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Rustims: An Open-Source Framework for Rapid Development and Processing of timsTOF Data-Dependent Acquisition Data.

David Teschner1,2, David Gomez-Zepeda3,4, Mateusz K Łącki5

  • 1Institute of Computer Science, Johannes-Gutenberg University, 55128 Mainz, Germany.

Journal of Proteome Research
|April 22, 2025
PubMed
Summary
This summary is machine-generated.

We developed rustims, a new open-source software framework for processing timsTOF mass spectrometry data. It offers a fast, integrable, and intuitive toolbox for analyzing complex biological samples.

Keywords:
DDA-PASEFPythonframeworkion mobilitymass spectrometryopen-sourceproteomicsrust-langtimsTOF

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Area of Science:

  • Proteomics and Metabolomics
  • Computational Biology and Bioinformatics

Background:

  • Mass spectrometry, particularly the timsTOF platform, is crucial for biological sample analysis.
  • Processing timsTOF data is complex due to the added ion mobility dimension.
  • Existing software for timsTOF data is often proprietary, hindering custom workflow integration.

Purpose of the Study:

  • To introduce rustims, a flexible, open-source framework for processing timsTOF raw data.
  • To provide a fast, intuitive, and integrable solution for timsTOF data analysis, focusing on DDA-PASEF.
  • To enable seamless integration of third-party tools and custom analysis pipelines.

Main Methods:

  • Developed rustims, a dual-language framework combining Rust and Python.
  • Utilized imspy for Python scripting and sagepy for Sage search engine bindings.
  • Implemented a DDA-PASEF data processing pipeline including rescoring and integration of Prosit and an extended ion mobility model.

Main Results:

  • Rustims provides efficient, multithreaded data processing via Rust and an easy-to-use Python interface.
  • The framework successfully processed tryptic proteomics and nontryptic immunopeptidomics data.
  • Benchmark comparisons demonstrated competitive performance against established software like FragPipe and PEAKS.

Conclusions:

  • Rustims offers a powerful, open-source alternative for timsTOF data analysis.
  • Its flexible design facilitates integration into diverse custom bioinformatics workflows.
  • The framework supports advanced analyses and is readily accessible for the scientific community.