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Nat Biotechnol. 2016 Nov;34(11):1130-1136. doi: 10.1038/nbt.3685. Epub 2016 Oct 3.

A multicenter study benchmarks software tools for label-free proteome quantification.

Author information

1
Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
2
Department of Biology, Institute of Molecular Systems Biology, Eidgenoessische Technische Hochschule (IMSB-ETH) Zurich, Zurich, Switzerland.
3
Biognosys AG, Schlieren, Switzerland.
4
Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
5
AB Sciex, Concord, Ontario, Canada.
6
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
7
PhD Program in Systems Biology, University of Zurich and Eidgenoessische Technische Hochschule (ETH) Zurich, Zurich, Switzerland.
8
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
9
Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
10
Faculty of Science, University of Zurich, Zurich, Switzerland.
#
Contributed equally

Abstract

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.

PMID:
27701404
PMCID:
PMC5120688
DOI:
10.1038/nbt.3685
[Indexed for MEDLINE]
Free PMC Article

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