A multicenter study benchmarks software tools for label-free proteome quantification.
- Navarro P Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
- Kuharev J Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
- Gillet LC Department of Biology, Institute of Molecular Systems Biology, Eidgenoessische Technische Hochschule (IMSB-ETH) Zurich, Zurich, Switzerland.
- Bernhardt OM Biognosys AG, Schlieren, Switzerland.
- MacLean B Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
- Röst HL Department of Biology, Institute of Molecular Systems Biology, Eidgenoessische Technische Hochschule (IMSB-ETH) Zurich, Zurich, Switzerland.
- Tate SA AB Sciex, Concord, Ontario, Canada.
- Tsou CC Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
- Reiter L Biognosys AG, Schlieren, Switzerland.
- Distler U Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
- Rosenberger G Department of Biology, Institute of Molecular Systems Biology, Eidgenoessische Technische Hochschule (IMSB-ETH) Zurich, Zurich, Switzerland.
- Perez-Riverol Y European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
- Nesvizhskii AI Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
- Aebersold R Department of Biology, Institute of Molecular Systems Biology, Eidgenoessische Technische Hochschule (IMSB-ETH) Zurich, Zurich, Switzerland.
- Tenzer S Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
- 2016-11-08
Published in:
- Nature biotechnology. - 2016
Algorithms
Benchmarking
Internationality
Mass Spectrometry
Proteome
Reproducibility of Results
Sensitivity and Specificity
Software
Staining and Labeling
English
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, SWATH 2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from sequential window acquisition of all theoretical fragment-ion spectra (SWATH)-MS, which uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test data sets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation-window 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 data sets 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.
- Language
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- English
- Open access status
- green
- Identifiers
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- DOI 10.1038/nbt.3685
- PMID 27701404
- Persistent URL
- https://folia.unifr.ch/global/documents/155248
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