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Best Practices in Manual Annotation with the Gene Ontology

Profile image of Sylvain PouxSylvain Poux

2016, Methods in Molecular Biology

https://doi.org/10.1007/978-1-4939-3743-1_4
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14 pages

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Abstract

The Gene Ontology (GO) is a framework designed to represent biological knowledge about gene products' biological roles and the cellular location in which they act. Biocuration is a complex process: the body of scientifi c literature is large and selection of appropriate GO terms can be challenging. Both these issues are compounded by the fact that our understanding of biology is still incomplete; hence it is important to appreciate that GO is inherently an evolving model. In this chapter, we describe how biocurators create GO annotations from experimental fi ndings from research articles. We describe the current best practices for high-quality literature curation and how GO curators succeed in modeling biology using a relatively simple framework. We also highlight a number of diffi culties when translating experimental assays into GO annotations.

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Chibucos et al. 2017 The Gene Ontology Handbook Chap
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The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientifi c study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made.

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Use and misuse of the gene ontology annotations
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The accumulation of data produced by genome-scale research requires explicitly defined vocabularies to describe the biological attributes of genes in order to allow integration, retrieval and computation of the data 1 . Arguably, the most successful example of systematic description of biology is the Gene Ontology (GO) project 2 . GO is widely used in biological databases, annotation projects and computational analyses (there are 2,960 citations for GO in version 3.0 of the ISI Web of Knowledge) for annotating newly sequenced genomes 3 , text mining 4,5 , network modelling 6 and clinical applications 7 , among others. GO has two components: the ontologies themselves, which are the defined terms and the structured relationships between them (GO ontology); and the associations between gene products and the terms (GO annotations). GO provides both ontologies and annotations for three distinct areas of cell biology: molecular function, biological process, and cellular component or location.

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The Gene Ontology resource: enriching a GOld mine
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The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesig...

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Gene Ontology Annotations and Resources
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The Gene Ontology (GO) Consortium (GOC, http:// www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.

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References (30)

