↓ Skip to main content

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
  4. Altmetric Badge
    Chapter 3 Primer on the Gene Ontology
  5. Altmetric Badge
    Chapter 4 Best Practices in Manual Annotation with the Gene Ontology
  6. Altmetric Badge
    Chapter 5 Computational Methods for Annotation Transfers from Sequence
  7. Altmetric Badge
    Chapter 6 Text Mining to Support Gene Ontology Curation and Vice Versa
  8. Altmetric Badge
    Chapter 7 How Does the Scientific Community Contribute to Gene Ontology?
  9. Altmetric Badge
    Chapter 8 Evaluating Computational Gene Ontology Annotations
  10. Altmetric Badge
    Chapter 9 Evaluating Functional Annotations of Enzymes Using the Gene Ontology
  11. Altmetric Badge
    Chapter 10 Community-Wide Evaluation of Computational Function Prediction
  12. Altmetric Badge
    Chapter 11 Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools
  13. Altmetric Badge
    Chapter 12 Semantic Similarity in the Gene Ontology
  14. Altmetric Badge
    Chapter 13 Gene-Category Analysis
  15. Altmetric Badge
    Chapter 14 Gene Ontology: Pitfalls, Biases, and Remedies
  16. Altmetric Badge
    Chapter 15 Visualizing GO Annotations
  17. Altmetric Badge
    Chapter 16 A Gene Ontology Tutorial in Python
  18. Altmetric Badge
    Chapter 17 Annotation Extensions
  19. Altmetric Badge
    Chapter 18 The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations
  20. Altmetric Badge
    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
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

21 X users
1 Facebook page


73 Dimensions

Readers on

115 Mendeley
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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

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


Christophe Dessimoz, Nives Škunca


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 21 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
France 1 <1%
Mexico 1 <1%
Netherlands 1 <1%
Unknown 108 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 37%
Researcher 23 20%
Student > Master 13 11%
Student > Bachelor 7 6%
Student > Doctoral Student 5 4%
Other 9 8%
Unknown 16 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 42%
Biochemistry, Genetics and Molecular Biology 28 24%
Immunology and Microbiology 6 5%
Neuroscience 4 3%
Computer Science 4 3%
Other 8 7%
Unknown 17 15%
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
of 22,899,952 outputs
Outputs from Methods in molecular biology
of 13,134 outputs
Outputs of similar age
of 420,444 outputs
Outputs of similar age from Methods in molecular biology
of 1,074 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,134 research outputs from this source. They receive a mean Attention Score of 3.4. 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 420,444 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 87% of its contemporaries.
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 done particularly well, scoring higher than 93% of its contemporaries.