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.
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.
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
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.
Publikation
Salek, R. M.; Neumann, S.; Schober, D.; Hummel, J.; Billiau, K.; Kopka, J.; Correa, E.; Reijmers, T.; Rosato, A.; Tenori, L.; Turano, P.; Marin, S.; Deborde, C.; Jacob, D.; Rolin, D.; Dartigues, B.; Conesa, P.; Haug, K.; Rocca-Serra, P.; O’Hagan, S.; Hao, J.; van Vliet, M.; Sysi-Aho, M.; Ludwig, C.; Bouwman, J.; Cascante, M.; Ebbels, T.; Griffin, J. L.; Moing, A.; Nikolski, M.; Oresic, M.; Sansone, S.-A.; Viant, M. R.; Goodacre, R.; Günther, U. L.; Hankemeier, T.; Luchinat, C.; Walther, D.; Steinbeck, C.; Erratum to: COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access Metabolomics 11, 1598-1599, (2015) DOI: 10.1007/s11306-015-0822-7
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Publikation
Gonzalez-Beltran, A.; Neumann, S.; Maguire, E.; Sansone, S.-A.; Rocca-Serra, P.; The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again BMC Bioinformatics 15 (Suppl 1), S11, (2014) DOI: 10.1186/1471-2105-15-S1-S11
BackgroundThe ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing I nvestigations, S tudies and A ssays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment.ResultsThe Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data.ConclusionsThe Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking.Software availabilityThe Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests.
Publikation
Haug, K.; Salek, R. M.; Conesa, P.; Hastings, J.; de Matos, P.; Rijnbeek, M.; Mahendraker, T.; Williams, M.; Neumann, S.; Rocca-Serra, P.; Maguire, E.; Gonzalez-Beltran, A.; Sansone, S.-A.; Griffin, J. L.; Steinbeck, C.; MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data Nucleic Acids Res. 41, D781-D786, (2013) DOI: 10.1093/nar/gks1004
MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Metabolomic profiling is an important tool for research into biological functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of experimental methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their reference spectra as well as their biological roles, locations, concentrations and raw data from metabolic experiments. Studies automatically receive a stable unique accession number that can be used as a publication reference (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
Publikation
Sansone, S.-A.; Rocca-Serra, P.; Field, D.; Maguire, E.; Taylor, C.; Hofmann, O.; Fang, H.; Neumann, S.; Tong, W.; Amaral-Zettler, L.; Begley, K.; Booth, T.; Bougueleret, L.; Burns, G.; Chapman, B.; Clark, T.; Coleman, L.-A.; Copeland, J.; Das, S.; de Daruvar, A.; de Matos, P.; Dix, I.; Edmunds, S.; Evelo, C. T.; Forster, M. J.; Gaudet, P.; Gilbert, J.; Goble, C.; Griffin, J. L.; Jacob, D.; Kleinjans, J.; Harland, L.; Haug, K.; Hermjakob, H.; Sui, S. J. H.; Laederach, A.; Liang, S.; Marshall, S.; McGrath, A.; Merrill, E.; Reilly, D.; Roux, M.; Shamu, C. E.; Shang, C. A.; Steinbeck, C.; Trefethen, A.; Williams-Jones, B.; Wolstencroft, K.; Xenarios, I.; Hide, W.; Toward interoperable bioscience data Nat. Genet. 44, 121-126, (2012) DOI: 10.1038/ng.1054
To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.
