6533b7d9fe1ef96bd126d7dc
RESEARCH PRODUCT
A multicenter study benchmarks software tools for label-free proteome quantification
George RosenbergerGeorge RosenbergerLudovic C GilletJörg KuharevStephen TateYasset Perez-riverolChih-chiang TsouPedro NavarroBrendan MacleanStefan TenzerLukas ReiterAlexey I. NesvizhskiiRuedi AebersoldRuedi AebersoldOliver M. BernhardtHannes L. RöstUte Distlersubject
0301 basic medicineInternationalityProteomeComputer sciencemedia_common.quotation_subjectSoftware toolQuantitative proteomicsBiomedical EngineeringBioengineeringcomputer.software_genreBioinformaticsSensitivity and SpecificityApplied Microbiology and BiotechnologyArticleMass Spectrometry03 medical and health sciencesSoftwareQuality (business)media_commonLabel freeStaining and Labeling030102 biochemistry & molecular biologybusiness.industryReproducibility of ResultsBenchmarkingComputingMethodologies_PATTERNRECOGNITION030104 developmental biologyMulticenter studyProteomeBenchmark (computing)Molecular MedicineData miningbusinesscomputerAlgorithmsSoftwareBiotechnologydescription
The 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, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows 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 datasets 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.
year | journal | country | edition | language |
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2016-01-01 | Nature Biotechnology |