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The Gene Ontology Handbook

Overview of attention for book
Cover of 'The Gene Ontology Handbook'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Primer on Ontologies
  3. Altmetric Badge
    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 17: Annotation Extensions
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Chapter title
Annotation Extensions
Chapter number 17
Book title
The Gene Ontology Handbook
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-3743-1_17
Pubmed ID
Book ISBNs
978-1-4939-3741-7, 978-1-4939-3743-1
Authors

Rachael P. Huntley, Ruth C. Lovering, Huntley, RP, Lovering, RC, Huntley, Rachael P. H, Lovering, Ruth C.

Editors

Christophe Dessimoz, Nives Škunca

Abstract

The specificity of knowledge that Gene Ontology (GO) annotations currently can represent is still restricted by the legacy format of the GO annotation file, a format intentionally designed for simplicity to keep the barriers to entry low and thus encourage initial adoption. Historically, the information that could be captured in a GO annotation was simply the role or location of a gene product, although genetically interacting or binding partners could be specified. While there was no mechanism within the original GO annotation format for capturing additional information about the context of a GO term, such as the target gene of an activity or the location of a molecular function, the long-term vision for the GO Consortium was to provide greater expressivity in its annotations to capture physiologically relevant information.Thus, as a step forwards, the GO Consortium has introduced a new field into the annotation format, annotation extensions, which can be used to capture valuable contextual detail. This provides experimentally verified links between gene products and other physiological information that is crucial for accurate analysis of pathway and network data. This chapter will provide a simple overview of annotation extensions, illustrated with examples of their usage, and explain why they are useful for scientists and bioinformaticians alike.

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X Demographics

The data shown below were collected from the profiles of 2 X users 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 5%
Unknown 19 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Student > Ph. D. Student 5 25%
Student > Bachelor 2 10%
Student > Master 2 10%
Student > Doctoral Student 1 5%
Other 1 5%
Unknown 4 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 30%
Computer Science 3 15%
Agricultural and Biological Sciences 3 15%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Mathematics 1 5%
Other 2 10%
Unknown 4 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 July 2023.
All research outputs
#14,869,124
of 22,899,952 outputs
Outputs from Methods in molecular biology
#4,704
of 13,134 outputs
Outputs of similar age
#242,392
of 420,444 outputs
Outputs of similar age from Methods in molecular biology
#413
of 1,074 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,134 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 420,444 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.