Unser 10. Leibniz Plant Biochemistry Symposium am 7. und 8. Mai war ein großer Erfolg. Thematisch ging es in diesem Jahr um neue Methoden und Forschungsansätze der Naturstoffchemie. Die exzellenten Vorträge über Wirkstoffe…
Omanische Heilpflanze im Fokus der Phytochemie IPB-Wissenschaftler und Partner aus Dhofar haben jüngst die omanische Heilpflanze Terminalia dhofarica unter die phytochemische Lupe genommen. Die Pflanze ist reich an…
Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology, and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
Publikation
Peters, K.; Bradbury, J.; Bergmann, S.; Capuccini, M.; Cascante, M.; de Atauri, P.; Ebbels, T. M. D.; Foguet, C.; Glen, R.; Gonzalez-Beltran, A.; Günther, U. L.; Handakas, E.; Hankemeier, T.; Haug, K.; Herman, S.; Holub, P.; Izzo, M.; Jacob, D.; Johnson, D.; Jourdan, F.; Kale, N.; Karaman, I.; Khalili, B.; Emami Khoonsari, P.; Kultima, K.; Lampa, S.; Larsson, A.; Ludwig, C.; Moreno, P.; Neumann, S.; Novella, J. A.; O'Donovan, C.; Pearce, J. T. M.; Peluso, A.; Piras, M. E.; Pireddu, L.; Reed, M. A. C.; Rocca-Serra, P.; Roger, P.; Rosato, A.; Rueedi, R.; Ruttkies, C.; Sadawi, N.; Salek, R. M.; Sansone, S.-A.; Selivanov, V.; Spjuth, O.; Schober, D.; Thévenot, E. A.; Tomasoni, M.; van Rijswijk, M.; van Vliet, M.; Viant, M. R.; Weber, R. J. M.; Zanetti, G.; Steinbeck, C.;PhenoMeNal: processing and analysis of metabolomics data in the cloudGigaScience8giy149(2019)DOI: 10.1093/gigascience/giy149
BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution.FindingsPhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm.ConclusionsPhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.
Publikation
Hoffmann, N.; Rein, J.; Sachsenberg, T.; Hartler, J.; Haug, K.; Mayer, G.; Alka, O.; Dayalan, S.; Pearce, J. T. M.; Rocca-Serra, P.; Qi, D.; Eisenacher, M.; Perez-Riverol, Y.; Vizcaíno, J. A.; Salek, R. M.; Neumann, S.; Jones, A. R.;mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry MetabolomicsAnal. Chem.913302-3310(2019)DOI: 10.1021/acs.analchem.8b04310
Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.
Publikation
Emami Khoonsari, P.; Moreno, P.; Bergmann, S.; Burman, J.; Capuccini, M.; Carone, M.; Cascante, M.; de Atauri, P.; Foguet, C.; Gonzalez-Beltran, A. N.; Hankemeier, T.; Haug, K.; He, S.; Herman, S.; Johnson, D.; Kale, N.; Larsson, A.; Neumann, S.; Peters, K.; Pireddu, L.; Rocca-Serra, P.; Roger, P.; Rueedi, R.; Ruttkies, C.; Sadawi, N.; Salek, R. M.; Sansone, S.-A.; Schober, D.; Selivanov, V.; Thévenot, E. A.; van Vliet, M.; Zanetti, G.; Steinbeck, C.; Kultima, K.; Spjuth, O.;Interoperable and scalable data analysis with microservices: applications in metabolomicsBioinformatics353752-3760(2019)DOI: 10.1093/bioinformatics/btz160
MotivationDeveloping a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator.ResultsWe developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.Availability and implementationThe PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects.