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

By Pedro Navarro, Jörg Kuharev, Ludovic C. Gillet, Oliver M. Bernhardt, Brendan MacLean, Hannes L. Röst, S. Tate, Chih-Chiang Tsou, Lukas Reiter, Ute Distler and 5 more. Nature Biotechnology, 2016

316 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/fa98e294f985c43261753139ce319f23cc246095

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Citations in the corpus

IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs (2020, methodology): “We used LFQbench (39) to summarize the analyses and visualize the results (Figure 3).”
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, unknown): “The gain achieved by moving from 32 fixed to 64 variable windows has been demonstrated both qualitatively and quantitatively (Navarro et al , 2016).”
Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics (2020, background): “8, 9 Skyline is currently being used by many thousands of researchers worldwide, predominantly for quantitative proteomics applications.”