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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
  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
  18. Altmetric Badge
    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
  21. Altmetric Badge
    Chapter 20 Integrating Bio-ontologies and Controlled Clinical Terminologies: From Base Pairs to Bedside Phenotypes
  22. Altmetric Badge
    Chapter 21 The Vision and Challenges of the Gene Ontology
Attention for Chapter 15: Visualizing GO Annotations
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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Chapter title
Visualizing GO Annotations
Chapter number 15
Book title
The Gene Ontology Handbook
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-3743-1_15
Pubmed ID
Book ISBNs
978-1-4939-3741-7, 978-1-4939-3743-1
Authors

Fran Supek, Nives Škunca, Supek, Fran, Škunca, Nives

Editors

Christophe Dessimoz, Nives Škunca

Abstract

Contemporary techniques in biology produce readouts for large numbers of genes simultaneously, the typical example being differential gene expression measurements. Moreover, those genes are often richly annotated using GO terms that describe gene function and that can be used to summarize the results of the genome-scale experiments. However, making sense of such GO enrichment analyses may be challenging. For instance, overrepresented GO functions in a set of differentially expressed genes are typically output as a flat list, a format not adequate to capture the complexities of the hierarchical structure of the GO annotation labels.In this chapter, we survey various methods to visualize large, difficult-to-interpret lists of GO terms. We catalog their availability-Web-based or standalone, the main principles they employ in summarizing large lists of GO terms, and the visualization styles they support. These brief commentaries on each software are intended as a helpful inventory, rather than comprehensive descriptions of the underlying algorithms. Instead, we show examples of their use and suggest that the choice of an appropriate visualization tool may be crucial to the utility of GO in biological discovery.

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 40%
United States 2 40%
France 1 20%
Mexico 1 20%
Netherlands 1 20%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 840%
Researcher 23 460%
Student > Master 13 260%
Student > Bachelor 7 140%
Student > Doctoral Student 5 100%
Other 9 180%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 960%
Biochemistry, Genetics and Molecular Biology 28 560%
Immunology and Microbiology 6 120%
Neuroscience 4 80%
Computer Science 4 80%
Other 8 160%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 13 September 2017.
All research outputs
#3,117,866
of 25,736,439 outputs
Outputs from Methods in molecular biology
#573
of 14,336 outputs
Outputs of similar age
#58,213
of 423,887 outputs
Outputs of similar age from Methods in molecular biology
#77
of 1,089 outputs
Altmetric has tracked 25,736,439 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,336 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 96% 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 423,887 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 1,089 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.