• Rustims: An Open-Source Framew...

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

Title:
Rustims: An Open-Source Framework for Rapid Development and Processing of timsTOF Data-Dependent Acquisition Data.
Authors:
Teschner D; Institute of Computer Science, Johannes-Gutenberg University, 55128 Mainz, Germany.; Institute for Quantitative and Computer Biosciences (IQCB), Johannes-Gutenberg University, 55128 Mainz, Germany., Gomez-Zepeda D; Helmholtz Institute for Translational Oncology (HI-TRON) Mainz - a Helmholtz Institute of the DKFZ, 55131 Mainz, Germany.; German Cancer Research Center, DKFZ, 69120 Heidelberg, Germany., Łącki MK; University Medical Center, Johannes-Gutenberg University, 55131 Mainz, Germany., Kemmer T; Institute of Computer Science, Johannes-Gutenberg University, 55128 Mainz, Germany.; Institute for Quantitative and Computer Biosciences (IQCB), Johannes-Gutenberg University, 55128 Mainz, Germany., Busch A; Institute of Computer Science, Johannes-Gutenberg University, 55128 Mainz, Germany.; Institute for Quantitative and Computer Biosciences (IQCB), Johannes-Gutenberg University, 55128 Mainz, Germany., Tenzer S; Helmholtz Institute for Translational Oncology (HI-TRON) Mainz - a Helmholtz Institute of the DKFZ, 55131 Mainz, Germany.; German Cancer Research Center, DKFZ, 69120 Heidelberg, Germany.; University Medical Center, Johannes-Gutenberg University, 55131 Mainz, Germany., Hildebrandt A; Institute of Computer Science, Johannes-Gutenberg University, 55128 Mainz, Germany.; Institute for Quantitative and Computer Biosciences (IQCB), Johannes-Gutenberg University, 55128 Mainz, Germany.
Source:
Journal of proteome research [J Proteome Res] 2025 May 02; Vol. 24 (5), pp. 2358-2368. Date of Electronic Publication: 2025 Apr 22.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Chemical Society Country of Publication: United States NLM ID: 101128775 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1535-3907 (Electronic) Linking ISSN: 15353893 NLM ISO Abbreviation: J Proteome Res Subsets: MEDLINE
Imprint Name(s):
Original Publication: Washington, D.C. : American Chemical Society, c2002-
MeSH Terms:
Software* , Proteomics*/methods , Mass Spectrometry*/methods, Humans
References:
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Contributed Indexing:
Keywords: DDA-PASEF; Python; framework; ion mobility; mass spectrometry; open-source; proteomics; rust-lang; timsTOF
Entry Date(s):
Date Created: 20250422 Date Completed: 20250502 Latest Revision: 20250508
Update Code:
20250508
PubMed Central ID:
PMC12053931
DOI:
10.1021/acs.jproteome.4c00966
PMID:
40260647
Database:
MEDLINE

Weitere Informationen

Mass spectrometry is essential for analyzing and quantifying biological samples. The timsTOF platform is a prominent commercial tool for this purpose, particularly in bottom-up acquisition scenarios. The additional ion mobility dimension requires more complex data processing, yet most current software solutions for timsTOF raw data are proprietary or closed-source, limiting integration into custom workflows. We introduce rustims, a framework implementing a flexible toolbox designed for processing timsTOF raw data, currently focusing on data-dependent acquisition (DDA-PASEF). The framework employs a dual-language approach, combining efficient, multithreaded Rust code with an easy-to-use Python interface. This allows for implementations that are fast, intuitive, and easy to integrate. With imspy as its main Python scripting interface and sagepy for Sage search engine bindings, rustims enables fast, integrable, and intuitive processing. We demonstrate its capabilities with a pipeline for DDA-PASEF data including rescoring and integration of third-party tools like the Prosit intensity predictor and an extended ion mobility model. This pipeline supports tryptic proteomics and nontryptic immunopeptidomics data, with benchmark comparisons to FragPipe and PEAKS. Rustims is available on GitHub under the MIT license, with installation packages for multiple platforms on PyPi and all analysis scripts accessible via Zenodo.