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  • 1.
    Halloran, John T.
    et al.
    Univ Calif Davis, Dept Publ Hlth Sci, Davis, CA 95616 USA..
    Zhang, Hantian
    Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland..
    Kara, Kaan
    Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland..
    Renggli, Cedric
    Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland..
    The, Matthew
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Zhang, Ce
    Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland..
    Rocke, David M.
    Univ Calif Davis, Dept Publ Hlth Sci, Davis, CA 95616 USA..
    Käll, Lukas
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Noble, William Stafford
    Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA.;Univ Washington, Paul Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..
    Speeding Up Percolator2019In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 18, no 9, p. 3353-3359Article in journal (Refereed)
    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.

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