"Database" - what is it, definition of the term
A data repository that structures information into tables, records, and fields, enabling efficient storage, retrieval, and manipulation via query languages; it enforces integrity constraints, supports concurrent access, and provides mechanisms for backup and recovery.
Detailed information
Data repositories for rodent research compile genomic, phenotypic, and experimental records into searchable collections. These systems store nucleotide sequences, gene annotations, and variant information for species such as rats and mice, enabling cross‑study comparisons and meta‑analyses. Access points typically include web portals with query interfaces, programmatic APIs, and bulk download options.
Key features of rodent data stores include:
- Integrated taxonomy linking species, strains, and subspecies.
- Curated gene expression profiles from tissue‑specific experiments.
- Phenotype ontologies that standardize trait descriptions across laboratories.
- Version control that tracks updates to genome assemblies and annotation releases.
- Secure user authentication for unpublished or embargoed datasets.
Prominent examples are:
- The Mouse Genome Informatics (MGI) portal, which aggregates genetic, phenotypic, and functional data for Mus musculus.
- The Rat Genome Database (RGD), providing comprehensive resources for Rattus norvegicus, including disease models and pathway maps.
- Ensembl’s vertebrate division, offering comparative genomics tools that align mouse and rat genomes with other species.
- NCBI’s Gene and Sequence Read Archive, supplying raw sequencing reads and processed gene records for both rodents.
Data quality is maintained through automated validation pipelines and community‑driven curation. Contributors submit new entries via standardized templates; reviewers verify nomenclature, reference genomes, and experimental metadata before integration. Regular audits identify inconsistencies, duplicate records, and obsolete identifiers.
Interoperability is achieved by adopting common data standards such as the Minimum Information About a Microarray Experiment (MIAME) and the Clinical Data Interchange Standards Consortium (CDISC) models for preclinical studies. These conventions facilitate data exchange between repositories, statistical analysis platforms, and laboratory information management systems.
Researchers leverage these repositories to identify candidate genes, assess strain‑specific disease susceptibility, and design reproducible experiments. By centralizing disparate datasets, the information systems accelerate discovery and reduce redundancy in rodent research.