What is PhenoMeNal?
PhenoMeNal (Phenome and Metabolome aNalysis) is a comprehensive and standardised e-infrastructure that supports the data processing and analysis pipelines for molecular phenotype data generated by metabolomics applications. An infrastructure aimed to address the H2020 Societal Challenge in Health, Demographic Change and Wellbeing, PhenoMeNal provides services enabling computation and analysis to improve the understanding of the causes and mechanisms underlying health, healthy ageing and diseases.
- To use existing open source community standards, integrate tools, resources and methods for the management, dissemination and computational analysis of very large datasets of human metabolic phenotyping and genomic data into a secure and sustainable e-Infrastructure
- To operate and consolidate the PhenoMeNal e-infrastructure based on existing internal and external HPC (High-performance computing), cloud, and grid resources, including the EGI and the EGI Federated Cloud, and to extend it to world-wide computational infrastructures
- To improve and scale-up tools used within the infrastructure to cope with very large datasets
- To establish technology for a water-tight audit trail for the processing of human metabolic phenotyping data from the raw data acquisition all the way to the generation of high-level biomedical insights (such as a medical diagnosis)
- To establish privacy-protection methods that allow working with highly sensitive molecular phenotype data
- To foster the worldwide adoption of PhenoMeNal through a wide range of outreach, dissemination, networking and training activities
- To develop a model to ensure sustainability of the PhenoMeNal network
Deployment on Cloud Research Environment (CRE)
PhenoMeNal provides a Virtual Research Environment (VRE) known as the “PhenoMeNal Cloud Research Environment” (CRE), for interoperable and scalable metabolomics analysis. End-users, such as researchers and research teams, educators, SMEs, and any other type of user, will be able to create, on-demand and through a simple user interface, an environment of tools, services, data supporting their research needs. Hardware setup and software deployment required to operate these facilities are completely transparent to the PhenoMeNal CRE and hence the users can focus on the analysis and not the technicalities. See Starting a PhenoMeNal CRE on a public or private cloud provider.
Public Galaxy instance
The PhenoMeNal Public Galaxy VRE runs on top of a Kubernetes cluster. The pre-provisioned PhenoMeNal Galaxy docker image is able to run inside a Kubernetes Replication Controller/Pod and communicates through the service account of Kubernetes with the master nodes to submit jobs to the cluster. This docker image contains all the tools that have been dockerized, “galaxified” and tested with sample datasets to check that they work adequately. Within this public instance, we provide shared workflows and data sets within Galaxy, that any user can try on the instance. See PhenoMeNal: Accessing Metabolomics Workflows in Galaxy.
The PhenoMeNal App Library showcases our service catalogue listing 36 applications that are available via Galaxy workflows and Juputer libraries through the Cloud Research Environment. See How to make your software tool available through PhenoMeNal.
Privacy and Ethics
The PhenoMeNal e-infrastructure supports the data processing and analysis pipelines for molecular phenotype data from the earliest time point of the data acquisition in the laboratory up to the high level medical and biological conclusions and interpretations. This includes handling of data generated from comprehensive clinical, genotypic, ‘omics and analytic sources including medical records, electronic health records, clinical measurements, genotypic data, phenotypic data from tissue and biofluid analysis, image and pathology data. Depending on the data intended to be processed, Ethical Legal and Social Implications (ELSI) may have a strong bearing on the use and re-use of the data.
PhenoMeNal ensures patient confidentiality and prevention of identification of patients by providing a secure environment to process input data and to provide a series of data outputs for futher analysis.