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A multi-center study benchmarks software tools for label-free proteome quantification

The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-U...

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Autores principales: Navarro, Pedro, Kuharev, Jörg, Gillet, Ludovic C, Bernhardt, Oliver M., MacLean, Brendan, Röst, Hannes L., Tate, Stephen A., Tsou, Chih-Chiang, Reiter, Lukas, Distler, Ute, Rosenberger, George, Perez-Riverol, Yasset, Nesvizhskii, Alexey I., Aebersold, Ruedi, Tenzer, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5120688/
https://www.ncbi.nlm.nih.gov/pubmed/27701404
http://dx.doi.org/10.1038/nbt.3685
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author Navarro, Pedro
Kuharev, Jörg
Gillet, Ludovic C
Bernhardt, Oliver M.
MacLean, Brendan
Röst, Hannes L.
Tate, Stephen A.
Tsou, Chih-Chiang
Reiter, Lukas
Distler, Ute
Rosenberger, George
Perez-Riverol, Yasset
Nesvizhskii, Alexey I.
Aebersold, Ruedi
Tenzer, Stefan
author_facet Navarro, Pedro
Kuharev, Jörg
Gillet, Ludovic C
Bernhardt, Oliver M.
MacLean, Brendan
Röst, Hannes L.
Tate, Stephen A.
Tsou, Chih-Chiang
Reiter, Lukas
Distler, Ute
Rosenberger, George
Perez-Riverol, Yasset
Nesvizhskii, Alexey I.
Aebersold, Ruedi
Tenzer, Stefan
author_sort Navarro, Pedro
collection PubMed
description The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.
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spelling pubmed-51206882017-04-03 A multi-center study benchmarks software tools for label-free proteome quantification Navarro, Pedro Kuharev, Jörg Gillet, Ludovic C Bernhardt, Oliver M. MacLean, Brendan Röst, Hannes L. Tate, Stephen A. Tsou, Chih-Chiang Reiter, Lukas Distler, Ute Rosenberger, George Perez-Riverol, Yasset Nesvizhskii, Alexey I. Aebersold, Ruedi Tenzer, Stefan Nat Biotechnol Article The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics. 2016-10-03 2016-11 /pmc/articles/PMC5120688/ /pubmed/27701404 http://dx.doi.org/10.1038/nbt.3685 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Navarro, Pedro
Kuharev, Jörg
Gillet, Ludovic C
Bernhardt, Oliver M.
MacLean, Brendan
Röst, Hannes L.
Tate, Stephen A.
Tsou, Chih-Chiang
Reiter, Lukas
Distler, Ute
Rosenberger, George
Perez-Riverol, Yasset
Nesvizhskii, Alexey I.
Aebersold, Ruedi
Tenzer, Stefan
A multi-center study benchmarks software tools for label-free proteome quantification
title A multi-center study benchmarks software tools for label-free proteome quantification
title_full A multi-center study benchmarks software tools for label-free proteome quantification
title_fullStr A multi-center study benchmarks software tools for label-free proteome quantification
title_full_unstemmed A multi-center study benchmarks software tools for label-free proteome quantification
title_short A multi-center study benchmarks software tools for label-free proteome quantification
title_sort multi-center study benchmarks software tools for label-free proteome quantification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5120688/
https://www.ncbi.nlm.nih.gov/pubmed/27701404
http://dx.doi.org/10.1038/nbt.3685
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