A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics
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Date
2009-08-18
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Mary Ann Liebert, Inc.
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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 rigorous probabilistic model for protein inference and provide practical algorithmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.
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Keywords
algorithms, alignment, combinatorial proteomics, computational molecular biology, databases, mass spectroscopy, proteins, sequence analysis
Citation
Yong Fuga Li, Randy J. Arnold, Yixue Li, Predrag Radivojac, Quanhu Sheng, and Haixu Tang. Journal of Computational Biology. August 2009, 16(8): 1183-1193. doi:10.1089/cmb.2009.0018.
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Copyright Mary Ann Liebert, Inc.
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Article