Suzi AleksanderpersName, James P. BalhoffpersName, Seth CarbonpersName, J. Michael CherrypersName, Harold DrabkinpersName, Dustin EbertpersName, Marc FeuermannpersName, Pascale GaudetpersName, Nomi L. HarrispersName, David P. HillpersName, Raymond LeepersName, Huaiyu MipersName, Sierra MoxonpersName, Chris MungallpersName, Anushya MuruganuganpersName, Tremayne MushayahamapersName, Paul W. SternbergpersName, Paul D. ThomaspersName, Kimberly Van AukenpersName, Jolene RamseypersName, Deborah A. SiegelepersName, Rex L. ChisholmpersName, Petra FeypersName, Maria Cristina AspromontepersName, María Victoria NugnespersName, Federica QuagliapersName, Silvio C. E. TosattopersName, Michelle GigliopersName, Suvarna NadendlapersName, Giulia AntonazzopersName, Helen AttrillpersName, Gil dos SantospersName, Steven J MarygoldpersName, Victor StreletspersName, Christopher J. TabonepersName, Jim ThurmondpersName, Pinglei ZhoupersName, Saadullah H. AhmedpersName, Praoparn AsanitthongpersName, Diana Luna BuitragopersName, Meltem N ErdolpersName, Matthew GagepersName, Mohamed Ali KadhumpersName, Kan Yan Chloe LipersName, Miao LongpersName, Aleksandra MichalakpersName, Angeline PesalapersName, Armalya PritazahrapersName, Shirin C C SaverimuttupersName, Renzhi SupersName, Kate E. ThurlowpersName, Ruth C. LoveringpersName, Colin LogiepersName, Snezhana OliferenkopersName, Judith A. BlakepersName, Karen ChristiepersName, Lori E CorbanipersName, M. Eileen DolanpersName, Harold DrabkinpersName, David P. HillpersName, Li NipersName, Dmitry SitnikovpersName, Cynthia L. SmithpersName, Alayne CuzickpersName, James SeagerpersName, Laurel CooperpersName, Justin ElserpersName, Pankaj JaiswalpersName, Parul GuptapersName, Pankaj JaiswalpersName, Sushma NaithanipersName, Manuel Lera-RamírezpersName, Kim RutherfordpersName, Valerie WoodpersName, Jeffrey L De PonspersName, Melinda R. DwinellpersName, G. Thomas HaymanpersName, Mary L. KaldunskipersName, Anne E. KwitekpersName, Stanley J. F. LaulederkindpersName, Marek TutajpersName, Mahima VedipersName, Shur‐Jen WangpersName, Peter D’EustachiopersName, Lucila AimopersName, Kristian B. AxelsenpersName, Alan BridgepersName, Nevila Hyka‐NouspikelpersName, Anne MorgatpersName, Suzi AleksanderpersName, J. Michael CherrypersName, Stacia R. EngelpersName, Kalpana KarrapersName, Stuart R. MiyasatopersName, Robert S NashpersName, Marek S. SkrzypekpersName, Shuai WengpersName, Edith D. WongpersName, Erika BakkerpersName, Tanya BerardinipersName, Leonore ReiserpersName, Andrea H AuchinclosspersName, Kristian B. AxelsenpersName, Ghislaine Argoud‐PuypersName, Marie-Claude BlatterpersName, Emmanuel BoutetpersName, Lionel BreuzapersName, Alan BridgepersName, Cristina Casals‐CasaspersName, Elisabeth CoudertpersName, Anne EstreicherpersName, Maria Livia FamigliettipersName, Marc FeuermannpersName, Arnaud GospersName, Nadine Gruaz-GumowskipersName, Chantal HulopersName, Nevila Hyka‐NouspikelpersName, Florence JungopersName, Philippe Le MercierpersName, Damien LieberherrpersName, Patrick MassonpersName, Anne MorgatpersName, Ivo PedruzzipersName, Lucille PourcelpersName, Sylvain PouxpersName, Catherine RivoirepersName, Shyamala SundarampersName, Alex BatemanpersName, Emily Bowler-BarnettpersName, Hema Bye‐A‐JeepersName, Paul DennypersName, Alexandr IgnatchenkopersName, Rizwan IshtiaqpersName, Antonia LockpersName, Yvonne LussipersName, Michele MagranepersName, María MartinpersName, Sandra OrchardpersName, Pedro RaposopersName, Elena SperettapersName, Nidhi TyagipersName, Kate WarnerpersName, Rossana ZarupersName, Alexander D. DiehlpersName, Raymond LeepersName, Juancarlos ChanpersName, Stavros DiamantakispersName, Daniela RacitipersName, Magdalena ZarowieckipersName, Malcolm E FisherpersName, Christina James‐ZornpersName, Virgilio PonferradapersName, Aaron M. ZornpersName, Sridhar RamachandranpersName, Leyla RuzickapersName, Monte WesterfieldpersName, Suzi AleksanderpersName, James P. BalhoffpersName, Seth CarbonpersName, J. Michael CherrypersName, Harold DrabkinpersName, Dustin EbertpersName, Marc FeuermannpersName, Pascale GaudetpersName, Nomi L. HarrispersName, David P. HillpersName, Raymond LeepersName, Huaiyu MipersName, Sierra MoxonpersName, Chris MungallpersName, Anushya MuruganuganpersName, Tremayne MushayahamapersName, Paul W. SternbergpersName, Paul D. ThomaspersName, Kimberly Van AukenpersName, Jolene RamseypersName, Deborah A. SiegelepersName, Rex L. ChisholmpersName, Petra FeypersName, Maria Cristina AspromontepersName, María Victoria NugnespersName, Federica QuagliapersName, Silvio C. E. TosattopersName, Michelle GigliopersName, Suvarna NadendlapersName, Giulia AntonazzopersName, Helen AttrillpersName, Gil dos SantospersName, Steven J MarygoldpersName, Victor StreletspersName, Christopher J. TabonepersName, Jim ThurmondpersName, Pinglei ZhoupersName, Saadullah H. AhmedpersName, Praoparn AsanitthongpersName, Diana Luna BuitragopersName, Meltem N ErdolpersName, Matthew GagepersName, Mohamed Ali KadhumpersName, Kan Yan Chloe LipersName, Miao LongpersName, Aleksandra MichalakpersName, Angeline PesalapersName, Armalya PritazahrapersName, Shirin C C SaverimuttupersName, Renzhi SupersName, Kate E. ThurlowpersName, Ruth C. LoveringpersName, Colin LogiepersName, Snezhana OliferenkopersName, Judith A. BlakepersName, Karen ChristiepersName, Lori E CorbanipersName, M. Eileen DolanpersName, Harold DrabkinpersName, David P. HillpersName, Li NipersName, Dmitry SitnikovpersName, Cynthia L. SmithpersName, Alayne CuzickpersName, James SeagerpersName, Laurel CooperpersName, Justin ElserpersName, Pankaj JaiswalpersName, Parul GuptapersName, Pankaj JaiswalpersName, Sushma NaithanipersName, Manuel Lera-RamírezpersName, Kim RutherfordpersName, Valerie WoodpersName, Jeffrey L De PonspersName, Melinda R. DwinellpersName, G. Thomas HaymanpersName, Mary L. KaldunskipersName, Anne E. KwitekpersName, Stanley J. F. LaulederkindpersName, Marek TutajpersName, Mahima VedipersName, Shur‐Jen WangpersName, Peter D’EustachiopersName, Lucila AimopersName, Kristian B. AxelsenpersName, Alan BridgepersName, Nevila Hyka‐NouspikelpersName, Anne MorgatpersName, Suzi AleksanderpersName, J. Michael CherrypersName, Stacia R. EngelpersName, Kalpana KarrapersName, Stuart R. MiyasatopersName, Robert S NashpersName, Marek S. SkrzypekpersName, Shuai WengpersName, Edith D. WongpersName, Erika BakkerpersName, Tanya BerardinipersName, Leonore ReiserpersName, Andrea H AuchinclosspersName, Kristian B. AxelsenpersName, Ghislaine Argoud‐PuypersName, Marie-Claude BlatterpersName, Emmanuel BoutetpersName, Lionel BreuzapersName, Alan BridgepersName, Cristina Casals‐CasaspersName, Elisabeth CoudertpersName, Anne EstreicherpersName, Maria Livia FamigliettipersName, Marc FeuermannpersName, Arnaud GospersName, Nadine Gruaz-GumowskipersName, Chantal HulopersName, Nevila Hyka‐NouspikelpersName, Florence JungopersName, Philippe Le MercierpersName, Damien LieberherrpersName, Patrick MassonpersName, Anne MorgatpersName, Ivo PedruzzipersName, Lucille PourcelpersName, Sylvain PouxpersName, Catherine RivoirepersName, Shyamala SundarampersName, Alex BatemanpersName, Emily Bowler-BarnettpersName, Hema Bye‐A‐JeepersName, Paul DennypersName, Alexandr IgnatchenkopersName, Rizwan IshtiaqpersName, Antonia LockpersName, Yvonne LussipersName, Michele MagranepersName, María MartinpersName, Sandra OrchardpersName, Pedro RaposopersName, Elena SperettapersName, Nidhi TyagipersName, Kate WarnerpersName, Rossana ZarupersName, Alexander D. DiehlpersName, Raymond LeepersName, Juancarlos ChanpersName, Stavros DiamantakispersName, Daniela RacitipersName, Magdalena ZarowieckipersName, Malcolm E FisherpersName, Christina James‐ZornpersName, Virgilio PonferradapersName, Aaron M. ZornpersName, Sridhar RamachandranpersName, Leyla RuzickapersName, Monte WesterfieldpersName (2023date). “The Gene Ontology knowledgebase in 2023.”
Object of study — A CURATED, VERSIONED, EVIDENCE-BEARING KNOWLEDGEBASE OF WHAT GENES DO - and the rules by which its terms are added, merged, obsoletedterm and traced back to the paper that justified them
Research question — what the Gene Ontology knowledgebase currently contains, how it has changed in two years, and how users should use it
Contribution — a status report on the ontology, the annotations and the causal activity models, with the change counts per aspect and the obsoletion criteria stated
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | gene function as a thing that must be represented, evidenced, versioned and sometimes withdrawn when the paper behind it fails — “GO terms created from articles that have been retracted and for which no other supporting evidence exists are obsoleted, for example CDP-acylglycerol O-arachidonoyltransferase activity (GO:0047193) ( Thompson and Zuk 1983 ). Some terms have been obsoleted from authoritative databases, for example EC:1.3.1.59 was removed from the Enzyme Commission database, and the corresponding” | |
| claim-type | method — “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.” | |
| method — evidence | — “Additional revisions are initiated by internal review, and consistency and quality assurance checks. Revisions are also made following feedback from users. Whenever possible, changes are performed in collaboration with expert biocurators or domain specialists; recent examples include blood” | |
| object of study — evidence | — “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.” | |
| research question — evidence | — “For applications that use the components of the GO knowledgebase, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic (ontology, annotations, GO-CAMs, links to external ontologies, etc.), and citable, versioned updates are released on a monthly basis.” | |
| contribution — evidence | — “At each release, we track all changes and report on our website the number of added, obsoleted, and merged terms in the ontology. Table 1 shows the number of GO terms added and removed (merged or obsoleted) over the past two-year period, for each aspect of GO. In the MF and CC aspects of the ontology, term creation versus term obsoletion have approximately balanced each other, such that the number of terms in these two branches has remained roughly constant. The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms.” | |
| mechanism | THE STRUCTURE IS OURS, THREE LAYERS DEEP: `(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 - mechanistic models ... created by linking multiple GO annotations using defined relations`. An ontology, evidence-bearing tags, and typed links between tags. Their relation names are `is a`, `part of` and `regulates` — “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.” | mechanisms[0] |
| mechanism | AND ONE TAG CARRIES ONE CLAIM, WHICH IS OUR RULE TOO: `Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product` — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | mechanisms[1] |
| mechanism | AND THEY HAVE THIS PROJECT'S PROBLEM AND HAVE SOLVED IT THE SAME WAY: `GO terms created from articles that have been retracted and for which no other supporting evidence exists are obsoleted`. A RETRACTION DOES NOT KILL THE TERM. A RETRACTION THAT LEAVES THE TERM UNGROUNDED KILLS IT. Arrived at independently by a twenty-three-year-old consortium — “GO terms created from articles that have been retracted and for which no other supporting evidence exists are obsoleted, for example CDP-acylglycerol O-arachidonoyltransferase activity (GO:0047193) ( Thompson and Zuk 1983 ). Some terms have been obsoleted from authoritative databases, for example EC:1.3.1.59 was removed from the Enzyme Commission database, and the corresponding” | mechanisms[2] |
| mechanism | AND AN ERRATUM CASCADES THROUGH THE VOCABULARY, WITH EVERY CASUALTY NAMED: `alpha-taxilin was originally thought to be the high-molecular weight interleukin-14; an erratum was later published indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14 HAVE BEEN OBSOLETED [interleukin-14 binding (GO:0019974), interleukin-14 production (GO:0032617), AND 5 OTHER TERMS]`. ONE ERRATUM, SEVEN TERMS, EACH WITH ITS ACCESSION — “alpha-taxilin (UniProt: P40222 ) was originally thought to be the high-molecular weight interleukin-14 ( Ambrus et al. 1993 ); an erratum was later published ( Ambrus et al. 1996 ) indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14 have been obsoleted [ interleukin-14 binding (GO:0019974), interleukin-14 production (GO:0032617), and 5 other terms].” | mechanisms[3] |
| extends | Ashburner et al. 2000's molecular biology paradigm, which supplies GO's three aspects and is still the basic model twenty-three years later — “The basic model underlying GO is the “molecular biology paradigm” ( Ashburner et al. 2000 ; Thomas 2017 ), in which there are three types (aspects) of functional characteristics used to describe gene function: Molecular function (MF): the activities performed by a gene product at the molecular level Cellular component (CC): the locations, relative to cellular structures, where MFs are performed Biological process (BP): a “biological program” comprising molecular activities acting in concert to achieve a particular outcome; this program can be at the cellular level or at the organism level of multicellular organisms.” | claim_relations[0] |
| supports | the position that a knowledge resource must be curated and versioned rather than accumulated - 'not just an archive of all public data' — “For applications that use the components of the GO knowledgebase, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic (ontology, annotations, GO-CAMs, links to external ontologies, etc.), and citable, versioned updates are released on a monthly basis.” | claim_relations[1] |
| sense | `GO annotation` - `an association between a specific gene (or gene product) and a GO term ... interpreted as a statement that the specified gene product possesses the specified functional characteristic`, and `Each GO annotation includes the evidence upon which it is based`. One claim, one evidence, one tag — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | senses[0] |
| sense | `obsoleted` - RETIRED BUT NOT DELETED, on three stated grounds: `Terms may be obsoleted when unused or inconsistently used in annotation, when they are redundant with other terms, or during revision of specific branches`. Plus two more the paper demonstrates: an ungrounded retraction, and being beyond the ontology's scope — “The GO is subject to constant review and revision to most accurately model the current biological knowledge. Revision of the ontology includes the addition or obsoletion of terms and reorganization of the relationship structure. New GO terms are added to represent concepts previously missing from the GO in response to published findings, or when a branch of GO is revised. Terms may be obsoleted when unused or inconsistently used in annotation, when they are redundant with other terms, or during revision of specific branches of the ontology.” | senses[1] |
| sense | `subclass` = `one term being more specific than another term`, and the paper gives both names in one clause: `such as one term being more specific than another term (ALSO CALLED SUBCLASS)`. A relation named twice, once relationally and once formally — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | senses[2] |
| sense | the three ASPECTS as senses of `function`: `MOLECULAR FUNCTION - the activities performed by a gene product at the molecular level`; `CELLULAR COMPONENT - the LOCATIONS, relative to cellular structures, where MFs are performed`; `BIOLOGICAL PROCESS - a biological program comprising molecular activities acting in concert to achieve a particular outcome`. An activity, a place and a programme, all called `function` — “The basic model underlying GO is the “molecular biology paradigm” ( Ashburner et al. 2000 ; Thomas 2017 ), in which there are three types (aspects) of functional characteristics used to describe gene function: Molecular function (MF): the activities performed by a gene product at the molecular level Cellular component (CC): the locations, relative to cellular structures, where MFs are performed Biological process (BP): a “biological program” comprising molecular activities acting in concert to achieve a particular outcome; this program can be at the cellular level or at the organism level of multicellular organisms.” | senses[3] |
| sense | `the GO` - THE SAME TWO LETTERS NAME THE WHOLE AND ONE OF ITS THREE PARTS. `The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure ...; (2) GO annotations—...; and (3) GO Causal Activity Models (GO-CAMs)`. `GO` alone is the ontology; `the GO knowledgebase` is the ontology plus the annotations plus the models, and both are written `GO` in ordinary prose. A citation saying `according to GO` cannot be resolved to one or the other without reading the sentence around it. — “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)” | senses[4] |
| sense | Ontology-term granularity - represent an in-vivo specificity range, not every isolated assay substrate — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | senses[5] |
| sense | Contextual substrate activity - capture pathway-specific detail in a GO-CAM or annotation extension rather than a GO term — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | senses[6] |
| sense | Annotation maintenance - edit or remove claims after ontology revision or experimental invalidation, with manual reassignment where possible — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | senses[7] |
| sense | PAN-GO curation - review a whole protein family in its phylogenetic tree and select informative, nonredundant terms — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | senses[8] |
| sense | Phylogenetic propagation - model gains and losses along branches while retaining traceability to experimental evidence — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | senses[9] |
| sense | Direct protein-binding annotation - partner-specific data suited to interaction-network construction but not gene-set enrichment — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | senses[10] |
| sense | Annotation evidence - a code for evidence type plus a persistent-reference provenance field — “All annotations are supported by evidence, comprising two fields in the annotation file ( Fig. 1b ): an evidence code that describes the type of evidence, and a reference that lists a persistent identifier for tracing the source (provenance) of the original data” | senses[11] |
| sense | Evidence reliability - evidence type and annotation review level must be considered jointly — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | senses[12] |
