A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics
- 1 August 2009
- journal article
- research article
- Published by Mary Ann Liebert Inc in Journal of Computational Biology
- Vol. 16 (8) , 1183-1193
- https://doi.org/10.1089/cmb.2009.0018
Abstract
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.Keywords
This publication has 22 references indexed in Scilit:
- Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and TransparencyJournal of Proteome Research, 2007
- A high-quality catalog of the Drosophila melanogaster proteomeNature Biotechnology, 2007
- Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometryNature Methods, 2007
- Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulationNature Biotechnology, 2006
- A computational approach toward label-free protein quantification using predicted peptide detectabilityBioinformatics, 2006
- RT‐PSM, a real‐time program for peptide‐spectrum matching with statistical significanceRapid Communications in Mass Spectrometry, 2006
- TANDEM: matching proteins with tandem mass spectraBioinformatics, 2004
- Mass spectrometry-based proteomicsNature, 2003
- Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database SearchAnalytical Chemistry, 2002
- Probability-based protein identification by searching sequence databases using mass spectrometry dataElectrophoresis, 1999