TY - CHAP A1 - Gaudry, Arnaud A1 - Huber, Florian A1 - Flückiger, Julien A1 - Quirós, L A1 - Rutz, Adriano A1 - Kaiser, M A1 - Grondin, A A1 - Marcourt, Laurence A1 - Ferreira Queiroz, E A1 - Wolfender, Jean-Luc A1 - Allard, Pierre-Marie T1 - Short Lecture “Mass spectrometry-based sample vectorization for exploration of large chemodiverse datasets and efficient identification of new antiparasitic compounds” T2 - Planta Medica KW - Massenspektrometrie KW - Naturstoffchemie KW - Antiparasitäres Mittel KW - Arzneimittelforschung Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1758983 SN - 1439-0221 VL - 88 IS - 15 PB - Thieme ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Mildau, Kevin A1 - Meijer, David A1 - Louwen, Joris J. R. A1 - Bueschl, Christoph A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools JF - Metabolomics N2 - Background Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. Aim of review We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. Key scientific concepts of review This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks. KW - Metabolomik KW - Massenspektrometrie KW - Maschinelles Lernen KW - Benchmarking KW - Mass fragmentation spectra Y1 - 2022 U6 - https://doi.org/10.1007/s11306-022-01963-y SN - 1573-3890 VL - 18 IS - 12 PB - Springer Nature ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Louwen, Joris J. R. A1 - Chekmeneva, Elena A1 - Camuzeaux, Stephane A1 - Vermeir, Femke J. A1 - Jansen, Robert S. A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - MS2Query: reliable and scalable MS2 mass spectra-based analogue search JF - Nature Communications N2 - Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology. KW - Metabolomik KW - Massenspektrometrie KW - Maschinelles Lernen KW - Reproduzierbarkeit KW - Metabolomics KW - Mass Spectrometry KW - Machine Learning KW - Complex Mixtures Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46105 SN - 2041-1723 VL - 14 PB - Springer ER - TY - INPR A1 - de Jonge, Niek F. A1 - Joas, David A1 - Truong, Lem-Joe A1 - van der Hooft, Justin J.J. A1 - Huber, Florian T1 - Reliable cross-ion mode chemical similarity prediction between MS2 spectra T2 - biorxiv N2 - Mass spectrometry is commonly used to characterize metabolites in untargeted metabolomics. This can be done in positive and negative ionization mode, a choice typically guided by the fraction of metabolites a researcher is interested in. During analysis, mass spectral comparisons are widely used to enable annotation through reference libraries and to facilitate data organization through networking. However, until now, such comparisons between mass spectra were restricted to mass spectra of the same ionization mode, as the two modes generally result in very distinct fragmentation spectra. To overcome this barrier, here, we have implemented a machine learning model that can predict chemical similarity between spectra of different ionization modes. Hence, our new MS2DeepScore 2.0 model facilitates the seamless integration of positive and negative ionization mode mass spectra into one analysis pipeline. This creates entirely new options for data exploration, such as mass spectral library searching of negative ion mode spectra in positive ion mode libraries or cross-ionization mode molecular networking. Furthermore, to improve the reliability of predictions and better cope with unseen data, we have implemented a method to estimate the quality of prediction. This will help to avoid false predictions on spectra with low information content or spectra that substantially differ from the training data. We anticipate that the MS2DeepScore 2.0 model will extend our current capabilities in organizing and annotating untargeted metabolomics profiles. KW - Massenspektrometrie KW - Metabolomik KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/10.1101/2024.03.25.586580 PB - Cold Spring Harbor Laboratory ER - TY - JOUR A1 - Gaudry, Arnaud A1 - Huber, Florian A1 - Nothias, Louis-Félix A1 - Cretton, Sylvian A1 - Kaiser, Marcel A1 - Wolfender, Jean-Luc A1 - Allard, Pierre-Marie T1 - MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets JF - Frontiers in Bioinformatics N2 - In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation—changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO—MS2 BasEd SaMple VectOrization—a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis. KW - Computational chemistry KW - Massenspektrometrie