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The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/.
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The Gene Ontology knowledgebase in 2023
Abstract The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.
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- Award ID(s):
- 2039324
- PAR ID:
- 10496319
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- GENETICS
- Volume:
- 224
- Issue:
- 1
- ISSN:
- 1943-2631
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Premise The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre‐defined, higher‐order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually. Methods and Results The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s). Conclusions GO annotations are a widely used “universal language” for describing gene functions and products. GOgetter is a fast and easy‐to‐implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.more » « less
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Abstract Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non‐homology gene features. Among the eight supervised classification algorithms evaluated, random‐forest‐based prediction consistently provided the most accurate gene function prediction. Non‐homology‐based functional annotation provides complementary strengths to homology‐based annotation, with higher average performance in Biological Process GO terms, the domain where homology‐based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology‐based functional annotation is highest. GO prediction models trained with homology‐based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non‐homology‐based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology‐based functional annotations.more » « less
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Fiston-Lavier, Anna-Sophie (Ed.)Abstract SummaryUnderstanding the pathways and biological processes underlying differential gene expression is fundamental for characterizing gene expression changes in response to an experimental condition. Zebrafish, with a transcriptome closely mirroring that of humans, are frequently utilized as a model for human development and disease. However, a challenge arises due to the incomplete annotations of zebrafish pathways and biological processes, with more comprehensive annotations existing in humans. This incompleteness may result in biased functional enrichment findings and loss of knowledge. danRerLib, a versatile Python package for zebrafish transcriptomics researchers, overcomes this challenge and provides a suite of tools to be executed in Python including gene ID mapping, orthology mapping for the zebrafish and human taxonomy, and functional enrichment analysis utilizing the latest updated Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. danRerLib enables functional enrichment analysis for GO and KEGG pathways, even when they lack direct zebrafish annotations through the orthology of human-annotated functional annotations. This approach enables researchers to extend their analysis to a wider range of pathways, elucidating additional mechanisms of interest and greater insight into experimental results. Availability and implementationdanRerLib, along with comprehensive documentation and tutorials, is freely available. The source code is available at https://github.com/sdsucomptox/danrerlib/ with associated documentation and tutorials at https://sdsucomptox.github.io/danrerlib/. The package has been developed with Python 3.9 and is available for installation on the package management systems PIP (https://pypi.org/project/danrerlib/) and Conda (https://anaconda.org/sdsu_comptox/danrerlib) with additional installation instructions on the documentation website.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/genetics/iyad031
