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Journal Articles Journal of Proteome Research Year : 2023

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

Fredrik Levander
Magnus Palmblad
Balázs Tibor Kunkli
Veit Schwämmle

Abstract

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Dates and versions

hal-04597844 , version 1 (03-06-2024)

Identifiers

Cite

David Bouyssié, Pınar Altıner, Salvador Capella-Gutierrez, José Fernández, Yanick Paco Hagemeijer, et al.. WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows. Journal of Proteome Research, 2023, 23 (1), pp.418-429. ⟨10.1021/acs.jproteome.3c00636⟩. ⟨hal-04597844⟩
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