  1. Popper KR (2002) Conjectures and refuta- tions: the growth of scientifi c knowledge. Routledge, New York
  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unifi cation of biology. The Gene Ontology Consortium. Nat Genet 25:25-29
  3. Thomas PD (2016) The gene ontology and the meaning of biological function. In: Dessimoz C, Škunca N (eds) The gene ontol- ogy handbook. Methods in molecular biology, vol 1446. Humana Press. Chapter 2
  4. Gene Consortium (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res 43:D1049-D1056
  5. Gaudet P, Škunca N, Hu JC, Dessimoz C (2016) Primer on the gene ontology. In: Dessimoz C, Škunca N (eds) The gene ontol- ogy handbook. Methods in molecular biology, vol 1446. Humana Press. Chapter 3
  6. Huntley RP, Lovering RC (2016) Annotation extensions. In: Dessimoz C, Škunca N (eds) The gene ontology handbook. Methods in molecular biology, vol 1446. Humana Press. Chapter 17
  7. Korn C, Scholz SR, Gimadutdinow O, Lurz R, Pingoud A, Meiss G (2005) Interaction of DNA fragmentation factor (DFF) with DNA reveals an unprecedented mechanism for nuclease inhibition and suggests that DFF can be activated in a DNA-bound state. J Biol Chem 280:6005-6015
  8. Liu X, Kim CN, Yang J, Jemmerson R, Wang X (1996) Induction of apoptotic program in cell-free extracts: requirement for dATP and cytochrome c. Cell 86:147-157
  9. Lee K, Pisarska MD, Ko JJ, Kang Y, Yoon S, Ryou SM, Cha KY, Bae J (2005) Transcriptional factor FOXL2 interacts with DP103 and induces apoptosis. Biochem Biophys Res Commun 336:876-881
  10. Poux S, Magrane M, Arighi CN, Bridge A, O'Donovan C, Laiho K (2014) Expert cura- tion in UniProtKB: a case study on dealing with confl icting and erroneous data. Database (Oxford):bau016
  11. Stefely JA, Reidenbach AG, Ulbrich A, Oruganty K, Floyd BJ, Jochem A, Saunders JM, Johnson IE, Minogue CE, Wrobel RL et al (2015) Mitochondrial ADCK3 employs an atypical protein kinase-like fold to enable coenzyme Q biosynthesis. Mol Cell 57:83-94
  12. Kim J, Hake SB, Roeder RG (2005) The human homolog of yeast BRE1 functions as a transcriptional coactivator through direct acti- vator interactions. Mol Cell 20:759-770
  13. Groza T, Kohler S, Moldenhauer D, Vasilevsky N, Baynam G, Zemojtel T, Schriml LM, Kibbe WA, Schofi eld PN, Beck T et al (2015) The human phenotype ontology: semantic unifi ca- tion of common and rare disease. Am J Hum Genet 97(1):111-124
  14. 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 6:11
  15. Edson KZ, Prasad B, Unadkat JD, Suhara Y, Okano T, Guengerich FP, Rettie AE (2013) Cytochrome P450-dependent catabolism of vitamin K: omega-hydroxylation catalyzed by human CYP4F2 and CYP4F11. Biochemistry 52:8276-8285
  16. McDonald MG, Rieder MJ, Nakano M, Hsia CK, Rettie AE (2009) CYP4F2 is a vitamin K1 oxidase: an explanation for altered warfarin dose in carriers of the V433M variant. Mol Pharmacol 75:1337-1346
  17. Fava C, Montagnana M, Almgren P, Rosberg L, Lippi G, Hedblad B, Engstrom G, Berglund G, Minuz P, Melander O (2008) The V433M variant of the CYP4F2 is associated with ischemic stroke in male Swedes beyond its effect on blood pressure. Hypertension 52:373-380
  18. Jin R, Koop DR, Raucy JL, Lasker JM (1998) Role of human CYP4F2 in hepatic catabolism of the proinfl ammatory agent leukotriene B4. Arch Biochem Biophys 359:89-98
  19. Kikuta Y, Kusunose E, Kondo T, Yamamoto S, Kinoshita H, Kusunose M (1994) Cloning and expression of a novel form of leukotriene B4 omega-hydroxylase from human liver. FEBS Lett 348:70-74
  20. Lasker JM, Chen WB, Wolf I, Bloswick BP, Wilson PD, Powell PK (2000) Formation of 20-hydroxyeicosatetraenoic acid, a vasoactive and natriuretic eicosanoid, in human kidney. Role of Cyp4F2 and Cyp4A11. J Biol Chem 275:4118-4126
  21. Stec DE, Roman RJ, Flasch A, Rieder MJ (2007) Functional polymorphism in human CYP4F2 decreases 20-HETE production. Physiol Genomics 30:74-81
  22. Gerlitz O, Basler K (2002) Wingful, an extra- cellular feedback inhibitor of Wingless. Genes Dev 16:1055-1059
  23. Giraldez AJ, Copley RR, Cohen SM (2002) HSPG modifi cation by the secreted enzyme Notum shapes the Wingless morphogen gradi- ent. Dev Cell 2:667-676
  24. Kreuger J, Perez L, Giraldez AJ, Cohen SM (2004) Opposing activities of Dally-like glypi- can at high and low levels of Wingless morpho- gen activity. Dev Cell 7:503-512
  25. Kakugawa S, Langton PF, Zebisch M, Howell SA, Chang TH, Liu Y, Feizi T, Bineva G, O'Reilly N, Snijders AP et al (2015) Notum deacylates Wnt proteins to suppress signalling activity. Nature 519:187-192
  26. Zhang X, Cheong SM, Amado NG, Reis AH, MacDonald BT, Zebisch M, Jones EY, Abreu JG, He X (2015) Notum is required for neural and head induction via Wnt deacylation, oxi- dation, and inactivation. Dev Cell 32: 719-730
  27. Huntley RP, Sawford T, Mutowo-Meullenet P, Shypitsyna A, Bonilla C, Martin MJ, O'Donovan C (2015) The GOA database: gene ontology annotation updates for 2015. Nucleic Acids Res 43:D1057-D1063
  28. Deegan (née Clark) JI, Dimmer EC, Mungall CJ (2010) Formalization of taxon-based con- straints to detect inconsistencies in annotation and ontology development. BMC Bioinformatics 11:530
  29. Gaudet P, Livstone MS, Lewis SE, Thomas PD (2011) Phylogenetic-based propagation of func- tional annotations within the Gene Ontology consortium. Brief Bioinform 12:449-462
  30. Mi H, Muruganujan A, Thomas PD (2013) PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res 41:D377-D386

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