Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research

  • Janan T. EppigEmail author
  • Cynthia L. Smith
  • Judith A. Blake
  • Martin Ringwald
  • James A. Kadin
  • Joel E. Richardson
  • Carol J. Bult
Part of the Methods in Molecular Biology book series (MIMB, volume 1488)


The Mouse Genome Informatics (MGI), resource ( has existed for over 25 years, and over this time its data content, informatics infrastructure, and user interfaces and tools have undergone dramatic changes (Eppig et al., Mamm Genome 26:272–284, 2015). Change has been driven by scientific methodological advances, rapid improvements in computational software, growth in computer hardware capacity, and the ongoing collaborative nature of the mouse genomics community in building resources and sharing data. Here we present an overview of the current data content of MGI, describe its general organization, and provide examples using simple and complex searches, and tools for mining and retrieving sets of data.

Key words

Resources for systems genetics Database Genome informatics 



MGI is supported by the following NIH grants: HG000330 and HG002273 from the National Human Genome Research Institute; HD062499 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development; CA089713 from the National Cancer Institute; OD011190 from the Office of the Director, Division of Comparative Medicine; and NS082666 from the National Institute of Neurological Disorder and Stroke.


  1. 1.
    Eppig JT, Richardson JE, Kadin JA, Ringwald M, Blake JA, Bult CJ (2015) Mouse Genome Informatics (MGI): reflecting on 25 years. Mamm Genome 26:272–284CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Eppig JT, Richardson JE, Kadin JA, Smith CL, Blake JA, Bult CJ, MGD Team (2015) Mouse Genome Database: from sequence to phenotypes and disease models. Genesis 53:458–473CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Finger JH, Smith CM, Hayamizu TF, McCright IJ, Xu J, Eppig JT, Kadin JA, Richardson JE, Ringwald M (2015) The mouse Gene Expression Database (GXD): new features and how to get the most out of them. Genesis 53:510–522CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Murray SA, Eppig JT, Smedley D, Simpson EM, Rosenthal N (2012) Beyond knockouts: cre resources for conditional mutagenesis. Mamm Genome 23:587–599CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Bult CJ, Krupke DM, Begley DA, Richardson JE, Neuhauser SB, Sundberg JP, Eppig JT (2015) Mouse Tumor Biology (MTB): a database of mouse models for human cancer. Nucleic Acids Res 43:D818–D824CrossRefPubMedGoogle Scholar
  6. 6.
    Shultz LD, Lyons BL, Burzenski LM, Gott B, Chen X, Chaleff S, Kotb M, Gillies SD, King M, Mangada J, Greiner DL, Handgretinger R (2005) Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2R gamma null mice engrafted with mobilized human hemopoietic stem cells. J Immunol 174:6477–6489CrossRefPubMedGoogle Scholar
  7. 7.
    Eppig JT, Motenko H, Richardson JE, Richards-Smith B, Smith CL (2015) The International Mouse Strain Resource (IMSR): cataloging worldwide mouse and ES cell line resources. Mamm Genome 26:448–455CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Eppig JT, Blake JA, Bult CJ, Kadin JA, Richardson JE, Mouse Genome Database Group (2015) The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res 43:D726–D736CrossRefPubMedGoogle Scholar
  9. 9.
    Motenko H, Neuhauser SB, O'Keefe M, Richardson JE (2015) MouseMine: a new data warehouse for MGI. Mamm Genome 26:325–330CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Smith RN, Aleksic J, Butano D, Carr A, Contrino S, Hu F, Lyne M, Lyne R, Kalderimis A, Rutherford K, Stepan R, Sullivan J, Wakeling M, Watkins X, Micklem G (2012) InterMine: a flexible data warehouse system for the integration and analysis of heterogeneous biological data. Bioinformatics 28:3163–3165CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Richardson JE, Bult CJ (2015) Visual annotation display (VLAD): a tool for finding functional themes in lists of genes. Mamm Genome 26:567–573CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Köhler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I, Black GC, Brown DL, Brudno M, Campbell J, FitzPatrick DR, Eppig JT, Jackson AP, Freson K, Girdea M, Helbig I, Hurst JA, Jähn J, Jackson LG, Kelly AM, Ledbetter DH, Mansour S, Martin CL, Moss C, Mumford A, Ouwehand WH, Park SM, Riggs ER, Scott RH, Sisodiya S, Van Vooren S, Wapner RJ, Wilkie AO, Wright CF, Vulto-van Silfhout AT, de Leeuw N, de Vries BB, Washingthon NL, Smith CL, Westerfield M, Schofield P, Ruef BJ, Gkoutos GV, Haendel M, Smedley D, Lewis SE, Robinson PN (2014) The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42:D966–D974CrossRefPubMedGoogle Scholar
  13. 13.
    Richardson JE (2006) fjoin: simple and efficient computation of feature overlaps. J Comput Biol 13:1457–1464CrossRefPubMedGoogle Scholar
  14. 14.
    Zhu Y, Richardson JE, Hale P, Baldarelli RM, Reed DJ, Recla JM, Sinclair R, Reddy TBK, Bult CJ (2015) A unified gene catalog for the laboratory mouse reference genome. Mamm Genome 26:295–304CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Blake JA, Bult CJ, Eppig JT, Kadin JA, Richardson JE, Mouse Genome Database Group (2014) The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Res 42:D810–D817CrossRefPubMedGoogle Scholar
  16. 16.
    Smith CL, Eppig JT (2012) The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data. Mamm Genome 23:653–668CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Smith CL, Eppig JT (2015) Expanding the mammalian phenotype ontology to support automated exchange of high throughput mouse phenotyping data generated by large-scale mouse knockout screens. J Biomed Semantics 6:11CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Bello SM, Eppig JT, MGI Software Group (2016) Inferring gene-to-phenotype and gene-to-disease relationships at mouse genome informatics: challenges and solutions. J Biomed Semantics 7:14Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Janan T. Eppig
    • 1
    Email author
  • Cynthia L. Smith
    • 1
  • Judith A. Blake
    • 1
  • Martin Ringwald
    • 1
  • James A. Kadin
    • 1
  • Joel E. Richardson
    • 1
  • Carol J. Bult
    • 1
  1. 1.The Jackson LaboratoryBar HarborUSA

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