| sense | High-throughput evidence - many-gene experiment annotations not individually reviewed by authors or a GO biocurator — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | senses[13] |
| sense | IBA review - experimental annotation reviewed once directly and again in the context of related genes — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | senses[14] |
| sense | IEA annotation - automated but often pipeline-level expert-reviewed, regarded as accurate though less specific — “While annotations using the Inferred from Electronic Annotation (IEA) evidence code are considered automated, most implement expert review of a subset of annotations to minimize false positives (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurate though they are often less specific than other annotations” | senses[15] |
| sense | GO-CAM - a highly structured model linking gene-product molecular activities by causal relations and adding location and process context — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | senses[16] |
| sense | Gene product activity unit - molecular activity combined with cellular location and larger functional-module context — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | senses[17] |
| sense | Molecular pathway in the reported model count - at least three distinct gene-product activities connected as a causal chain — “As of November 2022, GO Consortium annotation groups have created over 300 GO-CAM models that describe molecular pathways (defined as containing at least three distinct gene product activities linked into a causal chain). These models reflect curation priorities of the contributing groups. Most of the available GO-CAMs are for processes in human or mouse, with a limited number in zebrafish, Drosophila melanogaster , and C. elegans . Many of the human GO-CAMs describe chromatin-mediated regulation of gene expression and immune response pathways, while the mouse GO-CAMs focus on metabolic and signaling pathways. GO-CAMs are accessible from the GO website homepage, by clicking on the “Browse GO-CAMs” link. GO-CAMs can be viewed as pathway diagrams” | senses[18] |
| sense | Intrinsically disordered protein - lacks stable three-dimensional structure and contains flexible, unstructured regions — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | senses[19] |
| sense | Broad GO symbiont - includes pathogens infecting a host, not only mutually beneficial organisms — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | senses[20] |
| sense | Rhea–GO division of labor - overlapping catalytic activities with Rhea carrying more precise participants, stoichiometry, and sometimes specificity — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | senses[21] |
| sense | go-basic - acyclic relation set safe for annotation propagation and recommended for most GO software — “go-basic contains the types of information that has been available for GO from the beginning of the project; hence, it only contains is a, part of, regulates, negatively regulates, and positively regulates relationships and excludes relationships that cross different aspects (BP, MF, or CC) of the ontology. This edition of the ontology is guaranteed to be acyclic and can safely be used to selectively propagate annotations across any relation. It is recommended for most GO-based software tools” | senses[22] |
| sense | go-plus - fully axiomatized GO edition that links into external ontologies — “go additionally includes has part and occurs in relationships that link terms across different aspects of the ontology (for example, a BP can have a has part relation to a MF term or an occurs in relation to a CC). This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO. go-plus is the fully axiomatized edition of the ontology and includes cross-ontology relationships to external ontologies including ChEBI, Cell Ontology, and Uberon” | senses[23] |
| sense | GO subset - tagged condensed selection providing a low-redundancy functional overview — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | senses[24] |
| sense | Homeostasis scale ambiguity - the same term can denote intracellular steady level or organismal blood/fluid regulation — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | senses[25] |
| control | traceability of every annotation to the paper or method behind it — held — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” — rules out unverifiable assertions - 'Because each annotation can be traced to the published paper containing the underlying evidence ... users can quickly verify the accuracy of a given annotation', and the original authors can retrieve and review every annotation made from their own paper — “The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation. Potential errors can be reported by clicking on the “Help” link at the top of the GO homepage ( http://geneontology.org ). In addition, authors of a paper used to create GO annotations can easily retrieve and review all annotations from a given paper and suggest changes; this can be done from the PubMed abstract page” | controls[0] |
| control | obsoletion of terms whose only supporting article has been retracted — held — “alpha-taxilin (UniProt: P40222 ) was originally thought to be the high-molecular weight interleukin-14 ( Ambrus et al. 1993 ); an erratum was later published ( Ambrus et al. 1996 ) indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14 have been obsoleted [ interleukin-14 binding (GO:0019974), interleukin-14 production (GO:0032617), and 5 other terms].” — rules out vocabulary entries with no surviving warrant - and ONLY those: a retracted source obsoletes the term 'for which no other supporting evidence exists' — “GO terms created from articles that have been retracted and for which no other supporting evidence exists are obsoleted, for example CDP-acylglycerol O-arachidonoyltransferase activity (GO:0047193) ( Thompson and Zuk 1983 ). Some terms have been obsoleted from authoritative databases, for example EC:1.3.1.59 was removed from the Enzyme Commission database, and the corresponding” | controls[1] |
| control | internal review, consistency and QA checks, and expert biocurator collaboration — held — “At each release, we track all changes and report on our website the number of added, obsoleted, and merged terms in the ontology. Table 1 shows the number of GO terms added and removed (merged or obsoleted) over the past two-year period, for each aspect of GO. In the MF and CC aspects of the ontology, term creation versus term obsoletion have approximately balanced each other, such that the number of terms in these two branches has remained roughly constant. The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms.” — rules out drift accumulating between releases - 'Additional revisions are initiated by internal review, and consistency and quality assurance checks. Revisions are also made following feedback from users' — “Additional revisions are initiated by internal review, and consistency and quality assurance checks. Revisions are also made following feedback from users. Whenever possible, changes are performed in collaboration with expert biocurators or domain specialists; recent examples include blood” | controls[2] |
| control | whether the knowledge is representative of life rather than of laboratories — absent-declared-unreported — “For applications that use the components of the GO knowledgebase, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic (ontology, annotations, GO-CAMs, links to external ontologies, etc.), and citable, versioned updates are released on a monthly basis.” — rules out nothing, and the abstract says so: 'most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms' — “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.” | controls[3] |
| weight | Ashburner et al. 2000's molecular biology paradigm, which supplies GO's three aspects and is still the basic model twenty-three years later — 0.9 — “The basic model underlying GO is the “molecular biology paradigm” ( Ashburner et al. 2000 ; Thomas 2017 ), in which there are three types (aspects) of functional characteristics used to describe gene function: Molecular function (MF): the activities performed by a gene product at the molecular level Cellular component (CC): the locations, relative to cellular structures, where MFs are performed Biological process (BP): a “biological program” comprising molecular activities acting in concert to achieve a particular outcome; this program can be at the cellular level or at the organism level of multicellular organisms.” | claim_relations[0] |
| weight | the position that a knowledge resource must be curated and versioned rather than accumulated - 'not just an archive of all public data' — 0.85 — “For applications that use the components of the GO knowledgebase, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic (ontology, annotations, GO-CAMs, links to external ontologies, etc.), and citable, versioned updates are released on a monthly basis.” | claim_relations[1] |
| audit | three components —part-of→ GO knowledgebase — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the source endpoint 'three components' is a count, not a term; the span lists the GO knowledgebase's three components by name. — “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.” | concept_edges[0] |
| note | three components —part-of→ GO knowledgebase — an ontology, evidence-bearing tags, and typed links between tags - the same three layers this corpus has, arrived at by a consortium twenty-three years older — “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.” | concept_edges[0] |
| weight | three components —part-of→ GO knowledgebase — 0.95 — “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.” | concept_edges[0] |
| note | GO annotation —is-a→ an association between a specific gene — 'should be interpreted as a statement that the specified gene product possesses the specified functional characteristic' - and each one carries its own evidence, so the association and its warrant travel together — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | concept_edges[1] |
| weight | GO annotation —is-a→ an association between a specific gene — 0.95 — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | concept_edges[1] |
| note | DNA-binding transcription factor activity —is-a→ transcription regulator activity — the paper's demonstration of its own relation type, given in the sentence that defines it — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concept_edges[2] |
| weight | DNA-binding transcription factor activity —is-a→ transcription regulator activity — 0.95 — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concept_edges[2] |
| note | subclass —same-as→ more specific than — 'one term being MORE SPECIFIC THAN another term (ALSO CALLED SUBCLASS)'. The relational description and the formal name, given together - and 'also called' is one of the two 'known as' forms EQUIVALENCE holds, which is why this one is typeable and kuznetsova2014's 'are called' is not — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concept_edges[3] |
| weight | subclass —same-as→ more specific than — 0.95 — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concept_edges[3] |
| note | GO-CAMs —part-of→ GO knowledgebase — 'consists of' is in the containment cue list, so this edge is cue-tested. The whole is named first, the parts second - CONTAINER_FIRST. — “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)” | concept_edges[4] |
| relation | GO-CAMs —part-of→ GO knowledgebase — The GO knowledgebase consists of three components: (1) the GO ... (2) GO annotations ... and (3) GO Causal Activity Models (GO-CAMs) — “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)” | concept_edges[4] |
| note | GO annotations —part-of→ GO knowledgebase — The second component. Both endpoints were already concept nodes; the edge joining them was not there. — “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)” | concept_edges[5] |
| relation | GO annotations —part-of→ GO knowledgebase — The GO knowledgebase consists of three components: (1) the GO ... (2) GO annotations ... and (3) GO Causal Activity Models (GO-CAMs) — “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)” | concept_edges[5] |
| note | GO-CAMs —same-as→ GO Causal Activity Models — The parenthetical first-use expansion directly equates the abbreviation and full name. — “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” | concept_edges[6] |
| note | range of in vivo substrate specificity —distinct-from→ each separate molecular substrate tested in a single, isolated experimental assay or result — The paper explicitly contrasts its current biological-range guidance with earlier assay-by-assay terms. — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concept_edges[7] |
| note | IBA —same-as→ inferred from biological ancestor — The abbreviation is immediately expanded in parentheses. — “PAN-GO annotations are updated at each GO release and are included in the standard, downloadable GO annotation files. These annotations can be identified by the “IBA” (inferred from biological ancestor) evidence code and are available for the 142 organisms included in PANTHER gene families” | concept_edges[10] |
| note | gene set enrichment analysis —contrasted-with→ protein–protein interaction network construction — The former is discouraged and the latter recommended for direct protein-binding annotations. — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | concept_edges[11] |
| note | element of a GO-CAM —is-a→ ontology class — Each model element is explicitly said to be an instance of an ontology class or standard identifier. — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concept_edges[12] |
| note | molecular pathways —operationalised-as→ at least three distinct gene product activities linked into a causal chain — This is the paper's stated definition for counting a GO-CAM as a molecular pathway. — “As of November 2022, GO Consortium annotation groups have created over 300 GO-CAM models that describe molecular pathways (defined as containing at least three distinct gene product activities linked into a causal chain). These models reflect curation priorities of the contributing groups. Most of the available GO-CAMs are for processes in human or mouse, with a limited number in zebrafish, Drosophila melanogaster , and C. elegans . Many of the human GO-CAMs describe chromatin-mediated regulation of gene expression and immune response pathways, while the mouse GO-CAMs focus on metabolic and signaling pathways. GO-CAMs are accessible from the GO website homepage, by clicking on the “Browse GO-CAMs” link. GO-CAMs can be viewed as pathway diagrams” | concept_edges[13] |
| note | pathogens that infect a host organism —part-of→ Symbionts in GO — The GO category is explicitly defined broadly to include infecting pathogens. — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | concept_edges[14] |
| note | cross-ontology relationships —part-of→ go-plus — The fully axiomatized edition includes these links to external ontologies. — “go additionally includes has part and occurs in relationships that link terms across different aspects of the ontology (for example, a BP can have a has part relation to a MF term or an occurs in relation to a CC). This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO. go-plus is the fully axiomatized edition of the ontology and includes cross-ontology relationships to external ontologies including ChEBI, Cell Ontology, and Uberon” | concept_edges[15] |
| note | portion of the terms —part-of→ GO subsets — A subset contains the tag-specified portion of GO terms. — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | concept_edges[16] |
| note | MFs —distinct-from→ BPs — The paper names distinguishing molecular functions from biological processes as an immediate ontology need. — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concept_edges[17] |
| outcome | control — held — “The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation. Potential errors can be reported by clicking on the “Help” link at the top of the GO homepage ( http://geneontology.org ). In addition, authors of a paper used to create GO annotations can easily retrieve and review all annotations from a given paper and suggest changes; this can be done from the PubMed abstract page” — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | controls[0] |
| outcome | control — held — “GO terms created from articles that have been retracted and for which no other supporting evidence exists are obsoleted, for example CDP-acylglycerol O-arachidonoyltransferase activity (GO:0047193) ( Thompson and Zuk 1983 ). Some terms have been obsoleted from authoritative databases, for example EC:1.3.1.59 was removed from the Enzyme Commission database, and the corresponding” — “alpha-taxilin (UniProt: P40222 ) was originally thought to be the high-molecular weight interleukin-14 ( Ambrus et al. 1993 ); an erratum was later published ( Ambrus et al. 1996 ) indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14 have been obsoleted [ interleukin-14 binding (GO:0019974), interleukin-14 production (GO:0032617), and 5 other terms].” | controls[1] |
| outcome | control — held — “Additional revisions are initiated by internal review, and consistency and quality assurance checks. Revisions are also made following feedback from users. Whenever possible, changes are performed in collaboration with expert biocurators or domain specialists; recent examples include blood” — “At each release, we track all changes and report on our website the number of added, obsoleted, and merged terms in the ontology. Table 1 shows the number of GO terms added and removed (merged or obsoleted) over the past two-year period, for each aspect of GO. In the MF and CC aspects of the ontology, term creation versus term obsoletion have approximately balanced each other, such that the number of terms in these two branches has remained roughly constant. The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms.” | controls[2] |
| outcome | control — absent-declared-unreported — “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.” — “For applications that use the components of the GO knowledgebase, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic (ontology, annotations, GO-CAMs, links to external ontologies, etc.), and citable, versioned updates are released on a monthly basis.” | controls[3] |
| weight | causal flow representation → chemical reaction-centered representation — 0.7 — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | unhoused_relations[8] |
| no type for this | PAN-GO annotations → experimental evidence — Every PAN-GO annotation can be traced to experimental evidence in one or more related genes. Traceable-to/provenance-of is absent from the eight edge types. — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | unhoused_relations[0] |
