JSON
Bouyssié D, Altıner P, Capella-Gutierrez S, Fernández JM, Hagemeijer YP, Horvatovich P, Hubálek M, Levander F, Mauri P, Palmblad M, Raffelsberger W, Rodríguez-Navas L, Di Silvestre D, Kunkli BT, Uszkoreit J, Vandenbrouck Y, Vizcaíno JA, Winkelhardt D, Schwämmle V
J. Proteome Res. 23 (1) 418-429 [2024-01-05; online 2023-12-01]
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.
Bioinformatics Support, Infrastructure and Training [Technology development]
PubMed 38038272
DOI 10.1021/acs.jproteome.3c00636
Crossref 10.1021/acs.jproteome.3c00636