The Mouse Genome Informatics group is a founding member of the Gene Ontology Consortium ( MGI fully incorporates the GO in the database and provides a GO browser.

What is a Gene Ontology annotation?

Gene Ontology annotation is a manually or automatically assigned text file containing the following information:

  • Gene name, symbol
  • Gene Ontology term
  • Published reference
  • Evidence code
  • Date of the annotation
  • Optional information (Qualifiers such as "NOT", "contributes_to", "colocalizes_with"; Notes with both free text and controlled vocabulary information)

What is manual annotation?

Scientific curators use published literature to assign GO terms based on evidence codes, which support the annotation. See also the Guide to GO Evidence Codes at the Gene Ontology Consortium site.

What are evidence codes?

MGI uses the following evidence codes:

  • Manually assigned:
    • EXP - Inferred from experiment
    • IAS - Inferred from ancestral sequence
    • IBA - Inferred from biological aspect of ancestor
    • IBD - Inferred from biological aspect of descendant
    • IC - Inferred by curator
    • IDA - Inferred from direct assay
    • IEP - Inferred from expression pattern
    • IGI - Inferred from genetic interaction
    • IKR - Inferred from key residues
    • IMP - Inferred from mutant phenotype
    • IMR - Inferred from missing residues
    • IPI - Inferred from physical Interaction
    • IRD - Inferred from rapid divergence
    • ISA - Inferred from sequence alignment
    • ISM - Inferred from sequence model
    • ISO - Inferred from sequence orthology
    • ISS - Inferred from sequence and structural similarity
    • ND - Not determined
    • TAS - Traceable author statement
  • Automatically assigned:
    • IEA - Inferred from electronic annotation
    • HDA - inferred from high throughput direct assay
    • HEP - inferred from high throughput expression pattern
    • HGI - inferred from high throughput genetic interaction
    • HMP - inferred from high throughput mutant phenotype
    • RCA - Reviewed computational analysis