| no type for this | GO evidence codes → Evidence and Conclusion Ontology (ECO) — GO evidence codes correspond to a subset of ECO terms. Maps-to/corresponds-to is absent and is neither identity nor kindhood. — “GO evidence codes correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO) ( Nadendla et al. 2022 ). Combinations of particular GO internal references (GO_REFs) and evidence codes are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO terms to GO evidence code abbreviations can use the mapping file provided by ECO” | unhoused_relations[1] |
| no type for this | GO internal references (GO_REFs) → granular ECO terms — Combinations of GO_REF identifiers and evidence codes are mapped to specific granular ECO terms. Schema lacks a mapping-to relation. — “GO evidence codes correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO) ( Nadendla et al. 2022 ). Combinations of particular GO internal references (GO_REFs) and evidence codes are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO terms to GO evidence code abbreviations can use the mapping file provided by ECO” | unhoused_relations[2] |
| no type for this | gene product activity unit → GO-CAM — The gene product activity unit is the basic unit of a GO-CAM. Unit-of is not admitted by the containment cue vocabulary, so this cannot honestly be forced into part-of. — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | unhoused_relations[3] |
| no type for this | causal relationships → Relations Ontology — The causal relationships linking activity units come from the Relations Ontology. Sourced-from is absent from the eight types. — “Activity units are linked together by causal relationships from the Relations Ontology ( Smith et al. 2005 ) to capture how they interact to impact larger pathways, modules, or processes” | unhoused_relations[4] |
| no type for this | Intrinsically Disordered Proteins Ontology (IDPO) → GO term — The collaboration maps IDPO terms to GO terms whenever possible. Maps-to is absent; same-as would overclaim because some IDPO terms have no GO counterpart. — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | unhoused_relations[5] |
| no type for this | Rhea database of reactions → ChEBI ontology of chemical entities — Rhea uses ChEBI terms to represent chemical entities. Uses-vocabulary-from is absent from the eight relation types. — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | unhoused_relations[6] |
| no type for this | Rhea database of reactions → catalytic activity branch — Rhea overlaps the GO catalytic-activity branch but provides additional reaction detail and sometimes specificity. Overlaps-with is absent, and same-as would erase the stated differences. — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | unhoused_relations[7] |
| no type for this | causal flow representation → chemical reaction-centered representation — The planned GO-CAM representation is complementary to Reactome's reaction-centered representation. Complements is absent; contrasted-with alone would lose the claimed complementarity. — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | unhoused_relations[8] |
| no type for this | homeostasis → level of biological organization — The meaning of homeostasis depends on whether the described steady state is cellular or organismal. Scale-dependent-sense is absent from the eight edge types. — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | unhoused_relations[9] |
| limitation | THE VOCABULARY IS SHRINKING ON PURPOSE, AND THE COUNTS ARE PUBLISHED PER RELEASE: molecular function 315 added / 65 obsoleted / 143 merged; cellular component 34 / 19 / 162; biological process 217 / 782 / 254. `The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms` — “At each release, we track all changes and report on our website the number of added, obsoleted, and merged terms in the ontology. Table 1 shows the number of GO terms added and removed (merged or obsoleted) over the past two-year period, for each aspect of GO. In the MF and CC aspects of the ontology, term creation versus term obsoletion have approximately balanced each other, such that the number of terms in these two branches has remained roughly constant. The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms.” | limitations[0] |
| limitation | and the reason is a stated curation principle: `it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic ... and citable, versioned updates are released on a monthly basis` — “For applications that use the components of the GO knowledgebase, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic (ontology, annotations, GO-CAMs, links to external ontologies, etc.), and citable, versioned updates are released on a monthly basis.” | limitations[1] |
| limitation | THE COVERAGE BIAS IS IN THE ABSTRACT: `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` — “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.” | limitations[2] |
| limitation | AND ONE EDITION IS PUBLISHED WITH A WARNING LABEL: `This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO`. A released artefact that says what it must not be used for — “This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO.” | limitations[3] |
| limitation | and the scope boundary is enforced by obsoletion: two terms `were obsoleted because they represent substeps of reactions which are beyond the scope of GO`. The vocabulary declines to represent something true, on the ground that it is not what this vocabulary is for — “Reaction mechanisms: primary charge separation (GO:0009766) and enzyme active site formation (GO:0018307) were obsoleted because they represent substeps of reactions which are beyond the scope of GO.” | limitations[4] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | — “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.” | content_tags.findings[0] |
| finding | — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | content_tags.findings[1] |
| finding | — “GO terms created from articles that have been retracted and for which no other supporting evidence exists are obsoleted, for example CDP-acylglycerol O-arachidonoyltransferase activity (GO:0047193) ( Thompson and Zuk 1983 ). Some terms have been obsoleted from authoritative databases, for example EC:1.3.1.59 was removed from the Enzyme Commission database, and the corresponding” | content_tags.findings[2] |
| finding | — “alpha-taxilin (UniProt: P40222 ) was originally thought to be the high-molecular weight interleukin-14 ( Ambrus et al. 1993 ); an erratum was later published ( Ambrus et al. 1996 ) indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14 have been obsoleted [ interleukin-14 binding (GO:0019974), interleukin-14 production (GO:0032617), and 5 other terms].” | content_tags.findings[3] |
| finding | — “At each release, we track all changes and report on our website the number of added, obsoleted, and merged terms in the ontology. Table 1 shows the number of GO terms added and removed (merged or obsoleted) over the past two-year period, for each aspect of GO. In the MF and CC aspects of the ontology, term creation versus term obsoletion have approximately balanced each other, such that the number of terms in these two branches has remained roughly constant. The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms.” | content_tags.findings[4] |
| finding | — “For applications that use the components of the GO knowledgebase, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebase are dynamic (ontology, annotations, GO-CAMs, links to external ontologies, etc.), and citable, versioned updates are released on a monthly basis.” | content_tags.findings[5] |
| finding | — “Reaction mechanisms: primary charge separation (GO:0009766) and enzyme active site formation (GO:0018307) were obsoleted because they represent substeps of reactions which are beyond the scope of GO.” | content_tags.findings[6] |
| finding | — “The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation. Potential errors can be reported by clicking on the “Help” link at the top of the GO homepage ( http://geneontology.org ). In addition, authors of a paper used to create GO annotations can easily retrieve and review all annotations from a given paper and suggest changes; this can be done from the PubMed abstract page” | content_tags.findings[7] |
| finding | — “This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO.” | content_tags.findings[8] |
| finding | — “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.” | content_tags.findings[9] |
| finding | — “The basic model underlying GO is the “molecular biology paradigm” ( Ashburner et al. 2000 ; Thomas 2017 ), in which there are three types (aspects) of functional characteristics used to describe gene function: Molecular function (MF): the activities performed by a gene product at the molecular level Cellular component (CC): the locations, relative to cellular structures, where MFs are performed Biological process (BP): a “biological program” comprising molecular activities acting in concert to achieve a particular outcome; this program can be at the cellular level or at the organism level of multicellular organisms.” | content_tags.findings[10] |
| finding | — “Additional revisions are initiated by internal review, and consistency and quality assurance checks. Revisions are also made following feedback from users. Whenever possible, changes are performed in collaboration with expert biocurators or domain specialists; recent examples include blood” | content_tags.findings[11] |
| quantity | 782 terms — biological-process terms obsoleted in two years, against 217 added - a net decrease of over 800 once merges are counted — “At each release, we track all changes and report on our website the number of added, obsoleted, and merged terms in the ontology. Table 1 shows the number of GO terms added and removed (merged or obsoleted) over the past two-year period, for each aspect of GO. In the MF and CC aspects of the ontology, term creation versus term obsoletion have approximately balanced each other, such that the number of terms in these two branches has remained roughly constant. The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms.” | content_tags.quantities[0] |
| value source note | quantity — THE VALUE DRAWS ON THE PAPER BEYOND THIS QUOTE: part of it is printed elsewhere in the paper, not in this quote ('4,039 34 19 162 Biological process 27,993 217 782 254 Also includes obsoleted terms that'). The number is in the paper's Table 1, which the quote cites. | content_tags.quantities[0] |
| quantity | 7 terms — GO terms obsoleted because one erratum showed interleukin-14 had been incorrectly predicted — “alpha-taxilin (UniProt: P40222 ) was originally thought to be the high-molecular weight interleukin-14 ( Ambrus et al. 1993 ); an erratum was later published ( Ambrus et al. 1996 ) indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14 have been obsoleted [ interleukin-14 binding (GO:0019974), interleukin-14 production (GO:0032617), and 5 other terms].” | content_tags.quantities[1] |
| quantity | 3 components — the ontology, the evidence-supported annotations, and the causal activity models — “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.” | content_tags.quantities[2] |
| variable | obsoleted — “The GO is subject to constant review and revision to most accurately model the current biological knowledge. Revision of the ontology includes the addition or obsoletion of terms and reorganization of the relationship structure. New GO terms are added to represent concepts previously missing from the GO in response to published findings, or when a branch of GO is revised. Terms may be obsoleted when unused or inconsistently used in annotation, when they are redundant with other terms, or during revision of specific branches of the ontology.” | content_tags.variables[0] |
| definition | GO annotation — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | content_tags.definitions[0] |
| definition | obsoleted — “The GO is subject to constant review and revision to most accurately model the current biological knowledge. Revision of the ontology includes the addition or obsoletion of terms and reorganization of the relationship structure. New GO terms are added to represent concepts previously missing from the GO in response to published findings, or when a branch of GO is revised. Terms may be obsoleted when unused or inconsistently used in annotation, when they are redundant with other terms, or during revision of specific branches of the ontology.” | content_tags.definitions[1] |
| definition | subclass — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | content_tags.definitions[2] |
| concept | GO knowledgebase — not defined in this paper's own words; the whole resource: ontology, annotations and causal activity models, released monthly with citable versions — “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.” | concepts[0] |
| concept | three components — not defined in this paper's own words; the ontology, the evidence-supported annotations, and the causal activity models — “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.” | concepts[1] |
| concept | GO annotations — not defined in this paper's own words; evidence-supported statements that a gene product has a particular functional characteristic - one characteristic each — “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.” | concepts[2] |
| concept | GO annotation — not defined in this paper's own words; a single association between a gene product and a GO term, with its evidence — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | concepts[3] |
| concept | an association between a specific gene — not defined in this paper's own words; what a GO annotation is, in the paper's own words — “A GO annotation ( Fig. 1b ) is an association between a specific gene (or gene product) and a GO term and should be interpreted as a statement that the specified gene product possesses the specified functional characteristic represented by the GO term. Each GO annotation includes the evidence upon which it is based. Because each GO annotation covers only a single characteristic of gene function, multiple GO annotations are generally required to completely describe the function of a gene product.” | concepts[4] |
| concept | subclass — not defined in this paper's own words; the formal name for one term being more specific than another — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concepts[5] |
| concept | DNA-binding transcription factor activity — not defined in this paper's own words; the paper's worked example of a more specific term — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concepts[6] |
| concept | transcription regulator activity — not defined in this paper's own words; the more general term it is a subclass of — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concepts[7] |
| concept | more specific than — not defined in this paper's own words; the relational description of the subclass relation, given in the same clause as its formal name — “Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene product. Terms can have relationships between them, such as one term being more specific than another term (also called “subclass”), e.g. DNA-binding transcription factor activity is a subclass of transcription regulator activity” | concepts[8] |
| concept | obsoleted — not defined in this paper's own words; retired from the ontology - for disuse, inconsistent use, redundancy, branch revision, an ungrounded retraction, or falling outside the ontology's scope — “The GO is subject to constant review and revision to most accurately model the current biological knowledge. Revision of the ontology includes the addition or obsoletion of terms and reorganization of the relationship structure. New GO terms are added to represent concepts previously missing from the GO in response to published findings, or when a branch of GO is revised. Terms may be obsoleted when unused or inconsistently used in annotation, when they are redundant with other terms, or during revision of specific branches of the ontology.” | concepts[9] |
| concept | interleukin-14 — not defined in this paper's own words; a protein that turned out not to exist as described; seven GO terms mentioning it were obsoleted after the erratum — “alpha-taxilin (UniProt: P40222 ) was originally thought to be the high-molecular weight interleukin-14 ( Ambrus et al. 1993 ); an erratum was later published ( Ambrus et al. 1996 ) indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14 have been obsoleted [ interleukin-14 binding (GO:0019974), interleukin-14 production (GO:0032617), and 5 other terms].” | concepts[10] |
| concept | GO-CAMs — not defined in this paper's own words; GO Causal Activity Models - the third component of the knowledgebase, and the only one of the three the record did not carry — “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)” | concepts[11] |
| concept | GO Causal Activity Models — not defined in this paper's own words; the expanded form of GO-CAMs, given once at first use and abbreviated everywhere after — “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)” | concepts[12] |
| concept | range of in vivo substrate specificity — not defined in this paper's own words; the biological range a single enzyme or transporter is intended to cover — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[13] |
| concept | each separate molecular substrate tested in a single, isolated experimental assay or result — not defined in this paper's own words; the earlier assay-by-assay granularity the revised ontology guidance rejects — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[14] |
| concept | nonphysiological substrates — not defined in this paper's own words; assay substrates that need not describe an in-vivo function — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[15] |
| concept | biological substrate specificity range — not defined in this paper's own words; protein-level specificity that improved experiments can now support — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[16] |
| concept | broad specificity enzymes and transporters — not defined in this paper's own words; gene products whose substrate-specific pathway activity belongs in contextual annotation — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[17] |
| concept | activity on a specific substrate in a specific pathway — not defined in this paper's own words; contextual detail captured outside a standalone ontology term — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[18] |
