Speeding up Percolator.

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ID: 5013
2019
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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 by using a machine learning post-processor. Here, we describe our efforts to speed up one widely used post-processor, 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.
Reference Key
halloran2019speedingjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Halloran, John T;Zhang, Hantian;Kara, Kaan;Renggli, Cedric;The, Matthew;Zhang, Ce;Rocke, David M;Käll, Lukas;Noble, William Stafford;
Journal journal of proteome research
Year 2019
DOI
10.1021/acs.jproteome.9b00288
URL
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