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Publikation

Martens, M.; Stierum, R.; Schymanski, E. L.; Evelo, C. T.; Aalizadeh, R.; Aladjov, H.; Arturi, K.; Audouze, K.; Babica, P.; Berka, K.; Bessems, J.; Blaha, L.; Bolton, E. E.; Cases, M.; Damalas, D. ?.; Dave, K.; Dilger, M.; Exner, T.; Geerke, D. P.; Grafström, R.; Gray, A.; Hancock, J. M.; Hollert, H.; Jeliazkova, N.; Jennen, D.; Jourdan, F.; Kahlem, P.; Klanova, J.; Kleinjans, J.; Kondić, T.; Kone, B.; Lynch, I.; Maran, U.; Martinez Cuesta, S.; Ménager, H.; Neumann, S.; Nymark, P.; Oberacher, H.; Ramirez, N.; Remy, S.; Rocca-Serra, P.; Salek, R. M.; Sallach, B.; Sansone, S.-A.; Sanz, F.; Sarimveis, H.; Sarntivijai, S.; Schulze, T.; Slobodnik, J.; Spjuth, O.; Tedds, J.; Thomaidis, N.; Weber, R. J.; van Westen, G. J.; Wheelock, C. E.; Williams, A. J.; Witters, H.; Zdrazil, B.; Županič, A.; Willighagen, E. L.; ELIXIR and Toxicology: a community in development F1000Research 10, 1129, (2023) DOI: 10.12688/f1000research.74502.2

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

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 Metabolomics Anal. Chem. 91, 3302-3310, (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 metabolomics Bioinformatics 35, 3752-3760, (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.
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 cloud GigaScience 8, giy149, (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.
Preprints

Peters, K.; Bradbury, J.; Bergmann, S.; Capuccini, M.; Cascante, M.; de Atauri, P.; Ebbels, T. M. D.; Foguet, C.; Glen, R.; Gonzalez-Beltran, A.; Guenther, U.; 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.; Pireddu, L.; Piras, M. E.; Reed, M. A. C.; Rocca-Serra, P.; Roger, P.; Rosato, A.; Rueedi, R.; Ruttkies, C.; Sadawi, N.; Salek, R.; 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 Cloud bioRxiv (2018) DOI: 10.1101/409151

Background Metabolomics 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.Findings The PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which 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.Conclusions PhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.
Preprints

Moreno, P.; Pireddu, L.; Roger, P.; Goonasekera, N.; Afgan, E.; van den Beek, M.; He, S.; Larsson, A.; Ruttkies, C.; Schober, D.; Johnson, D.; Rocca-Serra, P.; Weber, R. J. M.; Gruening, B.; Salek, R.; Kale, N.; Perez-Riverol, Y.; Papatheodorou, I.; Spjuth, O.; Neumann, S.; Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud bioRxiv (2018) DOI: 10.1101/488643

Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator. Availability: All code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment.
Publikation

Schober, D.; Jacob, D.; Wilson, M.; Cruz, J. A.; Marcu, A.; Grant, J. R.; Moing, A.; Deborde, C.; de Figueiredo, L. F.; Haug, K.; Rocca-Serra, P.; Easton, J.; Ebbels, T. M. D.; Hao, J.; Ludwig, C.; Günther, U. L.; Rosato, A.; Klein, M. S.; Lewis, I. A.; Luchinat, C.; Jones, A. R.; Grauslys, A.; Larralde, M.; Yokochi, M.; Kobayashi, N.; Porzel, A.; Griffin, J. L.; Viant, M. R.; Wishart, D. S.; Steinbeck, C.; Salek, R. M.; Neumann, S.; nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data Anal. Chem. 90, 649-656, (2018) DOI: 10.1021/acs.analchem.7b02795

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.
Preprints

Emami Khoonsari, P.; Moreno, P.; Bergmann, S.; Burman, J.; Capuccini, M.; Carone, M.; Cascante, M.; de Atauri, P.; Foguet, C.; Gonzalez-Beltran, A.; 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.; 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 Metabolomics bioRxiv (2017) DOI: 10.1101/213603

Developing 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 in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.
Publikation

van Rijswijk, M.; Beirnaert, C.; Caron, C.; Cascante, M.; Dominguez, V.; Dunn, W. B.; Ebbels, T. M. D.; Giacomoni, F.; Gonzalez-Beltran, A.; Hankemeier, T.; Haug, K.; Izquierdo-Garcia, J. L.; Jimenez, R. C.; Jourdan, F.; Kale, N.; Klapa, M. I.; Kohlbacher, O.; Koort, K.; Kultima, K.; Le Corguillé, G.; Moreno, P.; Moschonas, N. K.; Neumann, S.; O’Donovan, C.; Reczko, M.; Rocca-Serra, P.; Rosato, A.; Salek, R. M.; Sansone, S.-A.; Satagopam, V.; Schober, D.; Shimmo, R.; Spicer, R. A.; Spjuth, O.; Thévenot, E. A.; Viant, M. R.; Weber, R. J. M.; Willighagen, E. L.; Zanetti, G.; Steinbeck, C.; The future of metabolomics in ELIXIR F1000Research 6, 1649, (2017) DOI: 10.12688/f1000research.12342.2

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
Publikation

Bandrowski, A.; Brinkman, R.; Brochhausen, M.; Brush, M. H.; Bug, B.; Chibucos, M. C.; Clancy, K.; Courtot, M.; Derom, D.; Dumontier, M.; Fan, L.; Fostel, J.; Fragoso, G.; Gibson, F.; Gonzalez-Beltran, A.; Haendel, M. A.; He, Y.; Heiskanen, M.; Hernandez-Boussard, T.; Jensen, M.; Lin, Y.; Lister, A. L.; Lord, P.; Malone, J.; Manduchi, E.; McGee, M.; Morrison, N.; Overton, J. A.; Parkinson, H.; Peters, B.; Rocca-Serra, P.; Ruttenberg, A.; Sansone, S.-A.; Scheuermann, R. H.; Schober, D.; Smith, B.; Soldatova, L. N.; Stoeckert, C. J.; Taylor, C. F.; Torniai, C.; Turner, J. A.; Vita, R.; Whetzel, P. L.; Zheng, J.; The Ontology for Biomedical Investigations PLOS ONE 11, e0154556, (2016) DOI: 10.1371/journal.pone.0154556

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.
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