OMeR is an integrative e-infrastructure based on the 3 key components. This integration is rely on a modular strategy and an effort toward standardization in order to guaranty interoperability. Particular care will be taken to ensure OMeR interoperability with key resources like MetaboLights and virtual research environments (e.g. framework of the European project PhenoMeNal).
OMeR components are internationally acknowledged and involved in European infrastructures (ELIXIR, PhenoMeNal...). Moreover, they are key elements in collaborative project with national SMEs (e.g. MedDay Pharmaceuticals) and international research institutes (CEA, EBI ...) and to foster new academic and private partnerships.
Service offered by OMeR relies on three e-Resources strongly supported by INRA and hosted in the instituted data centres:
![]() | Workflow4Metabolomics, http://workflow4metabolomics.org
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![]() | MetExplore, www.metexplore.fr
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![]() | PeakForest, https://peakforest.org
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Concerning technological aspects, OMeR is developing modular approaches, based on web components (e.g. MetExploreViz, Bioinformatics 2017) and microservices for phenotyping data portals (eg Food Metabolome Knowledge databases or Metabolic profiles of Biological matrices database). OMeR has a strong and broad expertise in the development of systems integrating SQL technologies, noSQL and built upon R frameworks, python, java and JavaScript. Several prototypes are under development like the work initiated with an INRA funding (15k€) for a « Virtual environment for global metabolism study ».
For methodological aspects, OMeR implements strategies for data life cycle management (storage, secured environment...) related to data production platforms (MetaboHub and beyond) and other partners. These strategies are currently integrated in the quality policy of CNOC INRA labeled metabolomics platforms. OMeR interacts with European partners (H2020 PhenoMeNal, JPI FOODBALL, ...) in the developments of SOP to create centralized data curation portals. OMeR also develops and implements original algorithms to analyze metabolic networks which constitute the biological context of metabolomics data. Future directions include text mining and semantic approaches to enrich knowledge associated with metabolomics data.