From information to understanding: the role of model organism databases in comparative and functional genomics.

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.

[1]  Tatiana A. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2004, Nucleic Acids Res..

[2]  S. Salzberg,et al.  The Transcriptional Landscape of the Mammalian Genome , 2005, Science.

[3]  Judith A. Blake,et al.  The Mouse Genome Database (MGD): from genes to mice—a community resource for mouse biology , 2004, Nucleic Acids Res..

[4]  D. Reidel,et al.  The Transcriptional Landscape of the Mammalian Genome The FANTOM Consortium* and RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project Core Group)* , 2005 .

[5]  Li Ni,et al.  A procedure for assessing GO annotation consistency , 2005, ISMB.

[6]  Cynthia L. Smith,et al.  The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information , 2004, Genome Biology.

[7]  Pavel A Pevzner,et al.  Mammalian phylogenomics comes of age. , 2004, Trends in genetics : TIG.

[8]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[9]  Len A Pennacchio,et al.  Comparative genomic analysis as a tool for biological discovery , 2003, The Journal of physiology.

[10]  K. Olden Use of ‘Omic’ Approaches in Unraveling Mechanisms of Gene-Environment Interactions , 2004 .

[11]  B Marshall,et al.  Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource , 2004, Nucleic Acids Res..

[12]  Terrence S. Furey,et al.  The UCSC Genome Browser Database , 2003, Nucleic Acids Res..

[13]  Colin N. Dewey,et al.  Initial sequencing and comparative analysis of the mouse genome. , 2002 .

[14]  Carol J. Bult Data integration standards in model organisms: from genotype to phenotype in the laboratory mouse , 2002 .

[15]  Judith A. Blake,et al.  Mouse genome informatics in a new age of biological inquiry , 2000, Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering.

[16]  M. Justice,et al.  Mouse as the measure of man? , 2000, Trends in genetics : TIG.

[17]  Of Flies and Mice , 2000, Science.

[18]  R. DePinho,et al.  A bumper crop of cancer genes , 1999, Nature Genetics.

[19]  Richard D Klausner,et al.  Studying cancer in the mouse , 1999, Oncogene.

[20]  M. Meisler The role of the laboratory mouse in the human genome project. , 1996, American journal of human genetics.

[21]  G. Barsh,et al.  Biological insights through genomics: mouse to man. , 1996, The Journal of clinical investigation.

[22]  K. Paigen,et al.  A miracle enough: the power of mice , 1995, Nature Medicine.