BMS standards registry

BMS standards registry facilitates identification, comparison, and selection of existing data models, formats, and guidelines to support standards-driven management of omics, genomic, molecular, and phenotypic data.

Key Features:

• Data Model Shortlisting: Identifies and shortlists suitable data elements, entities, and models from uploaded example data files or text-based searches and maps example data to registry models.
• Comprehensive Registry Information: Records provenance, links to pertinent publications, contact details, and detailed descriptions of model entities and attributes for each registry entry.
• Integration with MOLGENIS Toolkit: Integrates with the MOLGENIS toolkit to leverage its model-driven framework for generating application components from data models.
• Model-Driven Development: Uses a simple modeling language and a generator suite to translate model XML into databases, exchange formats, and scriptable programming interfaces.
• Customization and Plug-ins: Supports a plug-in mechanism to customize both the generator suite and generated products programmatically.
• Efficiency in Development: Employs model-driven generation where approximately 500 lines of model XML can replace about 15,000 lines of hand-written code.
• Programming Interfaces and Data Formats: Produces an optimized database back-end, programming interfaces for R, Java, SOAP, REST/JSON, and RDF, and supports a tab-delimited file format for data exchange.
• ExtractModel Procedure: Provides an ExtractModel procedure to derive data models from existing databases for enhancement with MOLGENIS-generated components.

Scientific Applications:

• Next-generation sequencing: Applied for deploying data models and exchange formats in next-generation sequencing workflows.
• Genome-wide association studies (GWAS): Used to model, store, and exchange GWAS study data and metadata.
• Quantitative trait loci (QTL) analysis: Supports representation and management of QTL experiment data and associated metadata.
• Proteomics: Facilitates modeling and exchange of proteomics data and related experimental metadata.
• Biobanking: Supports data models and provenance recording relevant to biobanking operations and sample metadata.
• Genomic, molecular, and phenotypic experiments: Enables standardized representation and exchange for diverse genomic, molecular, and phenotypic experiment data.

Methodology:

Uses a model-driven approach with a simple modeling language and a generator suite that translates model XML into databases, exchange formats, and scriptable programming interfaces, and includes an ExtractModel procedure to derive models from existing databases.