| concept | GO-CAM — not defined in this paper's own words; a singular causal activity model used for pathway-context annotation — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[19] |
| concept | annotation extension — not defined in this paper's own words; an alternative mechanism for adding substrate and pathway context — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[20] |
| concept | GO term — not defined in this paper's own words; an ontology class whose intended granularity differs from contextual model detail — “We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO terms that represent the range of in vivo substrate specificity of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO term was created for each separate molecular substrate tested in a single, isolated experimental assay or result, which could include nonphysiological substrates. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity range of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO term 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene product has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoleted the more specific GO terms that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transporters, the activity on a specific substrate in a specific pathway can be captured by biocurators in a GO-CAM ( Thomas et al. 2019 ) or an annotation extension ( Gene Ontology Consortium 2010 ) rather than in a GO term” | concepts[21] |
| concept | experimentally supported gene function annotations — not defined in this paper's own words; paper-backed annotations continually added to the knowledgebase — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | concepts[22] |
| concept | annotation groups — not defined in this paper's own words; GO Consortium groups that curate biological knowledge, typically by organism — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | concepts[23] |
| concept | invalidated by later experimental data — not defined in this paper's own words; a reason an existing annotation can be edited or removed — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | concepts[24] |
| concept | manually reviewed — not defined in this paper's own words; human reassessment applied when ontology terms are obsoleted — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | concepts[25] |
| concept | additional quality control — not defined in this paper's own words; benefit attributed to integrated phylogenetic biocurator review — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | concepts[26] |
| concept | underlying evidence — not defined in this paper's own words; published basis to which an annotation remains traceable — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | concepts[27] |
| concept | accuracy of a given annotation — not defined in this paper's own words; property users can verify through publication provenance — “A GO annotation is a statement asserting that a particular gene or gene product has a particular functional characteristic (GO term); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotations have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebase contains experimental knowledge from almost 173,000 papers. GO annotations derived from experimental data are added primarily by the annotation groups in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotations are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental data. Annotations to terms that will be obsoleted are manually reviewed and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoleted terms were manually reviewed, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO terms. More minor annotation reviews occur regularly. The Phylogenetic Annotation with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality control. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidence or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotation” | concepts[28] |
| concept | Phylogenetic Annotation using GO (PAN-GO) project — not defined in this paper's own words; family-level expert curation of selected GO annotations — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[29] |
| concept | biocurator-reviewed, selected GO annotations — not defined in this paper's own words; the curated output of PAN-GO — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[30] |
| concept | PAINT software tool — not defined in this paper's own words; software used to review annotations in a family-tree context — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[31] |
| concept | experimentally supported GO annotations — not defined in this paper's own words; family-member annotations reviewed together by a biocurator — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[32] |
| concept | protein family — not defined in this paper's own words; the unit whose members are reviewed together — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[33] |
| concept | phylogenetic tree — not defined in this paper's own words; evolutionary structure used to interpret family annotations — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[34] |
| concept | PANTHER resource — not defined in this paper's own words; source of the phylogenetic trees and gene families — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[35] |
| concept | informative and nonredundant GO terms — not defined in this paper's own words; terms selected to describe a gene's functional characteristics — “The Phylogenetic Annotation using GO (PAN-GO) project creates a set of biocurator-reviewed, selected GO annotations. The PAN-GO process is described in detail in Gaudet et al. 2011 . Briefly, using the PAINT software tool, a biocurator reviews all experimentally supported GO annotations collected for all members of a protein family, in the context of a phylogenetic tree from the PANTHER resource ( Thomas et al. 2022 ). They then select the most informative and nonredundant GO terms that represent the gene's functional characteristics” | concepts[36] |
| concept | evolution of these characteristics — not defined in this paper's own words; gain-and-loss history modeled along branches of the family tree — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | concepts[37] |
| concept | gained or lost — not defined in this paper's own words; branch-specific change assigned to GO characteristics — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | concepts[38] |
| concept | duplications — not defined in this paper's own words; evolutionary event considered when assigning gains and losses — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | concepts[39] |
| concept | mutations — not defined in this paper's own words; evolutionary event considered when assigning gains and losses — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | concepts[40] |
| concept | horizontal gene transfers — not defined in this paper's own words; evolutionary event considered when assigning gains and losses — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | concepts[41] |
| concept | taxonomic specificity — not defined in this paper's own words; lineage restriction considered in phylogenetic annotation — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | concepts[42] |
| concept | experimental evidence — not defined in this paper's own words; evidence in related genes to which PAN-GO annotations can be traced — “Biocurators then model the evolution of these characteristics in the tree by specifying branches along which the GO terms were gained or lost, taking into account events such as duplications, mutations, horizontal gene transfers, and taxonomic specificity. This allows for different members of the same family to be annotated with different GO terms when justified by the experimental data. All PAN-GO annotations can be traced to experimental evidence in one or more related genes” | concepts[43] |
| concept | PAN-GO annotations — not defined in this paper's own words; phylogenetically reviewed annotations distributed in standard GO files — “PAN-GO annotations are updated at each GO release and are included in the standard, downloadable GO annotation files. These annotations can be identified by the “IBA” (inferred from biological ancestor) evidence code and are available for the 142 organisms included in PANTHER gene families” | concepts[44] |
| concept | IBA — not defined in this paper's own words; abbreviation identifying inferred-from-biological-ancestor annotations — “PAN-GO annotations are updated at each GO release and are included in the standard, downloadable GO annotation files. These annotations can be identified by the “IBA” (inferred from biological ancestor) evidence code and are available for the 142 organisms included in PANTHER gene families” | concepts[45] |
| concept | inferred from biological ancestor — not defined in this paper's own words; expanded name of the IBA evidence code — “PAN-GO annotations are updated at each GO release and are included in the standard, downloadable GO annotation files. These annotations can be identified by the “IBA” (inferred from biological ancestor) evidence code and are available for the 142 organisms included in PANTHER gene families” | concepts[46] |
| concept | protein binding — not defined in this paper's own words; direct binding annotation that carries its partner outside the GO term — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | concepts[47] |
| concept | protein binding partner — not defined in this paper's own words; specific partner stored in another annotation field — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | concepts[48] |
| concept | gene set enrichment analysis — not defined in this paper's own words; application for which direct protein-binding annotations are discouraged — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | concepts[49] |
| concept | protein–protein interaction network construction — not defined in this paper's own words; application for which direct binding annotations are recommended — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | concepts[50] |
| concept | molecular activity of proteins — not defined in this paper's own words; functional description GO seeks beyond binding alone — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | concepts[51] |
| concept | limited functional description — not defined in this paper's own words; status assigned to binding when used alone — “We suggest that users should be particularly cautious when using GO annotations directly to the term protein binding (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partner in another field of the annotation (not in the GO term itself) and should not be used in applications such as gene set enrichment analysis. Instead, they are recommended for applications such as protein–protein interaction network construction for human proteins (which represent the vast majority of direct protein binding annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteins using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional description and is represented as a distinct branch of GO MF” | concepts[52] |
| concept | evidence — not defined in this paper's own words; support attached to every annotation through code and reference fields — “All annotations are supported by evidence, comprising two fields in the annotation file ( Fig. 1b ): an evidence code that describes the type of evidence, and a reference that lists a persistent identifier for tracing the source (provenance) of the original data” | concepts[53] |
| concept | two fields — not defined in this paper's own words; the evidence-code and reference structure in an annotation file — “All annotations are supported by evidence, comprising two fields in the annotation file ( Fig. 1b ): an evidence code that describes the type of evidence, and a reference that lists a persistent identifier for tracing the source (provenance) of the original data” | concepts[54] |
| concept | evidence code — not defined in this paper's own words; field describing the type of evidential support — “All annotations are supported by evidence, comprising two fields in the annotation file ( Fig. 1b ): an evidence code that describes the type of evidence, and a reference that lists a persistent identifier for tracing the source (provenance) of the original data” | concepts[55] |
| concept | reference — not defined in this paper's own words; field carrying a persistent source identifier — “All annotations are supported by evidence, comprising two fields in the annotation file ( Fig. 1b ): an evidence code that describes the type of evidence, and a reference that lists a persistent identifier for tracing the source (provenance) of the original data” | concepts[56] |
| concept | persistent identifier — not defined in this paper's own words; identifier used to trace the source of original data — “All annotations are supported by evidence, comprising two fields in the annotation file ( Fig. 1b ): an evidence code that describes the type of evidence, and a reference that lists a persistent identifier for tracing the source (provenance) of the original data” | concepts[57] |
| concept | provenance — not defined in this paper's own words; traceable source lineage for an annotation's original data — “All annotations are supported by evidence, comprising two fields in the annotation file ( Fig. 1b ): an evidence code that describes the type of evidence, and a reference that lists a persistent identifier for tracing the source (provenance) of the original data” | concepts[58] |
| concept | type of experimental evidence — not defined in this paper's own words; one dimension users should consider when judging reliability — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | concepts[59] |
| concept | level of review — not defined in this paper's own words; second dimension users should consider when judging reliability — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | concepts[60] |
| concept | many genes — not defined in this paper's own words; simultaneously analyzed targets denoted by high-throughput evidence codes — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | concepts[61] |
| concept | individually reviewed — not defined in this paper's own words; annotation-level review absent for high-throughput codes and repeated for IBA — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | concepts[62] |
| concept | nonexperimental evidence types — not defined in this paper's own words; evidence classes that can nonetheless receive careful expert review — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | concepts[63] |
| concept | Phylogenetic annotations — not defined in this paper's own words; IBA annotations integrating and assessing experimental annotations — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | concepts[64] |
| concept | expert assessment — not defined in this paper's own words; curatorial judgment combined with experimental annotations — “It has often been asserted that the most reliable annotations are those made using an experimental evidence code. However, we suggest that users take into account the type of experimental evidence and the level of review of the annotation ( Table 5 ). Some types of experimental evidence, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codes should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genes are analyzed at the same time, and these annotations are not individually reviewed by either the paper's authors or GO Consortium biocurator ( Attrill et al. 2019 ). Conversely, many nonexperimental evidence types are carefully reviewed by experts. Phylogenetic annotations (IBA evidence code) are based on integration and expert assessment of experimental annotations and thus are individually reviewed twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes” | concepts[65] |
| concept | Inferred from Electronic Annotation (IEA) evidence code — not defined in this paper's own words; automated annotation category with expert-reviewed components — “While annotations using the Inferred from Electronic Annotation (IEA) evidence code are considered automated, most implement expert review of a subset of annotations to minimize false positives (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurate though they are often less specific than other annotations” | concepts[66] |
| concept | automated — not defined in this paper's own words; status assigned to IEA annotations — “While annotations using the Inferred from Electronic Annotation (IEA) evidence code are considered automated, most implement expert review of a subset of annotations to minimize false positives (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurate though they are often less specific than other annotations” | concepts[67] |
| concept | expert review — not defined in this paper's own words; subset or rule review used to minimize automated false positives — “While annotations using the Inferred from Electronic Annotation (IEA) evidence code are considered automated, most implement expert review of a subset of annotations to minimize false positives (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurate though they are often less specific than other annotations” | concepts[68] |
| concept | false positives — not defined in this paper's own words; errors that reviewed automated pipelines seek to minimize — “While annotations using the Inferred from Electronic Annotation (IEA) evidence code are considered automated, most implement expert review of a subset of annotations to minimize false positives (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurate though they are often less specific than other annotations” | concepts[69] |
| concept | accurate — not defined in this paper's own words; GOC judgment of the automated annotations described — “While annotations using the Inferred from Electronic Annotation (IEA) evidence code are considered automated, most implement expert review of a subset of annotations to minimize false positives (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurate though they are often less specific than other annotations” | concepts[70] |
| concept | less specific — not defined in this paper's own words; typical limitation of IEA annotations relative to others — “While annotations using the Inferred from Electronic Annotation (IEA) evidence code are considered automated, most implement expert review of a subset of annotations to minimize false positives (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurate though they are often less specific than other annotations” | concepts[71] |
