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The Gene Ontology Handbook

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Cover of 'The Gene Ontology Handbook'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Primer on Ontologies
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    Chapter 2 The Gene Ontology and the Meaning of Biological Function
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    Chapter 3 Primer on the Gene Ontology
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    Chapter 4 Best Practices in Manual Annotation with the Gene Ontology
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    Chapter 5 Computational Methods for Annotation Transfers from Sequence
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    Chapter 6 Text Mining to Support Gene Ontology Curation and Vice Versa
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    Chapter 7 How Does the Scientific Community Contribute to Gene Ontology?
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    Chapter 8 Evaluating Computational Gene Ontology Annotations
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    Chapter 9 Evaluating Functional Annotations of Enzymes Using the Gene Ontology
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    Chapter 10 Community-Wide Evaluation of Computational Function Prediction
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    Chapter 11 Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools
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    Chapter 12 Semantic Similarity in the Gene Ontology
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    Chapter 13 Gene-Category Analysis
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    Chapter 14 Gene Ontology: Pitfalls, Biases, and Remedies
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    Chapter 15 Visualizing GO Annotations
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    Chapter 16 A Gene Ontology Tutorial in Python
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    Chapter 17 Annotation Extensions
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    Chapter 18 The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations
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    Chapter 19 Complementary Sources of Protein Functional Information: The Far Side of GO
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    Chapter 20 Integrating Bio-ontologies and Controlled Clinical Terminologies: From Base Pairs to Bedside Phenotypes
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    Chapter 21 The Vision and Challenges of the Gene Ontology
Attention for Chapter 9: Evaluating Functional Annotations of Enzymes Using the Gene Ontology
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Chapter title
Evaluating Functional Annotations of Enzymes Using the Gene Ontology
Chapter number 9
Book title
The Gene Ontology Handbook
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-3743-1_9
Pubmed ID
Book ISBNs
978-1-4939-3741-7, 978-1-4939-3743-1

Gemma L. Holliday, Rebecca Davidson, Eyal Akiva, Patricia C. Babbitt


Christophe Dessimoz, Nives Škunca


The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 3%
Spain 1 3%
Netherlands 1 3%
Unknown 27 90%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 17%
Other 4 13%
Researcher 4 13%
Student > Ph. D. Student 4 13%
Professor 2 7%
Other 5 17%
Unknown 6 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 23%
Agricultural and Biological Sciences 6 20%
Computer Science 4 13%
Social Sciences 2 7%
Mathematics 1 3%
Other 4 13%
Unknown 6 20%
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