Current MGI Favorites:

  • Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021 Jan 8;49(D1):D325-D334. [PMID: 33290552]

  • Good PM, et al., Reactome and the Gene Ontology: Digital convergence of data resources. Bioinformatics. 2021 May 8;37(19):3343-3348. [PMID: 33964129]

  • Wood V, et al., Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns. Open Biol. 2020 Sep;10(9):200149. [PMID: 32875947]

  • Attrill H, et al., Annotation of gene product function from high-throughput studies using the Gene Ontology. Database. 2019 Jan1;2019:baz007. [PMID: 30715275]
         Presents an annotation framework for high-throughput studies and, through the use of new high-throughput evidence codes, will increase the visibility of these annotations.

  • The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 2019 Jan 8;47(D1):D330-D338. doi: 10.1093/nar/gky1055. [PMID: 30395331]

  • Thomas PD, et al., Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems. Nat Genet. 2019 Oct;51(10):1429-1433. [PMID: 31548717]

  • Christie KR and Blake JA. Sensing the cilium, digital capture of ciliary data for comparative genomics investigations. Cilia 2018; 7:3. [PMID: 29713460]
         Captured information about cilia as studied in the laboratory mouse and used it to improve the GO ciliary annotations for mouse genes, and to their corresponding human genes.

  • Lovering RC, et al., Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology. Circ Genom Precis Med. 2018. Feb;11(2):e001813. [PMID: 29440116]
         Uses a combination of literature curation and gene ontology development for heart-specific genes to aid the analysis of genes responsible for the spread of the cardiac action potential through the heart.

  • Denny P, et al., Exploring autophagy with Gene Ontology. Autophagy 2018 Feb. 17:1-18. [PMID: 29455577]
         Discusses how autophagy is represented in the Gene Ontology and how those terms are interrelated.

  • Roncaglia P, et al., The Gene Ontology of eukaryotic cilia and flagella. Cilia. 2017. Nov 16;6:10. [PMID: 29177046]
         Members of the SYSCILIA and the Gene Ontology Consortia created or modified 127 GO terms related to eukaryotic cilia/flagella or prokaryotic flagella.

  • Hill DP, 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.

  • Tripathi S, et al., Gene regulation knowledge commons: community action takes care of DNA binding transcription factors. Database (Oxford). 2016 Jun 5;2016. pii: baw088. doi: 10.1093/database/baw088. [PMID: 27270715]

  • Drabkin HJ, 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 JA, 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 RP, 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 DA, 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.

  • Chibucos MC, et al., Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford). 2014 Jul 22;2014. pii: bau075. doi: 10.1093/database/bau075. [PMID: 25052702]

  • 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 CM, 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 DP, 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.