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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...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2016
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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. |
format | Online Article Text |
id | pubmed-5120688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
record_format | MEDLINE/PubMed |
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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