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

软件 稳健性(进化) 计算机科学 蛋白质组 数据挖掘 蛋白质组学 定量蛋白质组学 无标记量化 生物信息学 生物 生物化学 基因 程序设计语言
作者
Pedro Navarro,Jörg Kuharev,Ludovic Gillet,Oliver M. Bernhardt,Brendan MacLean,Hannes Röst,Stephen Tate,Chih‐Chiang Tsou,Lukas Reiter,Ute Distler,George Rosenberger,Yasset Pérez‐Riverol,Alexey I. Nesvizhskii,Ruedi Aebersold,Stefan Tenzer
出处
期刊:Nature Biotechnology [Nature Portfolio]
卷期号:34 (11): 1130-1136 被引量:366
标识
DOI:10.1038/nbt.3685
摘要

LFQbench, a software tool to assess the quality of label-free quantitative proteomics analyses, enables developers to benchmark and improve analytic methods. 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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