TIMS2Rescore : a data dependent acquisition-parallel accumulation and serial fragmentation-optimized data-driven rescoring pipeline based on MS2Rescore
- Author
- Arthur Declercq (UGent) , Robbe Devreese (UGent) , Jonas Scheid, Caroline Jachmann (UGent) , Tim Van Den Bossche (UGent) , Annica Preikschat, David Gomez-Zepeda, Jeewan Babu Rijal, Aurelie Hirschler, Jonathan R. Krieger, Tharan Srikumar, George Rosenberger, Claudia Martelli, Dennis Trede, Christine Carapito, Stefan Tenzer, Juliane S. Walz, Sven Degroeve (UGent) , Robbin Bouwmeester (UGent) , Lennart Martens (UGent) and Ralf Gabriels (UGent)
- Organization
- Project
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- Improved data-driven bioinformatics tools to greatly extend neo- and xeno-epitope landscapes detected by immunopeptidomics.
- A diaPASEF powered computational deep dive into the myotonic dystrophy type 1 liquid biopsy proteome
- A novel era in proteomics-based protein PTM discovery through deep learning approaches
- Putting the AI in DIA - building an ML-driven multi-modification engine
- Tackling the big metaproteomics identification challenge in hypercomplex application domains
- Advanced AI-driven Bioinformatics Solutions for Performant Peptidomics
- ProteinContour: proteome-scale unraveling of the relation between post-translational modifications, biophysical properties, interactions and sub-cellular location of proteins.
- Immunopeptidomics-based Development of Next-Generation Bacterial mRNA Vaccines
- Proteomics-derived epitopes for dramatically improved anticancer and antibacterial vaccine development
- Novel knowledge from public life sciences data
- Abstract
- The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields, including plasma proteomics, immunopeptidomics, and metaproteomics, must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can mitigate these issues by acquiring more discerning information at higher sensitivity levels. This is exemplified by the incorporation of ion mobility and parallel accumulation and serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data and search results from ProteoScape and many other search engines, including Sage and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.
- Keywords
- proteomics, mass spectrometry, DDA-PASEF, timsTOF, machine learning, rescoring, peptide identification
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JQRG2H3FTKQN4SC5NZGT3E2N
- MLA
- Declercq, Arthur, et al. “TIMS2Rescore : A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore.” JOURNAL OF PROTEOME RESEARCH, vol. 24, no. 3, 2025, pp. 1067–76, doi:10.1021/acs.jproteome.4c00609.
- APA
- Declercq, A., Devreese, R., Scheid, J., Jachmann, C., Van Den Bossche, T., Preikschat, A., … Gabriels, R. (2025). TIMS2Rescore : a data dependent acquisition-parallel accumulation and serial fragmentation-optimized data-driven rescoring pipeline based on MS2Rescore. JOURNAL OF PROTEOME RESEARCH, 24(3), 1067–1076. https://doi.org/10.1021/acs.jproteome.4c00609
- Chicago author-date
- Declercq, Arthur, Robbe Devreese, Jonas Scheid, Caroline Jachmann, Tim Van Den Bossche, Annica Preikschat, David Gomez-Zepeda, et al. 2025. “TIMS2Rescore : A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore.” JOURNAL OF PROTEOME RESEARCH 24 (3): 1067–76. https://doi.org/10.1021/acs.jproteome.4c00609.
- Chicago author-date (all authors)
- Declercq, Arthur, Robbe Devreese, Jonas Scheid, Caroline Jachmann, Tim Van Den Bossche, Annica Preikschat, David Gomez-Zepeda, Jeewan Babu Rijal, Aurelie Hirschler, Jonathan R. Krieger, Tharan Srikumar, George Rosenberger, Claudia Martelli, Dennis Trede, Christine Carapito, Stefan Tenzer, Juliane S. Walz, Sven Degroeve, Robbin Bouwmeester, Lennart Martens, and Ralf Gabriels. 2025. “TIMS2Rescore : A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore.” JOURNAL OF PROTEOME RESEARCH 24 (3): 1067–1076. doi:10.1021/acs.jproteome.4c00609.
- Vancouver
- 1.Declercq A, Devreese R, Scheid J, Jachmann C, Van Den Bossche T, Preikschat A, et al. TIMS2Rescore : a data dependent acquisition-parallel accumulation and serial fragmentation-optimized data-driven rescoring pipeline based on MS2Rescore. JOURNAL OF PROTEOME RESEARCH. 2025;24(3):1067–76.
- IEEE
- [1]A. Declercq et al., “TIMS2Rescore : a data dependent acquisition-parallel accumulation and serial fragmentation-optimized data-driven rescoring pipeline based on MS2Rescore,” JOURNAL OF PROTEOME RESEARCH, vol. 24, no. 3, pp. 1067–1076, 2025.
@article{01JQRG2H3FTKQN4SC5NZGT3E2N,
abstract = {{The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields, including plasma proteomics, immunopeptidomics, and metaproteomics, must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can mitigate these issues by acquiring more discerning information at higher sensitivity levels. This is exemplified by the incorporation of ion mobility and parallel accumulation and serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data and search results from ProteoScape and many other search engines, including Sage and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.}},
author = {{Declercq, Arthur and Devreese, Robbe and Scheid, Jonas and Jachmann, Caroline and Van Den Bossche, Tim and Preikschat, Annica and Gomez-Zepeda, David and Rijal, Jeewan Babu and Hirschler, Aurelie and Krieger, Jonathan R. and Srikumar, Tharan and Rosenberger, George and Martelli, Claudia and Trede, Dennis and Carapito, Christine and Tenzer, Stefan and Walz, Juliane S. and Degroeve, Sven and Bouwmeester, Robbin and Martens, Lennart and Gabriels, Ralf}},
issn = {{1535-3893}},
journal = {{JOURNAL OF PROTEOME RESEARCH}},
keywords = {{proteomics,mass spectrometry,DDA-PASEF,timsTOF,machine learning,rescoring,peptide identification}},
language = {{eng}},
number = {{3}},
pages = {{1067--1076}},
title = {{TIMS2Rescore : a data dependent acquisition-parallel accumulation and serial fragmentation-optimized data-driven rescoring pipeline based on MS2Rescore}},
url = {{http://doi.org/10.1021/acs.jproteome.4c00609}},
volume = {{24}},
year = {{2025}},
}
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