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Improving Protein Detection Confidence Using SWATH‐Mass Spectrometry with Large Peptide Reference Libraries
Author(s) -
Wu Jemma X.,
Pascovici Dana,
Ignjatovic Vera,
Song Xiaomin,
Krisp Christoph,
Molloy Mark P.
Publication year - 2017
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201700174
Subject(s) - workflow , computer science , matching (statistics) , proteome , mass spectrometry , in silico , computational biology , data mining , database , bioinformatics , chromatography , chemistry , biology , biochemistry , mathematics , statistics , gene
Protein quantification using data‐independent acquisition methods such as SWATH‐MS most commonly relies on spectral matching to a reference MS/MS assay library. To enable deep proteome coverage and efficient use of existing data, in silico approaches have been described to use archived or publicly available large reference spectral libraries for spectral matching. Since implicit in the use of larger libraries is the increasing likelihood of false‐discoveries, new workflows are needed to ensure high confidence in protein matching under these conditions. We present a workflow which introduces a range of filters and thresholds aimed at increasing confidence that the resulting proteins are reliably detected and their quantitation is consistent and reproducible. We demonstrated the workflow using extended libraries with SWATH data from human plasma samples and yeast‐spiked human K562 cell lysate digest.