WOMBAT-P : Benchmarking Label-Free Proteomics Data Analysis Workflows
(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.
(Less)
- author
- organization
- publishing date
- 2024
- 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
- 2024-09-27 22:35:40
@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}}, }