S16775, P41 RR018942-028694, R01 RR024236, R01 RR024236-02, R01 RR024236-01A1, P41 RR018942-035068, P41 RR018942-01A29005, P41 RR018942] Funding Source: Medline
- NIGMS NIH HHS [R01 GM103725] Funding Source: Medline
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The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.
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