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Activity Number: 532
Type: Invited
Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
Sponsor: Caucus for Women in Statistics
Abstract #310740
Title: Improved Protein Inference from Tandem Mass Spectrometry Data
Author(s): Susmita Datta*+ and Riten Mitra
Companies: University of Louisville and University of Louisville
Keywords: Mass Spectrometry ; MS/MS ; Bayesian
Abstract:

Protein identification using tandem mass spectrometry MS/MS data remains to be an important area of research. Most of the major researches in this area provide a two-step procedure of identifying the peptide first followed identifying the protein next. In this setup, the interdependence of peptides and proteins are neglected resulting relatively inaccurate protein identification. In this research we develop an improved statistical methodology of one step protein identification using a nested hierarchical Bayesian model. Exploiting the fact that the complete conditionals of the proposed joint model are tractable, we propose and implement a Gibbs sampling scheme for full posterior inference. This is, to the best of our knowledge, the first implementation of a fully posterior inference scheme in this problem. It provides simultaneous uncertainty measures associated with protein detections in terms of posterior probabilities. The results from data analysis point towards a high sensitivity and specificity of our proposed approach. For a reasonable thresholding of the posterior probabilities, the algorithm is able to discard the entire set of decoy proteins present in the sample.


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