In-depth evaluation of software tools for data-independent acquisition based label-free quantification

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...

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Bibliographic Details
Published in:Proteomics (Weinheim) 2015-09, Vol.15 (18), p.3140-3151
Main Authors: Kuharev, Jörg, Navarro, Pedro, Distler, Ute, Jahn, Olaf, Tenzer, Stefan
Format: Article
Language:English
Subjects:
Bioinformatics
Biological properties
Biological samples
Clustering
Coefficient of variation
Computer programs
Computer simulation
Consortia
Data-independent acquisition
E coli
Escherichia coli
Humans
Ion mobility separation
Ionic mobility
Label-free quantification
Mass Spectrometry - methods
Peptide Fragments
Proteins
Proteome - analysis
Proteomes
Proteomics - methods
Reproducibility
Retention time
Software
Software development tools
Software packages
Yeast
Yeasts
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Summary: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 MSE 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).
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.201400396