TY - JOUR
T1 - Wombat-p
T2 - Benchmarking Label-Free Proteomics Data Analysis Workflows
AU - Bouyssié, David
AU - Altıner, Pınar
AU - Capella-Gutierrez, Salvador
AU - Fernández, José M
AU - Hagemeijer, Yanick Paco
AU - Horvatovich, Peter
AU - Hubálek, Martin
AU - Levander, Fredrik
AU - Mauri, Pierluigi
AU - Palmblad, Magnus
AU - Raffelsberger, Wolfgang
AU - Rodríguez-Navas, Laura
AU - Di Silvestre, Dario
AU - Kunkli, Balázs Tibor
AU - Uszkoreit, Julian
AU - Vandenbrouck, Yves
AU - Vizcaíno, Juan Antonio
AU - Winkelhardt, Dirk
AU - Schwämmle, Veit
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1021/acs.jproteome.3c00636
DO - 10.1021/acs.jproteome.3c00636
M3 - Article
C2 - 38038272
SN - 1535-3893
VL - 23
SP - 418
EP - 429
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 1
ER -