Title |
MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques
|
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Published in |
BMC Genomics, October 2012
|
DOI | 10.1186/1471-2164-13-s5-s4 |
Pubmed ID | |
Authors |
Fabio R Cerqueira, Ricardo S Ferreira, Alcione P Oliveira, Andreia P Gomes, Humberto JO Ramos, Armin Graber, Christian Baumgartner |
Abstract |
The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry) is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs) needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. |
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