null null et al. (2023), The Gene Ontology knowledgebase in 2023.

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Genetics 2023 May 04;2241:. doi: 10.1093/genetics/iyad031.

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The Gene Ontology knowledgebase in 2023.

Gene Ontology Consortium , Aleksander SA , Balhoff J , Carbon S , Cherry JM , Drabkin HJ , Ebert D , Feuermann M , Gaudet P , Harris NL , Hill DP , Lee R , Mi H , Moxon S , Mungall CJ , Muruganugan A , Mushayahama T , Sternberg PW , Thomas PD , Van Auken K , Ramsey J , Siegele DA , Chisholm RL , Fey P , Aspromonte MC , Nugnes MV , Quaglia F , Tosatto S , Giglio M , Nadendla S , Antonazzo G , Attrill H , Dos Santos G , Marygold S , Strelets V , Tabone CJ , Thurmond J , Zhou P , Ahmed SH , Asanitthong P , Luna Buitrago D , Erdol MN , Gage MC , Ali Kadhum M , Li KYC , Long M , Michalak A , Pesala A , Pritazahra A , Saverimuttu SCC , Su R , Thurlow KE , Lovering RC , Logie C , Oliferenko S , Blake J , Christie K , Corbani L , Dolan ME , Drabkin HJ , Hill DP , Ni L , Sitnikov D , Smith C , Cuzick A , Seager J , Cooper L , Elser J , Jaiswal P , Gupta P , Jaiswal P , Naithani S , Lera-Ramirez M , Rutherford K , Wood V , De Pons JL , Dwinell MR , Hayman GT , Kaldunski ML , Kwitek AE , Laulederkind SJF , Tutaj MA , Vedi M , Wang SJ , D'Eustachio P , Aimo L , Axelsen K , Bridge A , Hyka-Nouspikel N , Morgat A , Aleksander SA , Cherry JM , Engel SR , Karra K , Miyasato SR , Nash RS , Skrzypek MS , Weng S , Wong ED , Bakker E , Berardini TZ , Reiser L , Auchincloss A , Axelsen K , Argoud-Puy G , Blatter MC , Boutet E , Breuza L , Bridge A , Casals-Casas C , Coudert E , Estreicher A , Livia Famiglietti M , Feuermann M , Gos A , Gruaz-Gumowski N , Hulo C , Hyka-Nouspikel N , Jungo F , Le Mercier P , Lieberherr D , Masson P , Morgat A , Pedruzzi I , Pourcel L , Poux S , Rivoire C , Sundaram S , Bateman A , Bowler-Barnett E , Bye-A-Jee H , Denny P , Ignatchenko A , Ishtiaq R , Lock A , Lussi Y , Magrane M , Martin MJ , Orchard S , Raposo P , Speretta E , Tyagi N , Warner K , Zaru R , Diehl AD , Lee R , Chan J , Diamantakis S , Raciti D , Zarowiecki M , Fisher M , James-Zorn C , Ponferrada V , Zorn A , Ramachandran S , Ruzicka L , Westerfield M .

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The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

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Species referenced: Xenopus Xenopus tropicalis
Genes referenced: chd4

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	Fig. 1. Examples of the three components of the GO knowledgebase. a) The GO ontology consists of terms, e.g. DNA binding transcription factor activity, and relationships between the terms (arrows; black = is a, blue = part of, and orange = regulates). b) GO annotations associate a specific gene product (here, human ZNF410) with GO terms asserting its functional aspects (“GO Class” column, e.g. sequence-specific double-stranded DNA binding) and the evidence for each assertion with its traceable source (“Evidence” and “Reference” columns). c) The GO-CAM model combines individual GO annotations into a model, in this case a very simple model describing how human ZNF410 acts as a transcription factor to positively regulate (denoted by the arrow) transcription of the CHD4 gene, which in turn acts as a corepressor to repress (denoted by dashed lines) transcription of fetal hemoglobin genes (HBG1 and HBG2) in erythroid lineage cells. In this view, each box in the GO-CAM is labeled with the gene product and species abbreviation for simplicity.
	Fig. 2. GO-CAM model of the SARS-CoV2—host interactions as displayed using the GO-CAM Pathway Widget (code available at https://github.com/geneontology/wc-gocam-viz) on the Alliance of Genome Resources gene pages (https://www.alliancegenome.org/gene/HGNC:20144#pathways). The model includes proteins from both humans (Hsap) and the SARS-CoV-2 virus (Scov2). A simplified representation of the causal model is shown on the main figure, which is simplified by labeling each activity with the gene and organism. The model includes many additional details, which are displayed as “cards;” the information for MAVS activity (inset) which normally acts as a signaling adaptor located in the mitochondrial membrane. MAVS activity is suppressed directly by the SARS-CoV-2M protein and indirectly by other SARS-CoV-2 proteins. Each of the “E” symbols on the right-hand side can be clicked to see the evidence for each assertion in the model.
	Fig. 3. Alliance ribbon view for the yeast RPB7 gene. High-level GO categories annotated are shown in shaded squares (https://www.alliancegenome.org/gene/SGD:S000002812); darker shading indicates more annotations in that category.

References [+] :

Alliance of Genome Resources Consortium, Harmonizing model organism data in the Alliance of Genome Resources. 2022, Pubmed