Abstract
The protein inference problem represents a major challenge in shotgun proteomics. Here we describe a novel Bayesian approach to address this challenge that incorporates the predicted peptide detectabilities as the prior probabilities of peptide identification. Our model removes some unrealistic assumptions used in previous approaches and provides a rigorious probabilistic solution to this problem. We used a complex synthetic protein mixture to test our method, and obtained promising results.
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Li, Y.F., Arnold, R.J., Li, Y., Radivojac, P., Sheng, Q., Tang, H. (2008). A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics. In: Vingron, M., Wong, L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science(), vol 4955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78839-3_15
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DOI: https://doi.org/10.1007/978-3-540-78839-3_15
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