4.8 Article

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

Journal

NATURE BIOTECHNOLOGY
Volume 34, Issue 11, Pages 1130-+

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/nbt.3685

Keywords

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Funding

  1. Bundesministerium fur Bildung und Forschung (BMBF) [0316179C]
  2. Deutsche Forschungsgemeinschaft (DFG) [ST599/1-1, ST599/2-1]
  3. Mainz University (Research Center for Immunotherapy)
  4. Swiss National Science Foundation [P2EZP3_162268, 3100A_166435]
  5. Biotechnology and Biological Sciences Research Council (BBSRC) [BB/K01997X/1]
  6. US National Institutes of Health [5R01GM94231]
  7. European Research Council (ERC) [AdG 233226, ERC-2014-AdG 670821]
  8. PhosphonetX project of SystemsX.ch
  9. Swiss National Science Foundation (SNF) [P2EZP3_162268] Funding Source: Swiss National Science Foundation (SNF)

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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.

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