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

David Bouyssié1,2, Pınar Altıner1, Salvador Capella-Gutierrez3

  • 1Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III─Paul Sabatier (UT3), 31062 Toulouse, France.

Journal of Proteome Research
|December 1, 2023
PubMed
Summary
This summary is machine-generated.

WOMBAT-P offers automated benchmarking for proteomics software, revealing significant differences in quantified proteins across workflows. This platform aids researchers in selecting optimal tools for their specific data analysis needs.

Keywords:
benchmarkingdata analysislabel-free proteomicsquality metricsworkflow

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Proteomics research utilizes diverse software for data analysis, including peptide-spectrum matching, protein inference, quantification, and statistical analysis.
  • The variety of algorithms and approaches leads to challenges in comparing different proteomics software solutions.
  • A need exists for standardized, unbiased methods to evaluate and compare commonly used proteomics workflows.

Purpose of the Study:

  • To introduce WOMBAT-P, a versatile platform for automated benchmarking and comparison of bottom-up label-free proteomics workflows.
  • To simplify the processing of public proteomics data using the sample and data relationship format for proteomics (SDRF-Proteomics).
  • To facilitate efficient comparisons of diverse analytical outputs from annotated local or public ProteomeXchange datasets.

Main Methods:

  • Developed WOMBAT-P, a platform for automated benchmarking of proteomics software.
  • Utilized SDRF-Proteomics for streamlined processing of public and local proteomics data.
  • Evaluated WOMBAT-P using experimental ground truth data and a realistic biological dataset.

Main Results:

  • Uncovered significant disparities and limited overlap in quantified proteins across different proteomics workflows.
  • Demonstrated WOMBAT-P's capability for rapid execution and seamless comparison of workflows.
  • Generated valuable benchmarking metrics to guide researchers in software selection.

Conclusions:

  • WOMBAT-P provides essential insights into the performance of various proteomics software solutions.
  • The platform aids researchers in choosing the most suitable workflow for their specific datasets.
  • The modular design of WOMBAT-P allows for extensibility and customization.