ArticleBMC bioinformatics2014
A method for increasing expressivity of Gene Ontology annotations using a compositional approach.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
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- Automated descriptive cell type naming in flow and mass cytometry with CytoPheno.Scientific reports · 2025Article
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- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage.Nature methods · 2025Article
- Characterization and automated classification of sentences in the biomedical literature: a case study for biocuration of gene expression and protein kinase activity.Database : the journal of biological databases and curation · 2025Article
- Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data.bioRxiv : the preprint server for biology · 2024Article
- Discovery ofJournal of fungi (Basel, Switzerland) · 2024Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.bioRxiv : the preprint server for biology · 2024Article
- Review
- The OREGANO knowledge graph for computational drug repurposing.Scientific data · 2023Article
- Article
- Capturing heart valve development with Gene Ontology.Frontiers in genetics · 2023Article
- Insights into the genomic architecture of a newly discovered endophyticFrontiers in microbiology · 2023Article
- Comparative Transcriptome Analysis of Two Chrysomycin-Producing Wild-Type and Mutant Strains ofMetabolites · 2022Article
- Genome Features and AntiSMASH Analysis of an Endophytic StrainMetabolites · 2022Article
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- JaponicusDB: rapid deployment of a model organism database for an emerging model species.Genetics · 2022Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
18 authors.
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Abstract
backgroundThe Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations.
resultsThe GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions.
conclusionsThe additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.
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