KW - Metabolomik KW - Naturstoffchemie Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54660 SN - 2673-7647 VL - 2 PB - Frontiers ER - TY - JOUR A1 - Szwarc, Sarah A1 - Rutz, Adriano A1 - Lee, Kyungha A1 - Mejri, Yassine A1 - Bonnet, Olivier A1 - Hazni, Hazrina A1 - Jagora, Adrien A1 - Mbeng Obame, Rany B. A1 - Noh, Jin Kyoung A1 - Otogo N’Nang, Elvis A1 - Alaribe, Stephenie C. A1 - Awang, Khalijah A1 - Bernadat, Guillaume A1 - Choi, Young Hae A1 - Courdavault, Vincent A1 - Frederich, Michel A1 - Gaslonde, Thomas A1 - Huber, Florian A1 - Kam, Toh-Seok A1 - Low, Yun Yee A1 - Poupon, Erwan A1 - van der Hooft, Justin J. J. A1 - Kang, Kyo Bin A1 - Le Pogam, Pierre A1 - Beniddir, Mehdi A. T1 - Translating community-wide spectral library into actionable chemical knowledge: a proof of concept with monoterpene indole alkaloids JF - Journal of Cheminformatics N2 - With over 3000 representatives, the monoterpene indole alkaloids (MIAs) class is among the most diverse families of plant natural products. The MS/MS spectral space exploration of these complex compounds using chemoinformatic and computational mass spectrometry tools offers a valuable opportunity to extract and share chemical insights from this emblematic family of natural products (NPs). In this work, we first present a substantially updated version of the MIADB, a database now containing 422 MS/MS spectra of MIAs that has been uploaded to the GNPS library versus 172 initial entries. We then introduce an innovative workflow that leverages hundreds of fragmentation spectra to support the FAIRification, extraction and dissemination of chemical knowledge. This workflow aims at the extraction of spectral patterns matching finely defined MIA skeletons. These extracted signatures can then be queried against complex biological extract datasets using MassQL. By applying this strategy to an LC-MS/MS dataset of 75 plant extracts, our results demonstrated the efficiency of this approach in identifying the diversity of MIA skeletons present in the analyzed samples. Additionally, our work enabled the digitization of structural data for diverse MIA skeletons by converting them into machine-readable formats and thereby enhancing their dissemination for the scientific community. Scientific contribution A comprehensive investigation of the monoterpene indole alkaloid chemical space, aiming to highlight skeleton-dependent fragmentation similarity trends and to generate valuable spectrometric signatures that could be used as queries. KW - Computational chemistry KW - Massenspektrometrie KW - Naturstoffchemie Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54607 SN - 1758-2946 VL - 17 IS - 1 PB - Springer Nature ER - TY - INPR A1 - Bushuiev, Roman A1 - Bushuiev, Anton A1 - de Jonge, Niek F. A1 - Young, Adamo A1 - Kretschmer, Fleming A1 - Samusevich, Raman A1 - Heirman, Janne A1 - Wang, Fei A1 - Zhang, Luke A1 - Dührkop, Kai A1 - Ludwig, Marcus A1 - Haupt, Nils A. A1 - Kalia, Apurva A1 - Brungs, Corinna A1 - Schmid, Robin A1 - Greiner, Russell A1 - Wang, Bo A1 - Wishart, David S. A1 - Liu, Li-Ping A1 - Rousu, Juho A1 - Bittremieux, Wout A1 - Röst, Hannes A1 - Mak, Tytus D. A1 - Hassoun, Soha A1 - Huber, Florian A1 - van der Hooft, Justin J.J. A1 - Stravs, Michael A. A1 - Böcker, Sebastian A1 - Sivic, Josef A1 - Pluskal, Tomáš T1 - MassSpecGym: A benchmark for the discovery and identification of molecules T2 - arXiv N2 - 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 this https URL [https://github.com/pluskal-lab/MassSpecGym]. KW - Maschinelles Lernen KW - Benchmark KW - Computational chemistry KW - Massenspektrometrie KW - Molekülstruktur Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2410.23326 N1 - Open Review: https://web.archive.org/web/20250521010648/https://openreview.net/forum?id=AAo8zAShX3#discussion PB - arXiv ET - v3 ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Hecht, Helge A1 - Strobel, Michael A1 - Wang, Mingxun A1 - van der Hooft, Justin J. J. A1 - Huber, Florian T1 - Reproducible MS/MS library cleaning pipeline in matchms JF - Journal of Cheminformatics N2 - Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries. KW - Massenspektrometrie KW - Metabolomik KW - Metadaten KW - Python (Programmiersprache) KW - Library cleaning KW - HSD Publikationsfonds KW - DFG Publikationskosten Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-46491 SN - 1758-2946 VL - 16 IS - 1 PB - Springer Nature ER -