PhenoMeNal

PhenoMeNal provides an e-infrastructure to process and analyze molecular phenotype data, primarily supporting metabolomics research.

Key Features:

• Infrastructure-as-a-Service (IaaS): Delivers a cloud-based IaaS approach for metabolomics data processing and analysis.
• Containerization (Docker): Packages open-source tools as Docker containers for consistent deployment.
• Continuous integration: Applies continuous integration testing to containerized tools to ensure reliability.
• Orchestration (Kubernetes): Uses Kubernetes for orchestration to enable scalable deployment across public and private clouds.
• Workflow systems: Supports workflow execution via Galaxy, Jupyter, Luigi, and Pachyderm.
• Standardized workflows: Provides standardized, automated, and published analysis workflows to promote reproducibility.
• Data standards and versioning: Supports standard data formats and uses representative, versioned datasets for consistency and interoperability.
• Harmonized software configuration: Harmonizes software installation and configuration across integrated tools to enable interoperability.
• Elasticity and cross-'omics adaptation: Implements an elastic architecture that can be adapted for other 'omics research domains beyond metabolomics.

Scientific Applications:

• Metabolomics studies: Facilitates processing and reproducible, workflow-driven analysis of metabolomics molecular phenotype data for biomedical, biotechnological, and applied biological research.
• Other 'omics analyses: Can be adapted to support scalable cloud-based analyses in additional 'omics domains.

Methodology:

Tools are packaged as Docker containers, subjected to continuous integration, orchestrated with Kubernetes for cloud deployment, executed via Galaxy/Jupyter/Luigi/Pachyderm workflows, and applied to standard data formats and versioned representative datasets.