ArticleMethods in molecular biology (Clifton, N.J.)2017
Evaluating Functional Annotations of Enzymes Using the Gene Ontology.
Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
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- Sequence and taxonomic feature evaluation facilitated the discovery of alcohol oxidases.Synthetic and systems biotechnology · 2025Article
- Development of a novel diagnostic model for Alzheimer's disease based on glymphatic system and metabolism-related genes.Frontiers in molecular biosciences · 2025Article
- Insights from the reanalysis of high-throughput chemical genomics data for Escherichia coli K-12.G3 (Bethesda, Md.) · 2021Article
- Detecting and correcting misclassified sequences in the large-scale public databases.Bioinformatics (Oxford, England) · 2020Article
- Identification of mammalian glycoproteins with type-I LacdiNAc structures synthesized by the glycosyltransferase B3GALNT2.The Journal of biological chemistry · 2019Article
- The CaspBase: a curated database for evolutionary biochemical studies of caspase functional divergence and ancestral sequence inference.Protein science : a publication of the Protein Society · 2018Article
- Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a "Plug and Play" Domain.Methods in enzymology · 2018Article
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Authors and funding
4 authors.
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Abstract
The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our 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) as a guide for informed utilisation of annotation transfer based on GO terms.
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