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Speeding Up Percolator
Univ Calif Davis, Dept Publ Hlth Sci, Davis, CA 95616 USA..
Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland..
Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland..
Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland..
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2019 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 18, no 9, p. 3353-3359Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
AMER CHEMICAL SOC , 2019. Vol. 18, no 9, p. 3353-3359
Keywords [en]
tandem mass spectrometry, machine learning, support vector machine, SVM, percolator
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-261034DOI: 10.1021/acs.jproteome.9b00288ISI: 000485089100012PubMedID: 31407580Scopus ID: 2-s2.0-85071999233OAI: oai:DiVA.org:kth-261034DiVA, id: diva2:1356730
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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The, MatthewKäll, Lukas

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