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

• Published in: Journal of proteome research Vol. 23; no. 1; pp. 418 - 429
• Main Authors: Bouyssié, David, Altıner, Pınar, Capella-Gutierrez, Salvador, Fernández, José M., Hagemeijer, Yanick Paco, Horvatovich, Peter, Hubálek, Martin, Levander, Fredrik, Mauri, Pierluigi, Palmblad, Magnus, Raffelsberger, Wolfgang, Rodríguez-Navas, Laura, Di Silvestre, Dario, Kunkli, Balázs Tibor, Uszkoreit, Julian, Vandenbrouck, Yves, Vizcaíno, Juan Antonio, Winkelhardt, Dirk, Schwämmle, Veit
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 05.01.2024
• Subjects: Benchmarking; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi); Computer and Information Science; Data Analysis; Data- och informationsvetenskap (Datateknik); label-free proteomics; Life Sciences; Natural Sciences; Naturvetenskap; Proteins; Proteomics; quality metrics; Software; Workflow; data analysis; quality metrics; workflow; benchmarking; label-free proteomics
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.3c00636

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.