Current MGI Favorites:

  • Hill D.P., et al., 2016. Modeling biochemical pathways in the gene ontology. Database. Sep 1. [PMID: 27589964]
         Discusses how the Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways.

  • Drabkin H.J., et al., 2015. Application of comparative biology in GO functional annotation: the mouse model. Mamm Genome. 2015 Jul;26(9):574-583. [PMID: 26141960]
         Describes the application of comparative approaches to capturing mouse functional data.

  • MGI GO Group, et al., 2015. Gene Ontology Consortium: Going Forward. Nucleic Acids Research 43:D1049-D1056. [PMID: 25428369]
         Describes improvements and expansions to several branches of the ontology as well as updates that permit more efficient dissemination of the GO.

  • Ascensao J.A., et al., 2014. Methodology for Inferring Gene Function from Phenotype Data. BMC Bioinformatics. Dec 12;15(1):405. [PMID: 25495798]
         Describes algorithms that define rules for predicting gene function by examining the structure and relationships between the gene functions and phenotypes.

  • Alam-Faruque Y, et al., 2014. Representing kidney development using the gene ontology. PLoS One. Jun 18;9(6):e99864. [PMID: 24941002]
         Discusses the collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development.

  • Huntley R.P., et al., 2014. A method for increasing expressivity of Gene Ontology annotations using a compositional approach. BMC Bioinformatics. May 21;15(1):155. [PMID: 24885854]
         Describes the content and structure of GO annotation extensions and how they provide additional contextual information.

  • Natale D.A., et al., 2014. Protein Ontology: a controlled structured network of protein entities. Nucleic Acids Res. Jan;42(Database issue):D415-21 [PMID: 24270789]
         Describes how the Protein Ontology (PRO) defines protein entities and explicitly represents their major forms and interrelations.

  • Blake J.A., 2013. Ten quick tips for using the Gene Ontology. PLoS Comput Biol. Nov;9(11):e1003343. [PMID: 24244145]
         Describes how to get the most out of your GO resources.

  • Machado C.M., et al., 2013. Enrichment analysis applied to disease prognosis. J Biomed Semantics. Oct 8;4(1):21. [PMID: 24103636]
         Uses GO enrichment analysis to assist medical doctors in the definition of the appropriate treatments and preventive actions for individual patients.

  • Tripathi S., et al., 2013. Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort. Database (Oxford). Aug 27;2013:bat062. [PMID: 23981286]
         Describes a curation strategy for using the GO to annotate specific DNA binding transcription factors based on experimental evidence.

  • Hill D.P., et al., 2013. Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology. BMC Genomics. Jul 29;14:513. [PMID: 23895341]
         Describes a collaboration between the GO and the Chemical Entities of Biological Interest (ChEBI) ontology developers to ensure the representation of chemicals in the GO is consistent with expertise captured in ChEBI.

  • Drabkin H.J., et al., 2012. Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database. Database (Oxford). Oct 29;2012:bas045. [PMID: 23110975]
         Discusses the workflow associated with manual GO annotation at MGI, from literature collection to display of annotations.