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MetaboT: An LLM-based Multi-Agent Frameworkfor Interactive Analysis of Mass SpectrometryMetabolomics Knowledge Graphs
Authors:
Madina Bekbergenova,
Lucas Pradi,
Benjamin Navet,
Emma Tysinger,
Franck Michel,
Matthieu Feraud,
Yousouf Taghzouti,
Yan Zhou Chen,
Olivier Kirchhoffer,
Florence Mehl,
Martin Legrand,
Tao Jiang,
Marco Pagni,
Soha Hassoun,
Jean-Luc Wolfender,
Wout Bittremieux,
Fabien Gandon,
Louis-Félix Nothias
Abstract:
Mass spectrometry-based metabolomics generates complex, high-dimensional data that holds vast potential for biological discovery but remains difficult to integrate and interpret. Knowledge graphs (KGs) unify this heterogeneous information by representing spectra, annotations, taxa, chemical classes, and biological activities as a single interoperable network; however, their practical use is limite…
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Mass spectrometry-based metabolomics generates complex, high-dimensional data that holds vast potential for biological discovery but remains difficult to integrate and interpret. Knowledge graphs (KGs) unify this heterogeneous information by representing spectra, annotations, taxa, chemical classes, and biological activities as a single interoperable network; however, their practical use is limited by the steep learning curve of corresponding specialized representation and query languages. Here we introduce MetaboT, an open-source multi-agent Large Language Model (LLM) framework that translates natural-language questions into executable SPARQL queries over metabolomics knowledge graphs. MetaboT mitigates the hallucination and schema-compliance limitations of single-model approaches through a modular architecture in which specialised agents handle scope validation, entity resolution against authoritative resources, schema-aware query generation, iterative refinement, and result interpretation. We validated MetaboT on the Experimental Natural Products Knowledge Graph (ENPKG), using an expert-authored benchmark of natural-language questions paired with reference SPARQL queries, and demonstrate its ability to answer complex questions about plant--metabolite relationships and biological activities. MetaboT lowers the technical barrier for metabolomics researchers and enables semantic data mining without specialised programming expertise.
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Submitted 27 May, 2026; v1 submitted 2 October, 2025;
originally announced October 2025.
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Foundation model for mass spectrometry proteomics
Authors:
Justin Sanders,
Melih Yilmaz,
Jacob H. Russell,
Wout Bittremieux,
William E. Fondrie,
Nicholas M. Riley,
Sewoong Oh,
William Stafford Noble
Abstract:
Mass spectrometry is the dominant technology in the field of proteomics, enabling high-throughput analysis of the protein content of complex biological samples. Due to the complexity of the instrumentation and resulting data, sophisticated computational methods are required for the processing and interpretation of acquired mass spectra. Machine learning has shown great promise to improve the analy…
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Mass spectrometry is the dominant technology in the field of proteomics, enabling high-throughput analysis of the protein content of complex biological samples. Due to the complexity of the instrumentation and resulting data, sophisticated computational methods are required for the processing and interpretation of acquired mass spectra. Machine learning has shown great promise to improve the analysis of mass spectrometry data, with numerous purpose-built methods for improving specific steps in the data acquisition and analysis pipeline reaching widespread adoption. Here, we propose unifying various spectrum prediction tasks under a single foundation model for mass spectra. To this end, we pre-train a spectrum encoder using de novo sequencing as a pre-training task. We then show that using these pre-trained spectrum representations improves our performance on the four downstream tasks of spectrum quality prediction, chimericity prediction, phosphorylation prediction, and glycosylation status prediction. Finally, we perform multi-task fine-tuning and find that this approach improves the performance on each task individually. Overall, our work demonstrates that a foundation model for tandem mass spectrometry proteomics trained on de novo sequencing learns generalizable representations of spectra, improves performance on downstream tasks where training data is limited, and can ultimately enhance data acquisition and analysis in proteomics experiments.
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Submitted 18 May, 2025; v1 submitted 16 May, 2025;
originally announced May 2025.
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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
Authors:
Yingying Sun,
Jun A,
Zhiwei Liu,
Rui Sun,
Liujia Qian,
Samuel H. Payne,
Wout Bittremieux,
Markus Ralser,
Chen Li,
Yi Chen,
Zhen Dong,
Yasset Perez-Riverol,
Asif Khan,
Chris Sander,
Ruedi Aebersold,
Juan Antonio Vizcaíno,
Jonathan R Krieger,
Jianhua Yao,
Han Wen,
Linfeng Zhang,
Yunping Zhu,
Yue Xuan,
Benjamin Boyang Sun,
Liang Qiao,
Henning Hermjakob
, et al. (37 additional authors not shown)
Abstract:
Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights.…
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Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.
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Submitted 21 February, 2025;
originally announced February 2025.