| concept | GO evidence codes — not defined in this paper's own words; coarser annotation abbreviations linked to ECO terms — “GO evidence codes correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO) ( Nadendla et al. 2022 ). Combinations of particular GO internal references (GO_REFs) and evidence codes are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO terms to GO evidence code abbreviations can use the mapping file provided by ECO” | concepts[72] |
| concept | Evidence and Conclusion Ontology (ECO) — not defined in this paper's own words; ontology providing more granular evidence terms — “GO evidence codes correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO) ( Nadendla et al. 2022 ). Combinations of particular GO internal references (GO_REFs) and evidence codes are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO terms to GO evidence code abbreviations can use the mapping file provided by ECO” | concepts[73] |
| concept | GO internal references (GO_REFs) — not defined in this paper's own words; method references combined with evidence codes in ECO mappings — “GO evidence codes correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO) ( Nadendla et al. 2022 ). Combinations of particular GO internal references (GO_REFs) and evidence codes are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO terms to GO evidence code abbreviations can use the mapping file provided by ECO” | concepts[74] |
| concept | granular ECO terms — not defined in this paper's own words; fine-grained evidence concepts mapped to GO abbreviations — “GO evidence codes correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO) ( Nadendla et al. 2022 ). Combinations of particular GO internal references (GO_REFs) and evidence codes are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO terms to GO evidence code abbreviations can use the mapping file provided by ECO” | concepts[75] |
| concept | GO evidence code abbreviations — not defined in this paper's own words; GO-level labels obtainable from ECO mapping files — “GO evidence codes correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO) ( Nadendla et al. 2022 ). Combinations of particular GO internal references (GO_REFs) and evidence codes are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO terms to GO evidence code abbreviations can use the mapping file provided by ECO” | concepts[76] |
| concept | models of causal influences — not defined in this paper's own words; pathway-level interpretation assigned to GO-CAMs — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[77] |
| concept | gene products — not defined in this paper's own words; biological entities whose activities are causally linked — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[78] |
| concept | causal relations — not defined in this paper's own words; links specifying one activity's effect on another — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[79] |
| concept | effect of one activity on the other — not defined in this paper's own words; directed influence encoded by a GO-CAM relation — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[80] |
| concept | element of a GO-CAM — not defined in this paper's own words; model element instantiated from an ontology class or database identifier — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[81] |
| concept | ontology class — not defined in this paper's own words; formal type instantiated by a GO-CAM element — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[82] |
| concept | standard database identifiers — not defined in this paper's own words; non-ontology identifiers permitted in structured GO-CAM elements — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[83] |
| concept | computational analysis — not defined in this paper's own words; analysis enabled by the model's structured identifiers — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[84] |
| concept | gene product activity unit — not defined in this paper's own words; basic GO-CAM unit combining activity, location, and process context — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[85] |
| concept | GO MF annotation — not defined in this paper's own words; molecular-activity component of a gene product activity unit — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[86] |
| concept | GO CC — not defined in this paper's own words; cellular-location context in a gene product activity unit — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[87] |
| concept | GO BP — not defined in this paper's own words; larger functional-module context in a gene product activity unit — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[88] |
| concept | biological context — not defined in this paper's own words; location and process context supplied around molecular activity — “GO-CAMs are models of causal influences between gene products ( Thomas et al. 2019 ) or pathways. More precisely, a GO-CAM links the activities (GO MFs) of gene products together by causal relations that specify the effect of one activity on the other. Each element of a GO-CAM is an instance of an ontology class or other standard database identifiers, so GO-CAMs are highly structured and amenable to computational analysis. The basic unit of a GO-CAM is a “gene product activity unit,” which combines a GO MF annotation (molecular activity), together with GO CC (location) and GO BP (larger functional module) annotations that provide the biological context of the activity” | concepts[89] |
| concept | other ontologies — not defined in this paper's own words; external vocabularies used to refine GO-CAM context — “The context can be further specified with other ontologies to capture the cell type” | concepts[90] |
| concept | cell type — not defined in this paper's own words; one contextual dimension added from an external ontology — “The context can be further specified with other ontologies to capture the cell type” | concepts[91] |
| concept | activity units — not defined in this paper's own words; GO-CAM units linked into interacting causal structures — “Activity units are linked together by causal relationships from the Relations Ontology ( Smith et al. 2005 ) to capture how they interact to impact larger pathways, modules, or processes” | concepts[92] |
| concept | causal relationships — not defined in this paper's own words; Relations Ontology links between activity units — “Activity units are linked together by causal relationships from the Relations Ontology ( Smith et al. 2005 ) to capture how they interact to impact larger pathways, modules, or processes” | concepts[93] |
| concept | Relations Ontology — not defined in this paper's own words; source vocabulary for causal links among activity units — “Activity units are linked together by causal relationships from the Relations Ontology ( Smith et al. 2005 ) to capture how they interact to impact larger pathways, modules, or processes” | concepts[94] |
| concept | larger pathways, modules, or processes — not defined in this paper's own words; higher-level structures impacted by interacting activities — “Activity units are linked together by causal relationships from the Relations Ontology ( Smith et al. 2005 ) to capture how they interact to impact larger pathways, modules, or processes” | concepts[95] |
| concept | molecular pathways — not defined in this paper's own words; models defined here by a minimum causal chain of activities — “As of November 2022, GO Consortium annotation groups have created over 300 GO-CAM models that describe molecular pathways (defined as containing at least three distinct gene product activities linked into a causal chain). These models reflect curation priorities of the contributing groups. Most of the available GO-CAMs are for processes in human or mouse, with a limited number in zebrafish, Drosophila melanogaster , and C. elegans . Many of the human GO-CAMs describe chromatin-mediated regulation of gene expression and immune response pathways, while the mouse GO-CAMs focus on metabolic and signaling pathways. GO-CAMs are accessible from the GO website homepage, by clicking on the “Browse GO-CAMs” link. GO-CAMs can be viewed as pathway diagrams” | concepts[96] |
| concept | at least three distinct gene product activities linked into a causal chain — not defined in this paper's own words; explicit inclusion criterion for the pathway count — “As of November 2022, GO Consortium annotation groups have created over 300 GO-CAM models that describe molecular pathways (defined as containing at least three distinct gene product activities linked into a causal chain). These models reflect curation priorities of the contributing groups. Most of the available GO-CAMs are for processes in human or mouse, with a limited number in zebrafish, Drosophila melanogaster , and C. elegans . Many of the human GO-CAMs describe chromatin-mediated regulation of gene expression and immune response pathways, while the mouse GO-CAMs focus on metabolic and signaling pathways. GO-CAMs are accessible from the GO website homepage, by clicking on the “Browse GO-CAMs” link. GO-CAMs can be viewed as pathway diagrams” | concepts[97] |
| concept | curation priorities — not defined in this paper's own words; group priorities reflected in the currently available models — “As of November 2022, GO Consortium annotation groups have created over 300 GO-CAM models that describe molecular pathways (defined as containing at least three distinct gene product activities linked into a causal chain). These models reflect curation priorities of the contributing groups. Most of the available GO-CAMs are for processes in human or mouse, with a limited number in zebrafish, Drosophila melanogaster , and C. elegans . Many of the human GO-CAMs describe chromatin-mediated regulation of gene expression and immune response pathways, while the mouse GO-CAMs focus on metabolic and signaling pathways. GO-CAMs are accessible from the GO website homepage, by clicking on the “Browse GO-CAMs” link. GO-CAMs can be viewed as pathway diagrams” | concepts[98] |
| concept | pathway diagrams — not defined in this paper's own words; human-viewable rendering of GO-CAMs — “As of November 2022, GO Consortium annotation groups have created over 300 GO-CAM models that describe molecular pathways (defined as containing at least three distinct gene product activities linked into a causal chain). These models reflect curation priorities of the contributing groups. Most of the available GO-CAMs are for processes in human or mouse, with a limited number in zebrafish, Drosophila melanogaster , and C. elegans . Many of the human GO-CAMs describe chromatin-mediated regulation of gene expression and immune response pathways, while the mouse GO-CAMs focus on metabolic and signaling pathways. GO-CAMs are accessible from the GO website homepage, by clicking on the “Browse GO-CAMs” link. GO-CAMs can be viewed as pathway diagrams” | concepts[99] |
| concept | evidence for each assertion in the model — not defined in this paper's own words; click-through evidence attached to model claims — “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” | concepts[100] |
| concept | stable three-dimensional structure — not defined in this paper's own words; structural property absent from intrinsically disordered proteins — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | concepts[101] |
| concept | intrinsically disordered regions (IDRs) — not defined in this paper's own words; flexible and unstructured segments characterizing IDPs — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | concepts[102] |
| concept | Intrinsically Disordered Proteins Ontology (IDPO) — not defined in this paper's own words; custom ontology used by DisProt for structural states — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | concepts[103] |
| concept | structural states — not defined in this paper's own words; IDP properties annotated with IDPO — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | concepts[104] |
| concept | modes of action — not defined in this paper's own words; functional detail improved by the GO–DisProt collaboration — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | concepts[105] |
| concept | self-regulatory — not defined in this paper's own words; IDPO function category that could not be mapped to GO — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | concepts[106] |
| concept | intrinsic disorder-specific functions — not defined in this paper's own words; IDPO-only functions not mapped into GO — “IDPs lack a stable three-dimensional structure and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs). DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO), and used it to annotate the structural states of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO terms whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO terms to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurate and detailed annotation of the modes of action of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotations. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebase, comprising more than 860 MFs, 200 BPs, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatory (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functions (i.e. entropic chains ), so these annotations are available only in DisProt” | concepts[107] |
| concept | host–symbiont branch — not defined in this paper's own words; GO biological-process branch revised for interactions between organisms — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | concepts[108] |
| concept | current scientific knowledge — not defined in this paper's own words; target state for the revised host–symbiont representation — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | concepts[109] |
| concept | Symbionts in GO — not defined in this paper's own words; broad GO category that includes infecting pathogens — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | concepts[110] |
| concept | pathogens that infect a host organism — not defined in this paper's own words; pathogenic members included in GO's broad symbiont category — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | concepts[111] |
| concept | hijack various host cellular processes — not defined in this paper's own words; symbiont-initiated use of host entry, transport, replication, and expression machinery — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | concepts[112] |
| concept | evolved role — not defined in this paper's own words; functional criterion not met by host responses merely triggered by symbiont proteins — “The goal of this project is to revise the host–symbiont branch of GO BP to reflect the current scientific knowledge in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GO are broadly defined to include pathogens that infect a host organism. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclass terms. One challenge in this area was that some previous GO annotations for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processes. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoleted terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved role of these symbiont proteins” | concepts[113] |
| concept | Rhea database of reactions — not defined in this paper's own words; external source of precise biochemical reaction representations — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[114] |
| concept | ChEBI ontology of chemical entities — not defined in this paper's own words; standardized vocabulary used for reaction participants — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[115] |
| concept | in vivo biochemical reactions — not defined in this paper's own words; reaction objects represented precisely by Rhea — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[116] |
| concept | chemical entity participants — not defined in this paper's own words; reaction participants identified with ChEBI terms — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[117] |
| concept | stoichiometry — not defined in this paper's own words; quantitative reaction detail represented by Rhea — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[118] |
| concept | standardized, consistent manner — not defined in this paper's own words; way ChEBI terms normalize chemical entities — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[119] |
| concept | catalytic activity branch — not defined in this paper's own words; GO molecular-function branch overlapping Rhea content — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[120] |
| concept | additional detailed reaction information — not defined in this paper's own words; detail Rhea supplies beyond the overlapping GO content — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[121] |
| concept | nonexact matches — not defined in this paper's own words; mapping strategy used when GO and Rhea differ in chemical specificity — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[122] |
| concept | metabolomics analyses — not defined in this paper's own words; downstream application enabled by extensive chemical-entity linkage — “For accurate representation of biochemical aspects of gene function, we work closely with the Rhea database of reactions ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entities ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactions, including precise chemical entity participants and their stoichiometry. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent manner. The Rhea database overlaps in content with the catalytic activity branch of the GO but provides additional detailed reaction information and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matches when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plus release (see below). Previously, ChEBI terms were linked only to general terms in the GO BP branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analyses” | concepts[123] |
| concept | three editions of the ontology — not defined in this paper's own words; go-basic, go, and go-plus distributions for different applications — “GO provides three editions of the ontology on the download page ( http://geneontology.org/docs/download-ontology/ ) to accommodate various applications: go-basic, go, and go-plus ( Table 6 ). All GO terms, including obsolete terms and term metadata such as definitions, cross-references, and synonyms, are available in all three editions. These editions differ in the set of relations they contain” | concepts[124] |
| concept | go-basic — not defined in this paper's own words; acyclic GO edition recommended for most software tools — “GO provides three editions of the ontology on the download page ( http://geneontology.org/docs/download-ontology/ ) to accommodate various applications: go-basic, go, and go-plus ( Table 6 ). All GO terms, including obsolete terms and term metadata such as definitions, cross-references, and synonyms, are available in all three editions. These editions differ in the set of relations they contain” | concepts[125] |
