title: In-depth evaluation of software tools for data-independent acquisition based label-free quantification.
abstract: Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240).
authors.affiliation: Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Kuharev
authors.affiliation: Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
authors.forename: Pedro
authors.initials: P
authors.lastname: Navarro
authors.affiliation: Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
authors.forename: Ute
authors.initials: U
authors.lastname: Distler
authors.affiliation: Proteomics Group, Max-Planck-Institute of Experimental Medicine, Göttingen, Germany.
authors.forename: Olaf
authors.initials: O
authors.lastname: Jahn
authors.affiliation: Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Tenzer
mesh_terms: D004926:Escherichia coli; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D010446:Peptide Fragments; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software; D015003:Yeasts
chemical_list: D010446:Peptide Fragments; D020543:Proteome
keywords: Bioinformatics; Data-independent acquisition; Ion mobility separation; Label-free quantification