TY - JOUR A1 - Mildau, Kevin A1 - Ehlers, Henry A1 - Meisenburg, Mara A1 - Del Pup, Elena A1 - Koetsier, Robert A. A1 - Torres Ortega, Laura Rosina A1 - de Jonge, Niek F. A1 - Singh, Kumar Saurabh A1 - Ferreira, Dora A1 - Othibeng, Kgalaletso A1 - Tugizimana, Fidele A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - Effective data visualization strategies in untargeted metabolomics JF - Natural Product Reports N2 - LC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results. KW - Metabolomik KW - Visualisierung Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-47596 SN - 0265-0568 PB - Royal Society of Chemistry 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 - 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 -