Expansion of the Gene Ontology knowledgebase and resources
By Gene Ontology Consortium. 2016
1,784 citations recorded by the source corpus.
The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/.
Subfields: Molecular Biology
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In Molecular Biology
- Paul D. Thomas
- Anushya Muruganujan
- Huaiyu Mi
- Xiaosong Huang
- Caitlin Mills
- Haiming Tang
- Malte D. Luecken
- Diane Kang
Citations in the corpus
- PubChem 2019 update: improved access to chemical data (2018, neutral): “These annotations are collected from major molecular biology databases, including NCBI Gene (12), Gene Ontology (13,14), Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) (15), UniProt (16), Protein Data Bank (PDB) (17), Conserved Domains Database (CDD) (18), Pfam (19), MedGen…”
- Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program (2019, background): “%) in gene sets (that is, terms) from Gene Ontology (GO) 87,88 .”
- Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Future developments in this field will likely borrow from the mass cytometry (e.g. Tibshirani et al , 2002; Arvaniti & Claassen, 2017; Lun et al , 2017; Weber et al , 2018) or the microbiome literature (Gloor et al , 2017), where compositional data analysis has received more attention.”
- PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements (2016, methodology): “It is a member in the InterPro Consortium of protein classification resources (9); PANTHER HMMs can be searched from the InterPro website and us-ing the InterProScan tool.”