Optimization of the Use of Consensus Methods for the Detection and Putative Identification of Peptides via Mass-spectrometry Using Protein Standard Mixtures
Tamanna Sultana, Rick Jordan, James Lyons-Weiler*
Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories and Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
- *Corresponding Author:
- James Lyons-Weiler
Bioinformatics Analysis Core
Genomics and Proteomics Core Laboratories and
Department of Biomedical Informatics
University of Pittsburgh, Pittsburgh, PA,
Email: [email protected]
Received Date: May 20, 2009; Accepted Date: June 16, 2009; Published Date: June 16, 2009
Citation: Sultana T, Jordan R, Lyons-Weiler J (2009) Optimization of the Use of Consensus Methods for the Detection and Putative Identification of Peptides via Mass-spectrometry Using Protein Standard Mixtures. J Proteomics Bioinform 2: 262-273. doi: 10.4172/jpb.1000085
Copyright: © 2009 Sultana T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Correct identification of peptides and proteins in complex biological samples from proteomic mass-spec tra is a challenging problem in bioinformatics. The sensit ivity and specificity of identification algorithms depend on underlying scoring methods, some being more sensiti ve, and others more specific. F or high-throughput, auto- mated peptide identification, control over the algo rithm s performance in terms of trade-off between s ensitivity and specificity is desirable. Combinations of algorithms, called ‘consensus meth ods’, have been shown to pro- vide more accurate results than individual algorith ms. However, due to the proliferation of algorithms and their varied internal settings, a systematic understandin g of relative performance of individual and consens us meth- ods are lacking. We performed an in-depth analysis of various approaches to consensus scoring using known protein mixtures, and e valuated the performance of 2310 settings generated from consensus of three different search algorithms: Mascot, Sequest, and X!Tandem. O ur findings indicate that the union of Mascot, Seq uest, and X!Tandem performed well (considering overall ac curacy), and methods using 80-99.9% protein probabi lity and/or minimum 2 peptides and/or 0-50% minimum pept ide probability for protein identification performe d better (on average) among all consensus methods tes ted in terms of overall accuracy. The results also suggest method selection strategies to provide direct contr ol over sensitivity and specificity.