ArticlePLoS computational biology2012
Quality of computationally inferred gene ontology annotations.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed.
- Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026Article
- Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0.Briefings in bioinformatics · 2025Article
- Article
- Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.Bioinformatics (Oxford, England) · 2024Article
- Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data.Journal of neurodevelopmental disorders · 2022Article
- Exploring automatic inconsistency detection for literature-based gene ontology annotation.Bioinformatics (Oxford, England) · 2022Article
- Automatic consistency assurance for literature-based gene ontology annotation.BMC bioinformatics · 2021Article
- Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).PLoS computational biology · 2021Article
- Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain.Cell systems · 2021Article
- PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.PloS one · 2021Article
- Review
- Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.Open biology · 2020Article
- Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons.Bioinformatics (Oxford, England) · 2020Article
- Benchmarking gene ontology function predictions using negative annotations.Bioinformatics (Oxford, England) · 2020Article
- Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes.PLoS computational biology · 2020Article
- The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction.Bioinformatics (Oxford, England) · 2020Article
- Photosynthetic protein classification using genome neighborhood-based machine learning feature.Scientific reports · 2020Article
- Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.Scientific reports · 2020Article
- WildEcology and evolution · 2020Article
- SamPler - a novel method for selecting parameters for gene functional annotation routines.BMC bioinformatics · 2019Article
13 more citing papers are in PubMed but not listed here.
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only 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. This work provides the means to identify the subset of electronic annotations that can be relied upon-an important outcome given that >98% of all annotations are inferred without direct curation.
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