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Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level
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A multicenter study benchmarks software tools for label-free proteome quantification.

Pedro Navarro1, Jörg Kuharev1, Ludovic C Gillet2

  • 1Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.

Nature Biotechnology
|November 8, 2016
PubMed
Summary
This summary is machine-generated.

Accurate protein quantification in mass spectrometry (MS)-based proteomics relies on software. This study evaluated five leading software tools for sequential window acquisition of all theoretical fragment-ion spectra (SWATH)-MS, finding they offer robust performance after optimization.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Mass spectrometry (MS)-based proteomics is crucial for protein quantification.
  • Data-independent acquisition (DIA), specifically sequential window acquisition of all theoretical fragment-ion spectra (SWATH)-MS, is widely used for label-free protein quantification.
  • The performance of MS instruments, acquisition methods, and data analysis software significantly impacts quantification accuracy and consistency.

Purpose of the Study:

  • To evaluate the performance of five widely used software tools for SWATH-MS data processing: OpenSWATH, SWATH 2.0, Skyline, Spectronaut, and DIA-Umpire.
  • To develop a standardized benchmarking framework (LFQbench) for assessing label-free quantitative MS software.
  • To provide reference datasets for software developers to improve their tools.

Main Methods:

  • Utilized high-complexity test datasets from hybrid proteome samples with defined quantitative compositions.
  • Acquired data on two different MS instruments with varying SWATH isolation-window configurations.
  • Developed LFQbench, an R package, to calculate precision, accuracy, identification performance, robustness, and specificity metrics for label-free quantitative MS software.

Main Results:

  • Initial evaluation revealed performance variations among the five software tools.
  • Reference datasets facilitated software developers in optimizing their respective tools.
  • After optimization, all evaluated software tools demonstrated highly convergent identification and reliable quantification performance.
  • The optimized tools underscored their robustness for label-free quantitative proteomics.

Conclusions:

  • The evaluated software tools, when optimized, provide robust and reliable performance for label-free quantitative proteomics using SWATH-MS.
  • Standardized benchmarking and reference datasets are essential for consistent evaluation and improvement of quantitative proteomics software.
  • The collaborative approach enabled significant advancements in the accuracy and reliability of MS-based protein quantification.