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MassSpecGym: A benchmark for the discovery and identification of molecules
Authors:
Roman Bushuiev,
Anton Bushuiev,
Niek F. de Jonge,
Adamo Young,
Fleming Kretschmer,
Raman Samusevich,
Janne Heirman,
Fei Wang,
Luke Zhang,
Kai Dührkop,
Marcus Ludwig,
Nils A. Haupt,
Apurva Kalia,
Corinna Brungs,
Robin Schmid,
Russell Greiner,
Bo Wang,
David S. Wishart,
Li-Ping Liu,
Juho Rousu,
Wout Bittremieux,
Hannes Rost,
Tytus D. Mak,
Soha Hassoun,
Florian Huber
, et al. (5 additional authors not shown)
Abstract:
The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a resu…
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The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://github.com/pluskal-lab/MassSpecGym.
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Submitted 14 February, 2025; v1 submitted 30 October, 2024;
originally announced October 2024.
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RapidOMS: FPGA-based Open Modification Spectral Library Searching with HD Computing
Authors:
Sumukh Pinge,
Weihong Xu,
Wout Bittremieux,
Niema Moshiri,
Sang-Woo Jun,
Tajana Rosing
Abstract:
Mass spectrometry (MS) is essential for protein analysis but faces significant challenges with large datasets and complex post-translational modifications, resulting in difficulties in spectral identification. Open Modification Search (OMS) improves the analysis of these modifications. We present RapidOMS, a solution leveraging the Samsung SmartSSD, which integrates SSD and FPGA in a near-storage…
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Mass spectrometry (MS) is essential for protein analysis but faces significant challenges with large datasets and complex post-translational modifications, resulting in difficulties in spectral identification. Open Modification Search (OMS) improves the analysis of these modifications. We present RapidOMS, a solution leveraging the Samsung SmartSSD, which integrates SSD and FPGA in a near-storage configuration to minimize data movement and enhance the efficiency of large-scale database searching. RapidOMS employs hyperdimensional computing (HDC), a brain-inspired, high-dimensional data processing approach, exploiting the parallel processing and low-latency capabilities of FPGAs, making it well-suited for MS. Utilizing the parallelism and efficiency of bitwise operations in HDC, RapidOMS delivers up to a 60x speedup over the state-of-the-art (SOTA) CPU tool ANN-Solo and is 2.72x faster than the GPU tool HyperOMS. Furthermore, RapidOMS achieves an 11x improvement in energy efficiency compared to conventional systems, providing scalable, energy-efficient solutions for large-scale proteomics applications and advancing the efficient processing of proteomic data.
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Submitted 20 September, 2024;
originally announced September 2024.
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SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering
Authors:
Sumukh Pinge,
Weihong Xu,
Jaeyoung Kang,
Tianqi Zhang,
Neima Moshiri,
Wout Bittremieux,
Tajana Rosing
Abstract:
Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology has vast applications in biotechnology and biomedicine but faces significant computational bottlenecks. Current methodologies often require multiple hours or even days to process extensive datasets, particularly in the d…
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Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology has vast applications in biotechnology and biomedicine but faces significant computational bottlenecks. Current methodologies often require multiple hours or even days to process extensive datasets, particularly in the domain of spectral clustering. To tackle these inefficiencies, we introduce SpecHD, a hyperdimensional computing (HDC) framework supplemented by an FPGA-accelerated architecture with integrated near-storage preprocessing. Utilizing streamlined binary operations in an HDC environment, SpecHD capitalizes on the low-latency and parallel capabilities of FPGAs. This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications. Our evaluations demonstrate that SpecHD not only maintains but often surpasses existing clustering quality metrics while drastically cutting computational time. Specifically, it can cluster a large-scale human proteome dataset-comprising 25 million MS/MS spectra and 131 GB of MS data-in just 5 minutes. With energy efficiency exceeding 31x and a speedup factor that spans a range of 6x to 54x over existing state of-the-art solutions, SpecHD emerges as a promising solution for the rapid analysis of mass spectrometry data with great implications for personalized healthcare.
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Submitted 20 November, 2023;
originally announced November 2023.
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Massively Parallel Open Modification Spectral Library Searching with Hyperdimensional Computing
Authors:
Jaeyoung Kang,
Weihong Xu,
Wout Bittremieux,
Tajana Rosing
Abstract:
Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain unidentified or mismatched due to unexpected post-translational modifications. Open modification search (OMS) has been proposed as a strategy to improve the identification rate by considering every possible change in sp…
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Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain unidentified or mismatched due to unexpected post-translational modifications. Open modification search (OMS) has been proposed as a strategy to improve the identification rate by considering every possible change in spectra, but it expands the search space exponentially. In this work, we propose HyperOMS, which redesigns OMS based on hyperdimensional computing to cope with such challenges. Unlike existing algorithms that represent spectral data with floating point numbers, HyperOMS encodes them with high dimensional binary vectors and performs the efficient OMS in high-dimensional space. With the massive parallelism and simple boolean operations, HyperOMS can be efficiently handled on parallel computing platforms. Experimental results show that HyperOMS on GPU is up to $17\times$ faster and $6.4\times$ more energy efficient than the state-of-the-art GPU-based OMS tool while providing comparable search quality to competing search tools.
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Submitted 31 December, 2022; v1 submitted 15 November, 2022;
originally announced November 2022.