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Generating high-quality libraries for DIA-MS with empirically-corrected peptide predictions

View ORCID ProfileBrian C. Searle, View ORCID ProfileKristian E. Swearingen, Christopher A. Barnes, View ORCID ProfileTobias Schmidt, View ORCID ProfileSiegfried Gessulat, View ORCID ProfileBernhard Kuster, View ORCID ProfileMathias Wilhelm
doi: https://doi.org/10.1101/682245
Brian C. Searle
1Institute for Systems Biology, Seattle, WA, USA
2Proteome Software, Inc. Portland, OR, USA
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  • For correspondence: bsearle@systemsbiology.org
Kristian E. Swearingen
1Institute for Systems Biology, Seattle, WA, USA
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Christopher A. Barnes
3Novo Nordisk Research Center Seattle, Inc. Seattle, WA, USA
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Tobias Schmidt
4Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
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Siegfried Gessulat
4Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
5SAP SE, Potsdam, Germany
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Bernhard Kuster
4Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
6Bavarian Center for Biomolecular Mass Spectrometry, Freising, Germany
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Mathias Wilhelm
4Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
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ABSTRACT

Data-independent acquisition approaches typically rely on sample-specific spectrum libraries requiring offline fractionation and tens to hundreds of injections. We demonstrate a new library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.

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Posted September 04, 2019.
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Generating high-quality libraries for DIA-MS with empirically-corrected peptide predictions
Brian C. Searle, Kristian E. Swearingen, Christopher A. Barnes, Tobias Schmidt, Siegfried Gessulat, Bernhard Kuster, Mathias Wilhelm
bioRxiv 682245; doi: https://doi.org/10.1101/682245
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Generating high-quality libraries for DIA-MS with empirically-corrected peptide predictions
Brian C. Searle, Kristian E. Swearingen, Christopher A. Barnes, Tobias Schmidt, Siegfried Gessulat, Bernhard Kuster, Mathias Wilhelm
bioRxiv 682245; doi: https://doi.org/10.1101/682245

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