Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024:2836:285-298.
doi: 10.1007/978-1-0716-4007-4_15.

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods

Affiliations

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods

Marc Feuermann et al. Methods Mol Biol. 2024.

Abstract

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to convert experimental results into GO annotations that systematically classify gene products' functions. However, to address the fact that only a minor fraction of all genes has been characterized experimentally, multiple predictive methods to assign GO annotations have been developed since the inception of GO. Sequence homologies between novel genes and genes with known functions help to approximate the roles of these non-characterized genes. Here we describe the main sequence homology methods to produce annotations: pairwise comparison (BLAST), protein profile models (InterPro), and phylogenetic-based annotation (PAINT). Some of these methods can be implemented with genome analysis pipelines (BLAST and InterPro2GO), while PAINT is curated by the GO consortium.

Keywords: Gain of function; Gene ontology; Homology annotation; Loss of function; Phylogenetic annotation.

PubMed Disclaimer

References

    1. Gaudet P, Škunca N, Hu JC et al (2017) Primer on the gene ontology. In: Dessimoz C, Škunca N (eds) The gene ontology handbook. Springer, New York, pp 25–37. https://doi.org/10.1007/978-1-4939-3743-1_3 - DOI
    1. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:15545–15550. https://doi.org/10.1073/pnas.0506580102 - DOI - PubMed
    1. Bult CJ, Sternberg PW (2023) The alliance of genome resources: transforming comparative genomics. Mamm Genome 34:531–544. https://doi.org/10.1007/s00335-023-10015- - DOI - PubMed - PMC
    1. Li W, Freudenberg J, Oswald M (2015) Principles for the organization of gene-sets. Comput Biol Chem 59(Pt B):139–149. https://doi.org/10.1016/j.compbiolchem.2015.04.005 - DOI - PubMed
    1. Poux S, Gaudet P (2017) Best practices in manual annotation with the gene ontology. In: Dessimoz C, Škunca N (eds) The gene ontology handbook. Springer, New York, pp 41–54. https://doi.org/10.1007/978-1-4939-3743-1_4 - DOI

LinkOut - more resources