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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Autores principales:	Brian C. Searle, Kristian E. Swearingen, Christopher A. Barnes, Tobias Schmidt, Siegfried Gessulat, Bernhard Küster, Mathias Wilhelm
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/fb9023947e6a4fa1a16ec3f069b1e441
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spelling	oai:doaj.org-article:fb9023947e6a4fa1a16ec3f069b1e4412021-12-02T16:49:14ZGenerating high quality libraries for DIA MS with empirically corrected peptide predictions10.1038/s41467-020-15346-12041-1723https://doaj.org/article/fb9023947e6a4fa1a16ec3f069b1e4412020-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15346-1https://doaj.org/toc/2041-1723Data-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.Brian C. SearleKristian E. SwearingenChristopher A. BarnesTobias SchmidtSiegfried GessulatBernhard KüsterMathias WilhelmNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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
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.
format	article
author	Brian C. Searle Kristian E. Swearingen Christopher A. Barnes Tobias Schmidt Siegfried Gessulat Bernhard Küster Mathias Wilhelm
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 Portfolio
publishDate	2020
url	https://doaj.org/article/fb9023947e6a4fa1a16ec3f069b1e441
work_keys_str_mv	AT briancsearle generatinghighqualitylibrariesfordiamswithempiricallycorrectedpeptidepredictions AT kristianeswearingen generatinghighqualitylibrariesfordiamswithempiricallycorrectedpeptidepredictions AT christopherabarnes generatinghighqualitylibrariesfordiamswithempiricallycorrectedpeptidepredictions AT tobiasschmidt generatinghighqualitylibrariesfordiamswithempiricallycorrectedpeptidepredictions AT siegfriedgessulat generatinghighqualitylibrariesfordiamswithempiricallycorrectedpeptidepredictions AT bernhardkuster generatinghighqualitylibrariesfordiamswithempiricallycorrectedpeptidepredictions AT mathiaswilhelm generatinghighqualitylibrariesfordiamswithempiricallycorrectedpeptidepredictions
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Generating high quality libraries for DIA MS with empirically corrected peptide predictions

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