Data integration standards in model organisms: from genotype to phenotype in the laboratory mouse

Abstract The tremendous progress of the genome sequencing centers, combined with computational advances in algorithms for genome assembly and gene model prediction, provide the research community with valuable new resources in the form of complete, or nearly complete, genome sequences for a wide variety of organisms that serve as platforms to investigate biological systems. The challenge facing the bioinformatics community is how to integrate the rapidly emerging genomic data with experimental data, such as gene expression, protein interactions, cell processes and systems characteristics under select perturbations. Data integration is key to understanding at all levels because the process of integration brings together disparate types of data in formats that support effective data mining, pattern detection and hypothesis generation. Databases for model organisms are valuable sources of integrated data from the level of the genome to that of the phenotype. Databases for model organisms promote data integration through the development and implementation of nomenclature standards, controlled vocabularies and ontologies, that allow data different organisms to be compared and contrasted.