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Published on: August 15, 2019
Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database
Harold J Drabkin1, Judith A Blake,
1The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA. harold.drabkin@jax.org
Database : the Journal of Biological Databases and Curation
|November 1, 2012
Summary
The Mouse Genome Informatics (MGI) resource integrates genetic and genomic data for laboratory mice, including gene and phenotype information. MGI utilizes the Gene Ontology (GO) for functional gene annotation, streamlining data curation and accessibility for research.
Area of Science:
- Genomics
- Bioinformatics
- Mammalian Genetics
Background:
- The Mouse Genome Informatics (MGI) resource integrates multiple databases (Mouse Genome Database, Gene Expression Database, Mouse Tumor Biology database) for comprehensive mouse genetics and genomics data.
- MGI provides consensus and experimental views of knowledge, linking genotype to phenotype and integrating data on genes, sequences, maps, expression, alleles, strains, and mutant phenotypes.
- MGI serves as a model for human disease research, incorporating comparative mammalian data and utilizing large datasets alongside literature curation.
Purpose of the Study:
- To detail the workflow for manual Gene Ontology (GO) annotation at MGI, from literature collection to annotation display.
- To highlight MGI's role as a founding member of GO and its application of GO for functional gene annotation.
- To explain the data curation process, including literature indexing, gene integration, and the use of controlled vocabularies.
Main Methods:
- Manual curation of peer-reviewed literature, primarily from electronic journals.
- Indexing selected articles to specific areas of interest (e.g., GO, homology, phenotype) and associating them with existing or new genes.
- Utilizing controlled vocabularies, including the Gene Ontology (GO), for uniform data encoding and robust analysis.
Main Results:
- A systematic workflow for manual GO annotation at MGI has been established.
- The process ensures data quality through controlled vocabularies and evidence-based associations.
- Curator reports facilitate tracking and management of GO annotations, such as identifying genes lacking GO annotation.
Conclusions:
- MGI provides a robust, integrated resource for mouse genetics and genomics, crucial for understanding gene function and disease models.
- The manual GO annotation workflow ensures accurate and standardized functional gene information, enhancing data usability.
- The use of controlled vocabularies and evidence statements supports complex data analysis and query construction within MGI.
