ArticleGigaScience2014
Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt.
Article in GigaScience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
45 citing papers in PubMed.
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- Integrated analysis identifies conserved transcription factors TCF12, E2F1, and TEAD4 with diagnostic and therapeutic value in osteoarthritis subchondral bone.Genes & genomics · 2026Article
- Structural Changes in Gene Ontology Reveal Modular and Complex Representations of Biological Function.Molecular biology and evolution · 2025Article
- A longitudinal analysis of function annotations of the human proteome reveals consistently high biases.Database : the journal of biological databases and curation · 2025Article
- Why an integrated view of gene expression studies on hematopoiesis in mouse aging is better than the sum of their parts.FEBS letters · 2024Review
- Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.Bioinformatics (Oxford, England) · 2024Article
- Calciprotein Particles Induce Cellular Compartment-Specific Proteome Alterations in Human Arterial Endothelial Cells.Journal of cardiovascular development and disease · 2023Article
- The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases.Biomolecules · 2022Article
- Article
- Hymenoptera Genome Database: new genomes and annotation datasets for improved go enrichment and orthologue analyses.Nucleic acids research · 2022Article
- Exploiting protein family and protein network data to identify novel drug targets for bladder cancer.Oncotarget · 2022Article
- Predicting functions of maize proteins using graph convolutional network.BMC bioinformatics · 2020Article
- Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons.Bioinformatics (Oxford, England) · 2020Article
- Computational Methods for Predicting Functions at the mRNA Isoform Level.International journal of molecular sciences · 2020Review
- Benchmarking gene ontology function predictions using negative annotations.Bioinformatics (Oxford, England) · 2020Article
- A Literature Review of Gene Function Prediction by Modeling Gene Ontology.Frontiers in genetics · 2020Review
- Identification of Proteins Differentially Expressed by Adipose-derived Mesenchymal Stem Cells Isolated from Immunodeficient Mice.International journal of molecular sciences · 2019Article
- Zinc intoxication induces ferroptosis in A549 human lung cells.Metallomics : integrated biometal science · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The Gene Ontology Consortium (GOC) is a major bioinformatics project that provides structured controlled vocabularies to classify gene product function and location. GOC members create annotations to gene products using the Gene Ontology (GO) vocabularies, thus providing an extensive, publicly available resource. The GO and its annotations to gene products are now an integral part of functional analysis, and statistical tests using GO data are becoming routine for researchers to include when publishing functional information. While many helpful articles about the GOC are available, there are certain updates to the ontology and annotation sets that sometimes go unobserved. Here we describe some of the ways in which GO can change that should be carefully considered by all users of GO as they may have a significant impact on the resulting gene product annotations, and therefore the functional description of the gene product, or the interpretation of analyses performed on GO datasets. GO annotations for gene products change for many reasons, and while these changes generally improve the accuracy of the representation of the underlying biology, they do not necessarily imply that previous annotations were incorrect. We additionally describe the quality assurance mechanisms we employ to improve the accuracy of annotations, which necessarily changes the composition of the annotation sets we provide. We use the Universal Protein Resource (UniProt) for illustrative purposes of how the GO Consortium, as a whole, manages these changes.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.