BioSamples

BioSamples stores and manages metadata about biological samples used in sequencing, gene expression, and proteomics experiments to enable consistent recording and cross-database linking of sample information.

Key Features:

• Consistent Sample Information Recording: Allows submitters to describe a sample once and reference it in subsequent submissions to assay databases to reduce redundancy and ensure metadata consistency.
• Cross-Database Queries: Supports attribute-based queries (sample types, disease names, sample providers) to enable searches across linked datasets.
• Reference Samples: Maintains a collection of reference samples, including cell lines, and assigns accession numbers for cross-database referencing and interoperability with NCBI and DDBJ.
• Integration with ENA, ArrayExpress, and PRIDE: Links sample metadata to ENA, ArrayExpress, and PRIDE to support consistent sample annotation across these resources.
• Technical Enhancements and Scalability: Implements improvements that enable scaling to broader communities and more diverse submissions while enriching metadata content and supporting FAIR principles.
• Technical Infrastructure: Provides infrastructure to support engagement with initiatives such as FAIRplus and the Global Alliance for Genomics and Health.

Scientific Applications:

• Standardization of experimental metadata: Standardizes sample metadata for sequencing, gene expression, and proteomics experiments to enable consistent downstream analysis.
• Cross-database sample discovery: Enables discovery across ENA, ArrayExpress, and PRIDE by sample characteristics such as disease or provider.
• Reference sample reuse: Provides accessioned reference cell lines to facilitate reuse and comparability across studies.
• Genotype–phenotype linking in plants: Facilitates linking genotypic to phenotypic information within plant research communities.
• Multi-omics integration for disease research: Supports complex multi-omics data integration relevant to COVID-19 research.
• Data management use cases: Supports exemplar initiatives such as the ReSOLUTE project to increase sample findability and improve data management practices.

Methodology:

Assigns accession numbers, records single-instance sample descriptions with referencing for subsequent submissions, supports attribute-based querying (sample types, disease names, sample providers), links metadata across ENA, ArrayExpress, PRIDE, NCBI and DDBJ, and applies metadata enrichment and scalability improvements to support FAIR principles.