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| - | ===== Improvements to the Percolator algorithm for peptide identification from shotgun | + | ===== Improvements to the Percolator algorithm for peptide identification from shotgun |
| - | ics data sets ===== | + | |
| + | // | ||
| + | Shotgun proteomics coupled with database search software allows the identification of a large | ||
| + | number of peptides in a single experiment. However, some existing search algorithms, such as | ||
| + | SEQUEST, use score functions that are designed primarily to identify the best peptide for a given | ||
| + | spectrum. Consequently, | ||
| + | function Xcorr fails to discriminate accurately between correct and incorrect peptide identifications. | ||
| + | Several machine learning methods have been proposed to address the resulting classification task of | ||
| + | distinguishing between correct and incorrect peptide-spectrum matches (PSMs). A recent example | ||
| + | is Percolator, which uses semi-supervised learning and a decoy database search strategy to learn to | ||
| + | distinguish between correct and incorrect PSMs identified by a database search algorithm. The | ||
| + | current work describes three improvements to Percolator. (1) Percolator’s heuristic optimization is | ||
| + | replaced with a clear objective function, with intuitive reasons behind its choice. (2) Tractable | ||
| + | nonlinear models are used instead of linear models, leading to improved accuracy over the original | ||
| + | Percolator. (3) A method, Q-ranker, for directly optimizing the number of identified spectra at a | ||
| + | specified q value is proposed, which achieves further gains. | ||