cid 423b300474115b0dce842e313b08689fcc2ea5bdda359e40d2d5db7bf0618b6d · original · open access · via openalex
Title: Speeding Up Percolator Authors: John T. Halloran; Hantian Zhang; Kaan Kara; Cédric Renggli; Matthew The; Ce Zhang; David M. Rocke; Lukas Käll; William Stafford Noble Venue: Journal of Proteome Research Year: 2019 Canonical: doi:10.1021/acs.jproteome.9b00288 URL: https://doi.org/10.1021/acs.jproteome.9b00288 Collector: openalex OA: true ABSTRACT 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.