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

0580617 - ÚOCHB 2025 RIV US eng J - Journal Article
Bouyssié, D. - Altiner, P. - Capella-Gutierrez, S. - Fernandez, J. M. - Hagemeijer, Y. P. - Horvatovich, P. - Hubálek, Martin - Levander, F. - Mauri, P. - Palmblad, M. - Raffelsberger, W. - Rodriguez-Navas, L. - Di Silvestre, D. - Kunkli, B. T. - Uszkoreit, J. - Vandenbrouck, Y. - Vizcaíno, J. A. - Winkelhardt, D. - Schwämmle, V.
WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows.
Journal of Proteome Research. Roč. 23, č. 1 (2024), s. 418-429. ISSN 1535-3893. E-ISSN 1535-3907
Institutional support: RVO:61388963
Keywords : workflow * data analysis * benchmarking * label-free proteomics * quality metrics
OECD category: Analytical chemistry
Impact factor: 4.4, year: 2022
Method of publishing: Limited access
https://doi.org/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.
Permanent Link: https://hdl.handle.net/11104/0349387


Research data: CRAN