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

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

Data-independent acquisition-mass spectrometry (MS) typically requires many preparatory MS runs to produce experiment-specific spectral libraries. Here, the authors show that empirical correction of in silico predicted spectral libraries enables efficient generation of high-quality experiment-specif...

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Main Authors: Brian C. Searle, Kristian E. Swearingen, Christopher A. Barnes, Tobias Schmidt, Siegfried Gessulat, Bernhard Küster, Mathias Wilhelm
Format: Article
Language:English
Published: Nature Publishing Group 2020-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-15346-1
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spelling doaj-fb9023947e6a4fa1a16ec3f069b1e4412021-05-11T08:41:39ZengNature Publishing GroupNature Communications2041-17232020-03-0111111010.1038/s41467-020-15346-1Generating high quality libraries for DIA MS with empirically corrected peptide predictionsBrian C. Searle0Kristian E. Swearingen1Christopher A. Barnes2Tobias Schmidt3Siegfried Gessulat4Bernhard Küster5Mathias Wilhelm6Institute for Systems BiologyInstitute for Systems BiologyNovo Nordisk Research Center Seattle, Inc.Technical University of MunichTechnical University of MunichTechnical University of MunichTechnical University of MunichData-independent acquisition-mass spectrometry (MS) typically requires many preparatory MS runs to produce experiment-specific spectral libraries. Here, the authors show that empirical correction of in silico predicted spectral libraries enables efficient generation of high-quality experiment-specific libraries.https://doi.org/10.1038/s41467-020-15346-1
collection DOAJ
language English
format Article
sources DOAJ
author Brian C. Searle
Kristian E. Swearingen
Christopher A. Barnes
Tobias Schmidt
Siegfried Gessulat
Bernhard Küster
Mathias Wilhelm
spellingShingle Brian C. Searle
Kristian E. Swearingen
Christopher A. Barnes
Tobias Schmidt
Siegfried Gessulat
Bernhard Küster
Mathias Wilhelm
Generating high quality libraries for DIA MS with empirically corrected peptide predictions
Nature Communications
author_facet Brian C. Searle
Kristian E. Swearingen
Christopher A. Barnes
Tobias Schmidt
Siegfried Gessulat
Bernhard Küster
Mathias Wilhelm
author_sort Brian C. Searle
title Generating high quality libraries for DIA MS with empirically corrected peptide predictions
title_short Generating high quality libraries for DIA MS with empirically corrected peptide predictions
title_full Generating high quality libraries for DIA MS with empirically corrected peptide predictions
title_fullStr Generating high quality libraries for DIA MS with empirically corrected peptide predictions
title_full_unstemmed Generating high quality libraries for DIA MS with empirically corrected peptide predictions
title_sort generating high quality libraries for dia ms with empirically corrected peptide predictions
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2020-03-01
description Data-independent acquisition-mass spectrometry (MS) typically requires many preparatory MS runs to produce experiment-specific spectral libraries. Here, the authors show that empirical correction of in silico predicted spectral libraries enables efficient generation of high-quality experiment-specific libraries.
url https://doi.org/10.1038/s41467-020-15346-1
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