| concept | go-plus — not defined in this paper's own words; fully axiomatized edition with external-ontology links — “GO provides three editions of the ontology on the download page ( http://geneontology.org/docs/download-ontology/ ) to accommodate various applications: go-basic, go, and go-plus ( Table 6 ). All GO terms, including obsolete terms and term metadata such as definitions, cross-references, and synonyms, are available in all three editions. These editions differ in the set of relations they contain” | concepts[126] |
| concept | set of relations — not defined in this paper's own words; feature on which ontology editions differ — “GO provides three editions of the ontology on the download page ( http://geneontology.org/docs/download-ontology/ ) to accommodate various applications: go-basic, go, and go-plus ( Table 6 ). All GO terms, including obsolete terms and term metadata such as definitions, cross-references, and synonyms, are available in all three editions. These editions differ in the set of relations they contain” | concepts[127] |
| concept | is a, part of, regulates, negatively regulates, and positively regulates relationships — not defined in this paper's own words; relation set retained by the basic edition — “go-basic contains the types of information that has been available for GO from the beginning of the project; hence, it only contains is a, part of, regulates, negatively regulates, and positively regulates relationships and excludes relationships that cross different aspects (BP, MF, or CC) of the ontology. This edition of the ontology is guaranteed to be acyclic and can safely be used to selectively propagate annotations across any relation. It is recommended for most GO-based software tools” | concepts[128] |
| concept | cross different aspects — not defined in this paper's own words; kind of relationship excluded from go-basic — “go-basic contains the types of information that has been available for GO from the beginning of the project; hence, it only contains is a, part of, regulates, negatively regulates, and positively regulates relationships and excludes relationships that cross different aspects (BP, MF, or CC) of the ontology. This edition of the ontology is guaranteed to be acyclic and can safely be used to selectively propagate annotations across any relation. It is recommended for most GO-based software tools” | concepts[129] |
| concept | acyclic — not defined in this paper's own words; structural guarantee of go-basic — “go-basic contains the types of information that has been available for GO from the beginning of the project; hence, it only contains is a, part of, regulates, negatively regulates, and positively regulates relationships and excludes relationships that cross different aspects (BP, MF, or CC) of the ontology. This edition of the ontology is guaranteed to be acyclic and can safely be used to selectively propagate annotations across any relation. It is recommended for most GO-based software tools” | concepts[130] |
| concept | selectively propagate annotations — not defined in this paper's own words; safe operation supported across go-basic relations — “go-basic contains the types of information that has been available for GO from the beginning of the project; hence, it only contains is a, part of, regulates, negatively regulates, and positively regulates relationships and excludes relationships that cross different aspects (BP, MF, or CC) of the ontology. This edition of the ontology is guaranteed to be acyclic and can safely be used to selectively propagate annotations across any relation. It is recommended for most GO-based software tools” | concepts[131] |
| concept | fully axiomatized edition — not defined in this paper's own words; status assigned to go-plus — “go additionally includes has part and occurs in relationships that link terms across different aspects of the ontology (for example, a BP can have a has part relation to a MF term or an occurs in relation to a CC). This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO. go-plus is the fully axiomatized edition of the ontology and includes cross-ontology relationships to external ontologies including ChEBI, Cell Ontology, and Uberon” | concepts[132] |
| concept | cross-ontology relationships — not defined in this paper's own words; links from go-plus to external ontologies — “go additionally includes has part and occurs in relationships that link terms across different aspects of the ontology (for example, a BP can have a has part relation to a MF term or an occurs in relation to a CC). This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO. go-plus is the fully axiomatized edition of the ontology and includes cross-ontology relationships to external ontologies including ChEBI, Cell Ontology, and Uberon” | concepts[133] |
| concept | external ontologies — not defined in this paper's own words; ChEBI, Cell Ontology, Uberon, and other resources linked by go-plus — “go additionally includes has part and occurs in relationships that link terms across different aspects of the ontology (for example, a BP can have a has part relation to a MF term or an occurs in relation to a CC). This edition is not acyclic and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO. go-plus is the fully axiomatized edition of the ontology and includes cross-ontology relationships to external ontologies including ChEBI, Cell Ontology, and Uberon” | concepts[134] |
| concept | GO subsets — not defined in this paper's own words; condensed ontology selections for overview applications — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | concepts[135] |
| concept | condensed versions of the GO — not defined in this paper's own words; form of GO subsets — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | concepts[136] |
| concept | portion of the terms — not defined in this paper's own words; tag-specified selection contained in a GO subset — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | concepts[137] |
| concept | global overview — not defined in this paper's own words; overview of a gene's or genome's functions supported by a subset — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | concepts[138] |
| concept | GO Generic subset — not defined in this paper's own words; species-general subset maintained by the consortium — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | concepts[139] |
| concept | as little redundancy as possible — not defined in this paper's own words; coverage constraint used when revising the generic subset — “GO subsets are condensed versions of the GO containing a portion of the terms, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing a global overview of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsets are particularly useful for providing a global overview of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subset,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many gene products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possible” | concepts[140] |
| concept | causal flow representation — not defined in this paper's own words; planned GO-CAM view of Reactome metabolic pathways — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | concepts[141] |
| concept | chemical reaction-centered representation — not defined in this paper's own words; existing Reactome view complemented by causal flow — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | concepts[142] |
| concept | automated pathway visualization — not defined in this paper's own words; potential use of expanded GO-CAM holdings — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | concepts[143] |
| concept | causal links — not defined in this paper's own words; model structure proposed to improve downstream applications — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | concepts[144] |
| concept | more granular gene sets — not defined in this paper's own words; GO-CAM-derived sets proposed to enhance enrichment — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | concepts[145] |
| concept | enrichment analysis — not defined in this paper's own words; analysis proposed to benefit from causal links and granular sets — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | concepts[146] |
| concept | automated descriptions of gene function — not defined in this paper's own words; potential output improved through structured pathway models — “In parallel, we have started converting Reactome pathways into GO-CAMs ( Good et al. 2021 ) and expect to release GO-CAM representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representation of the chemical reaction-centered representation in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMs, making a large number of yeast metabolic pathways available. The increasing number of GO-CAM models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualization, using the causal links and more granular gene sets to enhance enrichment analysis, and better generation of automated descriptions of gene function” | concepts[147] |
| concept | level of biological organization — not defined in this paper's own words; scale the authors say ontology development must delineate — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[148] |
| concept | MFs — not defined in this paper's own words; molecular functions to be distinguished from biological processes — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[149] |
| concept | BPs — not defined in this paper's own words; biological processes to be distinguished from molecular functions — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[150] |
| concept | individual cells — not defined in this paper's own words; one scale at which biological processes occur — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[151] |
| concept | multicellular organisms — not defined in this paper's own words; organism-level scale contrasted with individual cells — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[152] |
| concept | homeostasis — not defined in this paper's own words; literature term spanning both cell-internal and organismal steady-state processes — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[153] |
| concept | steady-state level within a cell — not defined in this paper's own words; cellular reading of homeostasis — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[154] |
| concept | steady state in blood or other fluid — not defined in this paper's own words; multicellular-organism reading of homeostasis — “With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organization at which a function is described. This includes distinguishing MFs from BPs, and distinguishing BPs that occur at the level of individual cells, versus those that occur at the level of multicellular organisms. For example, the term "homeostasis"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cell and processes that maintain a steady state in blood or other fluid that is transported within a multicellular organism. Even in some publications, it is difficult to know which type of homeostasis is being tested” | concepts[155] |
| claim type | method — a second reading reaching the same value; recorded: method — “We describe the latest improvements to the GO resource and give an overview of the current contents of the knowledgebase.” | alternatives.claim_type |
| method | database-resource-description — a second reading DIFFERING from the recorded value; recorded: system-or-tool-building — “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.” | alternatives.method |
| phenomenon | the Gene Ontology knowledgebase and the representation of gene function — a second reading DIFFERING from the recorded value; recorded: gene function as a thing that must be represented, evidenced, versioned and sometimes withdrawn when the paper behind it — “The Gene Ontology (GO) knowledgebase ( http://geneontology.org ) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs).” | alternatives.phenomenon |
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Abstract. Abstract The Gene Ontology (GO) knowledgebase ( http://geneontology.org ) is a comprehensive resource concerning the functions of genes and gene productsterm (proteins and noncoding RNAs). GO annotationsterm 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 knowledgebaseterm, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebaseterm. The GO knowledgebaseterm consists of three componentsterm: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotationsterm—evidence-supported statements asserting that a specific gene productterm has a particular functional characteristic; and (3) GO Causal Activity Modelsterm (GO-CAMsterm)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotationsterm 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. Keywords: Gene Ontology, gene annotation, gene function, knowledgebase, knowledge graphs Introduction Genes encode gene productsterm, often proteins but also noncoding RNA molecules (ncRNAs), that perform functions at the molecular, cellular, and organismal levels. The Gene Ontology (GO) knowledgebase provides a comprehensive, structured, computer-accessible representation of gene function, for genes from any cellular organism or virus. The GO knowledgebaseterm has become a critical component of life science research, supporting analysis of large-scale experiments and biological systems ( Duck et al. 2016 ). It is designed to make expert knowledge of gene function accessible for bench scientists as well as computational analyses. The basic model underlying GO is the “molecular biology paradigm” ( Ashburner et al. 2000 ; ThomaspersName 2017 ), in which there are three types (aspects) of functional characteristics used to describe gene function: Molecular function (MF): the activities performed by a gene productterm at the molecular level Cellular component (CC): the locations, relative to cellular structures, where MFsterm are performed Biological process (BP): a “biological program” comprising molecular activities acting in concert to achieve a particular outcome; this program can be at the cellular level or at the organism level of multicellular organismsterm. The GO knowledgebaseterm consists of three componentsterm: the GO, GO annotationsterm, and GO Causal Activity modelsterm (GO-CAMsterm) ( Fig. 1 ). The GO ( Fig. 1a ) structures our current knowledge of the types of functional characteristics that a gene productterm may possess into a connected graph-based representation. Each ontology term (called “class” in the field of ontologies) represents a functional characteristic that can be attributed to a gene productterm. Terms can have relationships between them, such as one term being more specific thanterm another term (also called “subclassterm”), e.g. DNA-binding transcription factor activityterm is a subclassterm of transcription regulator activityterm . A GO annotationterm ( Fig. 1b ) is an association between a specific geneterm (or gene productterm) and a GO termterm and should be interpreted as a statement that the specified gene productterm possesses the specified functional characteristic represented by the GO termterm. Each GO annotationterm includes the evidenceterm upon which it is based. Because each GO annotationterm covers only a single characteristic of gene function, multiple GO annotationsterm are generally required to completely describe the function of a gene productterm. GO-CAMsterm ( Fig. 1c ) link multiple GO annotationsterm together to create models of BPsterm by (1) connecting the activities of more than one gene productterm together into causal networks and (2) allowing the specification of the biological contextterm (e.g. cell typeterm and tissue type) in which the activities occur. Fig. 1. Examples of the three componentsterm of the GO knowledgebaseterm. a) The GO ontology consists of terms, e.g. DNA binding transcription factor activityterm , and relationships between the terms (arrows; black = is a , blue = part of , and orange = regulates ). b) GO annotationsterm associate a specific gene productterm (here, human ZNF410) with GO termsterm asserting its functional aspects (“GO Class” column, e.g. sequence-specific double-stranded DNA binding) and the evidenceterm for each assertion with its traceable source (“Evidenceterm” and “Referenceterm” columns). c) The GO-CAMterm model combines individual GO annotationsterm 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-CAMterm is labeled with the gene productterm and species abbreviation for simplicity. The GO knowledgebaseterm is large and dynamic For applications that use the components of the GO knowledgebaseterm, it is crucial that the ontology and associated annotations represent the current state of knowledge and are not just an archive of all public data. Therefore, all aspects of the GO knowledgebaseterm are dynamic (ontology, annotations, GO-CAMsterm, links to external ontologiesterm, etc.), and citable, versioned updates are released on a monthly basis. Below, we describe each component of the knowledgebase, focusing on recent changes made to improve the resource during the past two years. Statistics and descriptions given here are based on the GO release 2022–11–03 ( http://release.geneontology.org/2022-11-03 , doi:10.5281/zenodo.7407024). Ontology The ontology component of the GO knowledgebaseterm consists of the terms used to describe functional characteristics of gene productsterm, which are linked together by relations into a labeled directed acyclicterm graph (like a hierarchy but with multiple parentages allowed). It also includes term definitions, synonyms, and relations to terms from external ontologiesterm. The GO is available in different editions, including (1) the “basic” edition, which includes only core relationship types; (2) the core ontology, including additional relationship types; and (3) the “go-plusterm” edition which also includes relationships to terms in other ontologiesterm. These editions are explained on the GO downloads page http://geneontology.org/docs/download-ontology/ . The ontology contains 43,303 terms ( Table 2 ), linked together by 88,099 relationships in the basic edition. When relationships to external terms are included, there are 121,698 relationships; release statistics can be viewed at http://geneontology.org/stats . Table 2. Groups contributing literature-based annotations. Includes all annotations traceable to the literature (EXP, including HTP, TAS, NAS, and IC; see http://geneontology.org/docs/guide-go-evidence-codes ; see below for information). Direct annotations to the term “protein bindingterm” are listed separately, since without information about interacting partner(s), protein bindingterm represents an activity that most proteins possess, and therefore the GO class itself provides little information (see text for further description). The statistics for groups that have contributed more than 700 manual annotations. Other contributing groups include the following: HGNC, JaponicusDB, PHI-base, PAMGO, JCVI, MENGO, and GDB. Current GO Consortium members are labeled with an asterisk. See http://geneontology.org/docs/annotation-contributors/ for more details. Group Organism or area of focus Number of literature-based annotations, excluding direct protein bindingterm Number of literature-based annotations directly to protein bindingterm UniProt* ( UniProt: the universal protein knowledgebase 2017 ) Human and also a wide variety of organisms not covered by other GOC members 185,121 30,927 MGI* ( Bult et al . 