Speeding Up Percolator.
The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these peptide-spectrum matches can be improved substantially using a machine learning postprocessor. Here, we describe our efforts to speed up one widely used postprocessor, Percolator. The improved software is dramatically faster than the previous version of Percolator, even when using relatively few processors. We tested the new version of Percolator on a data set containing over 215 million spectra and recorded an overall reduction to 23% of the running time as compared to the unoptimized code. We also show that the memory footprint required by these speedups is modest relative to that of the original version of Percolator.
['Halloran|John T|JT|', 'Zhang|Hantian|H|', 'Kara|Kaan|K|', 'Renggli|Cédric|C|', 'The|Matthew|M|', 'Zhang|Ce|C|', 'Rocke|David M|DM|', 'Käll|Lukas|L|0000-0001-5689-9797', 'Noble|William Stafford|WS|0000-0001-7283-4715']
D000465:Algorithms D030562:Databases, Protein D000069550:Machine Learning D010455:Peptides D040901:Proteomics D012984:Software D053719:Tandem Mass Spectrometry
percolator tandem mass spectrometry machine learning SVM support vector machine