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

Bouyssié, David ; Altıner, Pınar ; Capella-Gutierrez, Salvador ; Fernández, José M. ; Hagemeijer, Yanick Paco ; Horvatovich, Peter LU ; Hubálek, Martin ; Levander, Fredrik LU orcid ; Mauri, Pierluigi and Palmblad, Magnus , et al. (2024) In Journal of Proteome Research 23(1). p.418-429
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
benchmarking, data analysis, label-free proteomics, quality metrics, workflow
in
Journal of Proteome Research
volume
23
issue
1
pages
12 pages
publisher
The American Chemical Society (ACS)
external identifiers
  • pmid:38038272
  • scopus:85179616172
ISSN
1535-3893
DOI
10.1021/acs.jproteome.3c00636
language
English
LU publication?
yes
id
516f1923-615c-4be8-bd02-b11f9e646ebd
date added to LUP
2024-01-11 12:16:24
date last changed
2024-09-14 20:33:00
@article{516f1923-615c-4be8-bd02-b11f9e646ebd,
  abstract     = {{<p>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.</p>}},
  author       = {{Bouyssié, David and Altıner, Pınar and Capella-Gutierrez, Salvador and Fernández, José M. and Hagemeijer, Yanick Paco and Horvatovich, Peter and Hubálek, Martin and Levander, Fredrik and Mauri, Pierluigi and Palmblad, Magnus and Raffelsberger, Wolfgang and Rodríguez-Navas, Laura and Di Silvestre, Dario and Kunkli, Balázs Tibor and Uszkoreit, Julian and Vandenbrouck, Yves and Vizcaíno, Juan Antonio and Winkelhardt, Dirk and Schwämmle, Veit}},
  issn         = {{1535-3893}},
  keywords     = {{benchmarking; data analysis; label-free proteomics; quality metrics; workflow}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{418--429}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Journal of Proteome Research}},
  title        = {{WOMBAT-P : Benchmarking Label-Free Proteomics Data Analysis Workflows}},
  url          = {{http://dx.doi.org/10.1021/acs.jproteome.3c00636}},
  doi          = {{10.1021/acs.jproteome.3c00636}},
  volume       = {{23}},
  year         = {{2024}},
}