2019 ) Mouse 106,435 8,051 Reactome* ( Fabregat et al . 2018 ) Human pathways 92,178 6 TAIR* ( Lamesch et al . 2012 ) A. thaliana (model plant) 64,633 4,695 FlyBase* ( Sian et al . 2022 ) D. melanogaster (fruit fly) 55,203 892 UCL* Human 54,595 2,935 RGD* ( SmithpersName et al . 2020 ) Rat 47,694 1,894 SGD* ( Lang et al . 2018 ) Saccharomyces cerevisiae (Baker's yeast) 48,811 165 ZFIN* ( Howe et al . 2021 ) Zebrafish 28,261 488 PomBase* ( HarrispersName et al. 2022 ) Schizosaccharomyces pombe (fission yeast) 26,128 2,201 GeneDB Microbial pathogens 23,884 756 ComplexPortal* ( Meldal et al . 2019 ) Protein complexes 18,343 0 WormBase* ( Davis et al . 2022 ) C. elegans (nematode) 17,171 560 CGD* ( SkrzypekpersName et al . 2017 ) Candida albicans (yeast pathogen) 17,113 0 EcoCyc* ( Keseler et al . 2017 ) E. coli (bacterium) 13,372 829 AgBase Agricultural animals, primarily chicken 11,198 1,110 dictyBase* ( Basu et al . 2015 ) Dictyostelium discoideum (slime mold) 9,615 844 HPA Human protein subcellular localization 9,963 0 SynGO ( Koopmans et al . 2019 ) Neuron–neuron synapses 9,552 0 PINC Human and mouse 6,746 0 MTBBASE Mycobacterium tuberculosis (bacterial pathogen) 6,160 463 IntAct* ( Del Toro et al. 2022 ) Protein–protein interactions 4,849 216,488 CAFA ( Radivojac et al . 2013 ) Various 4,818 371 CACAO* ( RamseypersName et al . 2021 ) Various 4,382 0 AspGD ( Cerqueira et al . 2014 ) Aspergillus niger (fungal pathogen) 4,099 0 PseudoCAP ( Winsor et al . 2005 ) Pseudomonas aeruginosa (bacterium) 2,323 0 EcoliWiki* ( McIntosh et al . 2012 ) E. coli (bacterium) 2,123 55 TIGR Bacteria 2,150 0 GO_Central* Various 3,643 160 CollecTF Bacterial transcription factors 1,850 0 NTNU_SB Human, mouse, and rat transcription factors 1,733 0 GR Rice 1,260 0 SGN Tomato 1,255 0 DisProt Disordered proteins 933 156 Xenbase* ( Fortriede et al . 2020 ) Xenopus (frog) 731 0 The GO is subject to constant review and revision to most accurately model the current biological knowledge. Revision of the ontology includes the addition or obsoletion of terms and reorganization of the relationship structure. New GO termsterm are added to represent concepts previously missing from the GO in response to published findings, or when a branch of GO is revised. Terms may be obsoletedterm when unused or inconsistently used in annotation, when they are redundant with other terms, or during revision of specific branches of the ontology. Most of the revisions in the structure of GO are in response to advances in biological knowledge, as well as improvements in the precision of newer experimental approaches. In addition, because many branches of the ontology have grown organically in a bottom-up fashion by accumulating specific individual term requests, we also perform systematic review aimed at improving consistency and clarity while reducing redundancy. Additional revisions are initiated by internal review, and consistency and quality assurance checks. Revisions are also made following feedback from users. Whenever possible, changes are performed in collaboration with expert biocurators or domain specialists; recent examples include blood–brain barrier-related functions ( SaverimuttupersName et al. 2021 ) and transcription factors ( GaudetpersName et al. 2021 ). At each release, we track all changes and report on our website the number of added, obsoletedterm, and merged terms in the ontology. Table 1 shows the number of GO termsterm added and removed (merged or obsoletedterm) over the past two-year period, for each aspect of GO. In the MF and CC aspects of the ontology, term creation versus term obsoletion have approximately balanced each other, such that the number of terms in these two branches has remained roughly constant. The most significant changes have been in the BP aspect of GO, with a net decrease of over 800 terms. Table 1. Changes to GO termsterm in the past two-year period. The ontology has undergone substantial revision and improvement, with nearly 2,000 terms added or removed. GO aspect Total number of terms Added terms Obsoletedterm terms Merged terms a Molecular function 11,271 315 65 143 Cellular component 4,039 34 19 162 Biological process 27,993 217 782 254 Also includes obsoletedterm terms that have been replaced by another term. Many of these revisions result from global reviews of the ontology to address clear inconsistencies in usage and changes in annotation practices. Terms that have been removed from the ontology over the last two years fall into several different categories, including the following: Terms that correspond to phenotypes and for which the understanding of the process was previously too incomplete to annotate to a different term. Examples include the following: regulation of spindle density (GO:0090225) and age-dependent general metabolic decline (GO:0007571). Terms that are combinations of multiple GO termsterm that can now be represented more precisely using GO-CAMterm models . Examples are chromatin remodeling in response to cation stress (GO:0043156) and regulation of cyclin-dependent protein serine/threonine kinase activity involved in G2/M transition of the mitotic cell cycle (GO:0031660). Revisions based on updated knowledge , either by GO editors, by authoritative databases, or in the literature. For example, alpha-taxilin (UniProt: P40222 ) was originally thought to be the high-molecular weight interleukin-14term ( Ambrus et al. 1993 ); an erratum was later published ( Ambrus et al. 1996 ) indicating that the open reading frame had been incorrectly predicted. Hence, all terms mentioning interleukin-14term have been obsoletedterm [ interleukin-14term binding (GO:0019974), interleukin-14term production (GO:0032617), and 5 other terms]. GO termsterm created from articles that have been retracted and for which no other supporting evidenceterm exists are obsoletedterm, for example CDP-acylglycerol O-arachidonoyltransferase activity (GO:0047193) ( Thompson and Zuk 1983 ). Some terms have been obsoletedterm from authoritative databases, for example EC:1.3.1.59 was removed from the Enzyme Commission database, and the corresponding GO termterm 1,6-dihydroxy-5-methylcyclohexa-2,4-dienecarboxylate dehydrogenase activity (GO:0018512) was obsoletedterm in GO. Single step reactions in the BP aspect of the ontology: there were many instances in the GO where a MF could be represented as both a MF and a BP, for example “histone kinase activity” and “histone phosphorylation.” This was useful when fewer activities were characterized at the molecular level, and the best level of resolution for many experiments was that the gene has some uncharacterized role that led to histone phosphorylation, for example. However, with increasingly detailed molecular data, the redundancy between MF and BP annotations became unnecessary and the value of having a similar term in both aspects of the ontology led to inconsistency. This is an ongoing project and many BP terms still need to be obsoletedterm for this reason. Terms that refer to more than one ontological aspect : ubiquinone biosynthetic process monooxygenase activity (GO:0015997) included a BP within a MF; MAP kinase phosphatase activity involved in regulation of innate immune response (GO:0038078) included a MF within a BP; and histone deacetylation at centromere (GO:0031059) represented all three aspects: a MF in the BP branch of the ontology (histone acetylation) that also included CC information (centromere). Misclassified terms , for example urea homeostasisterm (GO:0097274) and creatinine homeostasisterm (GO:0097273): while these compounds are important medical biomarkers, the normal process that they measure is proper renal function; therefore, these terms have been obsoletedterm. Annotations have been rehoused under renal tubular secretion (GO:0097254) (or one of its children) or removed if the paper supporting the annotation did not allow one to infer the process that affected the circulating levels of urea or creatinine. Reaction mechanisms: primary charge separation (GO:0009766) and enzyme active site formation (GO:0018307) were obsoletedterm because they represent substeps of reactions which are beyond the scope of GO. Protein-modifying activity terms that mention specific substrates , for example [cytochrome c]-arginine N-methyltransferase activity (GO:0016275), which is captured by the more general arginine N-methyltransferase activity (GO:0016274). Substrates can be captured with the “has input” relationship in GO-CAMterm models and in annotation extensionsterm. The exception to this is the histone code: for GO to represent this important mechanism of gene expression and chromatin structure mechanism, specific activities are created for known histone modifications, for example histone H2AR3 methyltransferase activity (GO:0070612) and histone H3T3 kinase activity (GO:0072354). Experimental assays and nonphysiological substratesterm : some experiments are easier to perform using analogs of physiological substrates. Because GO termsterm should represent in vivo functions, we have removed some terms that represent an experiment rather than its biological conclusion. An example is rubidium ion transport (GO:0035826): rubidium is used as a tracer for potassium ions ( Gill et al. 2004 ) but has no physiological role in itself. Another example is regulation of nucleosome density (GO:0060303), which measures the degree of compaction of chromatin, and is a readout for heterochromatin assembly or disassembly. Concomitantly with these term obsoletions, many new terms have been added to the ontology in the past two years. An example is molecular condensate scaffold activity (GO:0140693) for proteins that nucleate condensates that mediate liquid phase transition. This latter term represents a recent advance in the understanding of the organization of cellular biochemistry ( Banani et al. 2017 ). We have also clarified the level of specificity at which MF terms should be represented in GO. For example, we now strive to create GO termsterm that represent the range of in vivo substrate specificityterm of an enzyme or transporter. This is in contrast to earlier guidelines, in which a GO termterm was created for each separate molecular substrate tested in a single, isolated experimental assay or resultterm, which could include nonphysiological substratesterm. With recent improvements in experimental technologies and practices, it is now often possible to annotate with a concept that more closely matches the biological substrate specificity rangeterm of a protein. Therefore, while GO makes cross-references to Enzyme Commission (EC) ( McDonald and Tipton 2014 ), Rhea ( Bansal et al. 2022 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( Kanehisa et al. 2022 ), and MetaCyc ( Altman et al. 2013 ), GO does not necessarily create a different term for each of the reactions represented in these resources for each substrate on which a MF acts. For example, the GO termterm 3-oxoacyl-[acyl-carrier-protein] reductase (NADPH) activity (GO:0004316) represents the fact that the same gene productterm has a broad specificity toward 3-oxo-acyl groups, and therefore, we have obsoletedterm the more specific GO termsterm that refer to only one specific substrate, such as 3-oxo-cis-Delta9-hexadecenoyl-[acp] reductase activity (GO:0102072), 3-oxo-glutaryl-[acp] methyl ester reductase activity (GO:0102131), and 3-oxo-pimeloyl-[acp] methyl ester reductase activity (GO:0102132). For broad specificity enzymes and transportersterm, the activity on a specific substrate in a specific pathwayterm can be captured by biocurators in a GO-CAMterm ( ThomaspersName et al. 2019 ) or an annotation extensionterm ( Gene Ontology Consortium 2010 ) rather than in a GO termterm. Annotations A GO annotationterm is a statement asserting that a particular gene or gene productterm has a particular functional characteristic (GO termterm); examples are shown in Fig. 1b . New annotations are continually added to the knowledgebase. In the past two years, experimentally supported gene function annotationsterm have been added from over 10,000 scientific papers. As of November 2022, the GO knowledgebaseterm contains experimental knowledge from almost 173,000 papers. GO annotationsterm derived from experimental data are added primarily by the annotation groupsterm in the GO Consortium, which typically curate biological knowledge by organism ( Table 2 ). GO annotationsterm are also regularly reviewed and may be edited or removed from the knowledgebase for various reasons, particularly when ontology terms are revised (see “Ontology” section above) or when annotations are invalidated by later experimental dataterm. Annotations to terms that will be obsoletedterm are manually reviewedterm and annotations are made to a different term whenever possible. For example, when we edited the ontology for histone modifications, over 2,000 annotations to the obsoletedterm terms were manually reviewedterm, and histone modifying enzymes were reannotated to the appropriate MF term, while annotations from indirect effects were either removed or reannotated to different, appropriate GO termsterm. More minor annotation reviews occur regularly. The Phylogenetic Annotationterm with the GO project (see below) involves an integrated biocurator review of annotations that has provided additional quality controlterm. The GO user community also plays an important role in identifying incorrect annotations. Because each annotation can be traced to the published paper containing the underlying evidenceterm or describing a method used to infer the annotation, users can quickly verify the accuracy of a given annotationterm. Potential errors can be reported by clicking on the “Help” link at the top of the GO homepage ( http://geneontology.org ). In addition, authors of a paper used to create GO annotationsterm can easily retrieve and review all annotations from a given paper and suggest changes; this can be done from the PubMed abstract page (e.g. the PubMed page https://pubmed.ncbi.nlm.nih.gov/20516198/ ( Lydeard et al. 2010 )) by clicking on LinkOut and then the “Gene Ontology” link. Phylogenetic annotationsterm as a source of highly reviewed annotations The Phylogenetic Annotation using GO (PAN-GO) projectterm creates a set of biocurator-reviewed, selected GO annotationsterm. The PAN-GO process is described in detail in GaudetpersName et al. 2011 . Briefly, using the PAINT software toolterm, a biocurator reviews all experimentally supported GO annotationsterm collected for all members of a protein familyterm, in the context of a phylogenetic treeterm from the PANTHER resourceterm ( ThomaspersName et al. 2022 ). They then select the most informative and nonredundant GO termsterm that represent the gene's functional characteristics. Biocurators then model the evolution of these characteristicsterm in the tree by specifying branches along which the GO termsterm were gained or lostterm, taking into account events such as duplicationsterm, mutationsterm, horizontal gene transfersterm, and taxonomic specificityterm. This allows for different members of the same family to be annotated with different GO termsterm when justified by the experimental data. All PAN-GO annotationsterm can be traced to experimental evidenceterm in one or more related genes. To date, a total of 8,196 protein families (out of 11,719 families with experimental data) have been curated. The PAN-GO curation effort has prioritized human gene-containing families, though many other families have also been curated. As a result, annotation coverage of a genome generally depends on how closely related it is to humans. PAN-GO annotationsterm are available for 82% of human genes (compared with 68% with experimental evidenceterm alone). Other vertebrate genomes have similarly high coverage, with genomes from other taxa covered at lower but still substantial levels ( Tables 3 and 4 ). PAN-GO annotationsterm are updated at each GO release and are included in the standard, downloadable GO annotationterm files. These annotations can be identified by the “IBAterm” (inferred from biological ancestorterm) evidence codeterm and are available for the 142 organisms included in PANTHER gene families ( http://pantherdb.org/panther/summaryStats.jsp ). Table 3. Genome coverage of PAN-GO annotationsterm. Percentage of protein-coding genes with at least one PAN-GO-reviewed annotation, for different taxonomic groups. Taxonomic group Number of individually reviewedterm, annotated genomes Gene coverage of annotations Vertebrates 19 66%–83% Invertebrates 15 40%–68% Fungi 14 32%–76% Plants 40 28%–51% Protists, alveolates, and amoebae 11 19%–46% Archaea 8 23%–34% Bacteria 35 20%–57% Table 4. Number of PAN-GO annotationsterm for selected genomes. Genome Total IBAterm annotations MF annotations BP annotations CC annotations Danio rerio 82,855 23,049 33,888 25,918 Mus musculus 72,554 19,832 29,947 22,775 Rattus norvegicus 71,276 19,792 28,738 22,746 Homo sapiens 68,695 18,537 28,177 21,981 Gallus gallus 59,293 15,847 24,010 19,436 Xenopus tropicalis 43,232 12,687 16,735 13,810 Arabidopsis thaliana 37,509 12,106 12,922 12,481 Caenorhabditis elegans 31,093 9,021 11,728 10,344 Drosophila melanogaster 30,331 8,628 11,202 10,501 Dictyostelium discoideum 18,966 5,556 6,815 6,595 Saccharomyces cerevisiae 15,675 4,286 5,763 5,626 Schizosaccharomyces pombe 13,723 3,719 4,953 5,051 Escherichia coli 5,681 2,171 1,916 1,594 Protein bindingterm and protein-containing complex annotations We suggest that users should be particularly cautious when using GO annotationsterm directly to the term protein bindingterm (GO:0005515; see Table 2 ). These are highly specific annotations that include the protein binding partnerterm in another field of the annotation (not in the GO termterm itself) and should not be used in applications such