The Gene Ontology Handbook

@inproceedings{Dessimoz2017TheGO,
  title={The Gene Ontology Handbook},
  author={Christophe Dessimoz and Nives Skunca},
  booktitle={Methods in Molecular Biology},
  year={2017}
}
As molecular biology has increasingly become a data-intensive discipline, ontologies have emerged as an essential computational tool to assist in the organisation, description and analysis of data. Ontologies describe and classify the entities of interest in a scientifi c domain in a computationally accessible fashion such that algorithms and tools can be developed around them. The technology that underlies ontologies has its roots in logic-based artifi cial intelligence, allowing for… 
Primer on Ontologies.
TLDR
This chapter presents a general introduction to modern computational ontologies as they are used in biology.
Best Practices in Manual Annotation with the Gene Ontology.
TLDR
This chapter describes how biocurators create GO annotations from experimental findings from research articles and describes the current best practices for high-quality literature curation and how GO curators succeed in modeling biology using a relatively simple framework.
Gene-Category Analysis.
TLDR
This chapter motivates this class of analyses and describes an often used variant that is based on Fisher's exact test, and shows that this approach has some problems in the context of Gene Ontology of which users should be aware.
Formal Representations of Ontologies for Automation of Analyses
  • Computer Science
  • 2022
TLDR
An overview of the formal representation of ontologies in ways that support automated reasoning with such representations, and the application of such ontologies together with large ‐ scale sources of data is provided.
The Vision and Challenges of the Gene Ontology.
  • S. Lewis
  • Biology
    Methods in molecular biology
  • 2017
TLDR
The progress that has been made over the years towards this goal, and the work that still remains to be done, to make the Gene Ontology Consortium realize its goal of offering the most comprehensive and up-to-date resource for information on gene function.
Evaluating Functional Annotations of Enzymes Using the Gene Ontology
TLDR
The 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) is described as a guide for informed utilisation of annotation transfer based on GO terms.
Gene Ontology: A Resource for Analysis and Interpretation of Alzheimer’s Disease Data
TLDR
The UCL Functional Gene Annotation group has led initiatives to systematically annotate proteins and microRNAs across specific biomedical fields, and the current biocuration effort is focused on dementia and Alzheimer’s disease.
CirGO: an alternative circular way of visualising gene ontology terms
TLDR
This paper presents an open source CirGO (Circular Gene Ontology) software that visualises non-redundant two-level hierarchically structured ontology terms from gene expression data in a 2D space in an informative, comprehensive and intuitive format.
neXtA5: accelerating annotation of articles via automated approaches in neXtProt
TLDR
The current search methods significantly improve the search effectiveness of curators for three important curation axes, compared with PubMed, and in particular protein–protein interactions, which require specific relationship extraction capabilities.
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References

SHOWING 1-10 OF 56 REFERENCES
Gene Ontology: tool for the unification of biology
TLDR
The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Primer on the Gene Ontology.
TLDR
This chapter provides a concise primer for all users of the Gene Ontology, briefly introducing the structure of the ontology and explaining how to interpret annotations associated with the GO.
The Gene Ontology and the Meaning of Biological Function.
  • P. Thomas
  • Biology
    Methods in molecular biology
  • 2017
TLDR
An explicit formulation of the biological model that underlies the GO and annotations is presented, and how this model relates to the broader debates on the meaning of biological function is discussed.
Semantic Similarity in Biomedical Ontologies
TLDR
This work reviews semantic similarity measures applied to biomedical ontologies and proposes their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise.
The Confidence Information Ontology: a step towards a standard for asserting confidence in annotations
TLDR
This work presents the elements that were identified as essential for assessing confidence in annotations, as well as a draft ontology—the Confidence Information Ontology—to illustrate how the problems identified could be addressed.
Community-Wide Evaluation of Computational Function Prediction.
TLDR
The rationale, benefits, and issues associated with evaluating computational methods in an ongoing community-wide challenge involving a broad scientific community according to their ability to predict the associations between previously unannotated protein sequences and Gene Ontology terms are discussed.
Quality of Computationally Inferred Gene Ontology Annotations
TLDR
Overall, it is found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature.
Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms
TLDR
The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes.
The InterPro protein families database: the classification resource after 15 years
TLDR
The new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined, and the challenges faced by the resource given the explosive growth in sequence data in recent years are discussed.
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