as gene set enrichment analysisterm. Instead, they are recommended for applications such as protein–protein interaction network constructionterm for human proteins (which represent the vast majority of direct protein bindingterm annotations in the knowledgebase). Since all protein functions encompass some type of binding (to a substrate or to another protein), GO strives to describe the molecular activity of proteinsterm using at least one term that is not only under the binding branch of GO; see also the “noncatalytic MF” section above. Therefore, binding (GO:0005488) in isolation can be considered a limited functional descriptionterm and is represented as a distinct branch of GO MF. Annotation evidenceterm All annotations are supported by evidenceterm, comprising two fieldsterm in the annotation file ( Fig. 1b ): an evidence codeterm that describes the type of evidenceterm, and a referenceterm that lists a persistent identifierterm for tracing the source (provenanceterm) of the original data. It has often been asserted that the most reliable annotations are those made using an experimental evidenceterm code. However, we suggest that users take into account the type of experimental evidenceterm and the level of reviewterm of the annotation ( Table 5 ). Some types of experimental evidenceterm, such as inference from a gene expression pattern (IEP), mutant phenotype (IMP), or genetic interaction (IGI), can often be suggestive of function but not definitive when considered in isolation; other annotations for the same gene are often useful to help interpret these annotations. “High-throughput” evidence codesterm should be treated with particular care. These codes (beginning with the letter H) denote experiments in which many genesterm are analyzed at the same time, and these annotations are not individually reviewedterm by either the paper's authors or GO Consortium biocurator ( AttrillpersName et al. 2019 ). Conversely, many nonexperimental evidence typesterm are carefully reviewed by experts. Phylogenetic annotationsterm (IBAterm evidence codeterm) are based on integration and expert assessmentterm of experimental annotations and thus are individually reviewedterm twice: once in making the annotation from published experimental results and once in the context of all annotations for related genes ( GaudetpersName et al. 2011 ). While annotations using the Inferred from Electronic Annotation (IEA) evidence codeterm are considered automatedterm, most implement expert reviewterm of a subset of annotations to minimize false positivesterm (for example, UniRule ( MacDougall et al. 2021 ) and InterPro2GO ( Paysan-Lafosse et al. 2022 )). The GOC considers these annotations to be accurateterm though they are often less specificterm than other annotations. Table 5. GO evidence codesterm + referenceterm combinations. Users should consider both the type of evidenceterm and the review level. GO internal referencesterm (starting with GO_REF:) describe specific annotation methods and are available at https://github.com/geneontology/go-site/tree/master/metadata/gorefs/README.md . Evidence codeterm Referenceterm Evidence-type description Review-level description Inferred from direct assay (IDA) Scientific publication providing experimental data Experimental, most direct evidenceterm for function Individually expert-reviewed Inferred from mutant phenotype (IMP) Scientific publication providing experimental data Experimental, from a perturbation in the normal function Individually expert-reviewed Inferred from genetic interaction (IGI) Scientific publication providing experimental data Experimental, from perturbations in normal functions of more than one gene Individually expert-reviewed Inferred from expression pattern (IEP) Scientific publication providing experimental data Experimental, used only for biological process annotations, from comparison with genes of known function Individually expert-reviewed Inferred from protein interaction (IPI) Scientific publication providing experimental data Experimental, used only for annotations to protein bindingterm terms Individually expert-reviewed Inferred from high throughput direct assay (HDA) Scientific publication providing experimental data Experimental (high throughput), direct Not individually reviewedterm; expert reviewterm methodology to exclude high false positiveterm rate observations Inferred from high throughput mutant phenotype (HMP) Scientific publication providing experimental data Experimental (high throughput), mutant phenotype Not individually reviewedterm; expert reviewterm to exclude high false positiveterm rate observations Inferred from high throughput genetic interaction (HGI) Scientific publication providing experimental data Experimental (high throughput), genetic interaction Not individually reviewedterm; expert reviewterm to exclude high false positiveterm rate observations Inferred from high throughput expression pattern (HEP) Scientific publication providing experimental data Experimental (high throughput), expression pattern Not individually reviewedterm; expert reviewterm to exclude high false positiveterm rate observations Inferred from biological ancestorterm (IBAterm) ( GaudetpersName et al . 2011 ) Homology, from experimental evidenceterm propagated through a phylogenetic treeterm and/or from direct experimental evidenceterm Individually expert-reviewed in the context of all experimental annotations for related genes Inferred from sequence similarity (ISS) Scientific publication providing sequence similarity evidenceterm Homology, from experimental evidenceterm propagated from one gene to one related gene, asserted in the publication Individually expert-reviewed in the context of all experimental annotations for related genes Inferred from sequence similarity (ISS) GO_REF:0000024 Homology, from experimental evidenceterm propagated from one gene to one related gene, asserted by a biocurator Individually expert-reviewed Inferred from sequence orthology (ISO) Scientific publication providing orthology evidenceterm Homology, from experimental evidenceterm propagated from one gene to one orthologous gene Individually expert-reviewed Inferred from sequence Orthology GO_REF:0000008 Homology, from experimental evidenceterm propagated from one mammalian gene to one orthologous mouse gene Individually expert-reviewed ISO, Inferred from Sequence orthology (ISO) GO_REF:0000024 Homology, from experimental evidenceterm propagated from one gene to one orthologous gene Individually expert-reviewed Inferred from sequence orthology (ISO) GO_REF:0000096 Homology, from experimental evidenceterm propagated from one gene to one orthologous gene among human, mouse, and rat orthologs Not individually reviewedterm; orthology manually reviewedterm Inferred from sequence orthology (ISO) GO_REF:0000101 Homology, from experimental evidenceterm propagated from one gene to one orthologous gene Not individually reviewedterm; orthology computed using OrthoMCL Inferred from electronic annotation (IEA) GO_REF:0000107 Homology, from experimental evidenceterm propagated from one gene to one orthologous gene Not individually reviewedterm; one-to-one orthology computed using Ensembl Compara phylogenetic treesterm Inferred from electronic annotation (IEA) GO_REF:0000002 Homology, from a hit to an InterPro signature Not individually reviewedterm; expert reviewterm of annotations of signatures to ensure low or no false positivesterm Inferred from electronic annotation (IEA) GO_REF:0000003 Imported from another resource, from mapping an EC number assigned in UniProt Not individually reviewedterm; expert reviewterm of mappings; EC assignments are manually reviewedterm for Swiss-Prot and computationally inferred for TrEMBL Inferred from electronic annotation (IEA) GO_REF:0000004 Imported from another resource, from mapping a manually assigned Swiss-Prot keyword Not individually reviewedterm; expert reviewterm of keywords and mappings Inferred from electronic annotation (IEA) GO_REF:0000104 Homology, from manually curated UniRule Not individually reviewedterm; expert curation of UniRules to ensure low or no false positivesterm Inferred from electronic annotation (IEA) GO_REF:0000108 Logical assertion using the ontology, from asserted relation between different aspects of GO Not individually reviewedterm; expert curation of ontology links Inferred from electronic annotation (IEA) GO_REF:0000117 Computational, from machine learning Not individually reviewedterm; assigned by machine learning from curated training sets Traceable author statement (TAS) Scientific publication citing original data From a published statement referencing experimental evidenceterm in a different paper Individually reviewedterm Nontraceable author statement (NAS) Scientific publication with general biological knowledge statement From an unreferenced published statement Individually reviewedterm GO evidence codesterm correspond to a subset of the terms found in the Evidence and Conclusion Ontology (ECO)term ( NadendlapersName et al. 2022 ). Combinations of particular GO internal references (GO_REFs)term and evidence codesterm are also mapped to specific ECO terms ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping.txt ). Users needing to map granular ECO termsterm to GO evidence code abbreviationsterm can use the mapping file provided by ECO ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping-derived.txt ). GO causal activity modelsterm GO-CAMsterm are models of causal influencesterm between gene productsterm ( ThomaspersName et al. 2019 ) or pathways. More precisely, a GO-CAMterm links the activities (GO MFsterm) of gene productsterm together by causal relationsterm that specify the effect of one activity on the otherterm. Each element of a GO-CAMterm is an instance of an ontology classterm or other standard database identifiersterm, so GO-CAMsterm are highly structured and amenable to computational analysisterm. The basic unit of a GO-CAMterm is a “gene product activity unitterm,” which combines a GO MF annotationterm (molecular activity), together with GO CCterm (location) and GO BPterm (larger functional module) annotations that provide the biological contextterm of the activity. The context can be further specified with other ontologiesterm to capture the cell typeterm [using the Cell Typeterm Ontology ( DiehlpersName et al. 2016 )], tissue/anatomical location (using several different ontologies depending on the species, e.g. Uberon ( MungallpersName et al. 2012 ) for most vertebrates, other metazoan ontologies such as the Drosophila anatomy ontology ( Costa et al. 2013 ), Caenorhabditis elegans anatomy ontology ( LeepersName and SternbergpersName 2003 ), or nonanimal ontologies as the Plant Ontology ( CooperpersName and JaiswalpersName 2016 ), or a temporal period (e.g. GO biological phase). Activity unitsterm are linked together by causal relationshipsterm from the Relations Ontologyterm ( SmithpersName et al. 2005 ) to capture how they interact to impact larger pathways, modules, or processesterm. As of November 2022, GO Consortium annotation groupsterm have created over 300 GO-CAMterm models that describe molecular pathwaysterm (defined as containing at least three distinct gene product activities linked into a causal chainterm). These models reflect curation prioritiesterm of the contributing groups. Most of the available GO-CAMsterm are for processes in human or mouse, with a limited number in zebrafish, Drosophila melanogaster , and C. elegans . Many of the human GO-CAMsterm describe chromatin-mediated regulation of gene expression and immune response pathways, while the mouse GO-CAMsterm focus on metabolic and signaling pathways. GO-CAMsterm are accessible from the GO website homepage, by clicking on the “Browse GO-CAMsterm” link. GO-CAMsterm can be viewed as pathway diagramsterm ( Fig. 2 ) and are currently available on GitHub at https://github.com/geneontology/noctua-models . Fig. 2. GO-CAMterm model of the SARS-CoV2—host interactions as displayed using the GO-CAMterm 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 modelterm. Community collaborations The GO Consortium collaborates with experts in specific areas of molecular and cellular biology to systematically update and improve their representation in the ontology and the corresponding GO annotationsterm and GO-CAMsterm. We recently revised the representation for transcription factors and transcriptional regulation in collaboration with the GREEKC Consortium ( Kuiper et al. 2022 ). Additional collaborative projects include working with the DisProt project ( QuagliapersName et al. 2022 ) on improving the ontology and annotations for intrinsically disordered proteins (IDPs); revising processes that involve molecular pathwaysterm between interacting species, such as viral infection processes; and integrating the GO and annotations with external biochemical databases. In 2021, the GO started a collaboration with DisProt ( https://disprot.org/ )—the gold standard database of manually curated annotations from the literature for IDPs. IDPs lack a stable three-dimensional structureterm and are characterized by highly flexible and unstructured segments, i.e. intrinsically disordered regions (IDRs)term. DisProt has developed a custom ontology, the Intrinsically Disordered Proteins Ontology (IDPO)term, and used it to annotate the structural statesterm of IDPs. The GO Consortium and DisProt have collaborated to refactor IDPO and map the IDPO terms to GO termsterm whenever possible (those related to functions and interactions of IDPs). The collaboration between the GO Consortium and the DisProt database included the creation and addition of new GO termsterm to align with already existing IDPO terms that were not yet available in GO. These newly created terms also include the MF activator (GO:0140677) and MF inhibitor (GO:0140678) terms, used to annotate MF regulators that activate/inhibit or increase/decrease the activities of their targets via noncovalent binding that does not result in covalent modification to the target. This collaboration resulted in more accurateterm and detailed annotation of the modes of actionterm of IDPs, e.g. localization (GO:0051179, IDPO:00010) and DNA binding (GO:0003677, IDPO:00065), as well as providing GO annotationsterm. Currently, more than 1,000 expert-curated annotations from DisProt are available in the GO knowledgebaseterm, comprising more than 860 MFsterm, 200 BPsterm, and 10 CC annotations. The only terms in IDPO that could not be mapped to GO were those describing self-regulatoryterm (e.g. self-activation and self-inhibition ) and intrinsic disorder-specific functionsterm (i.e. entropic chains ), so these annotations are available only in DisProt. Multiorganism interactions A group that includes experts from within and outside the GO Consortium has been working together to improve and simplify the representation of interactions between organisms, including medically and agriculturally important host–pathogen interactions. Examples of these interactions include how a symbiont such as a virus enters its host, how the host's immune response recognizes and defends the body against a potentially harmful organism, and also beneficial interactions such as how plants form a symbiosis with nitrogen-fixing bacteria. The goal of this project is to revise the host–symbiont branchterm of GO BPterm to reflect the current scientific knowledgeterm in the field and to ensure that genes are properly annotated to the new ontology terms and structure, building on previous work undertaken as part of the PAMGO consortium ( Tyler et al. 2009 ). Symbionts in GOterm are broadly defined to include pathogens that infect a host organismterm. We expect that this revision will improve GO-based analyses of molecular studies of pathogens, the mechanisms by which they infect host cells, and host response processes. A major change is that the branch of GO under BP involved in interspecies interaction between organisms (GO:0044419) has been reorganized. It now reflects important concepts such as the types of biological programs used by symbionts to enable infection and by hosts to prevent or manage infection, such as disruption of CC of another organism (GO:0140975), formation of structure involved in a symbiotic process (GO:0044111), killing of cells of another organism (GO:0031640), and modulation of process of another organism (GO:0035821). Each of these terms has multiple, more specific subclassterm terms. One challenge in this area was that some previous GO annotationsterm for pathogen genes used terms that apply to normal host processes, such as regulation of defense response processes. Thus, it was not clear whether the pathogen gene was regulating its own defense process or that of a host. With the new ontology terms and structure, these distinctions are clear for both GO biocurators and users of GO. In general, it was important to clearly represent that certain symbiont-initiated processes hijack various host cellular processesterm. This includes mechanisms to enter and exit the cell, either by binding to host membrane proteins or using the intracellular transport machinery and using the host cellular machinery for genome replication, as well as transcription and translation. We have obsoletedterm terms that do not clearly distinguish hijacking with the functions that a host gene performs for the host organism, such as dissemination or transmission of symbiont from host by vector (GO:0044008) and positive regulation of viral release from host cell (GO:1902188). Conversely, a pathogenic symbiont triggers innate responses in the host that are not the evolved roleterm of these symbiont proteins, such as induction by symbiont of host cytokine production (GO:0036523) and pathogen-associated molecular pattern-dependent induction by symbiont of host innate immune response (GO:0052033)—these are not functions that a symbiont protein performs to enable its own survival and reproduction. Integration with biochemical knowledgebases For accurateterm representation of biochemical aspects of gene function, we work closely with the Rhea database of reactionsterm ( Bansal et al. 2022 ) and the ChEBI ontology of chemical entitiesterm ( Hastings et al. 2016 ). Rhea provides precise representations of in vivo biochemical reactionsterm, including precise chemical entity participantsterm and their stoichiometryterm. Rhea uses ChEBI terms to represent chemical entities in a standardized, consistent mannerterm. The Rhea database overlaps in content with the catalytic activity branchterm of the GO but provides additional detailed reaction informationterm and in some cases provides additional specificity. We have improved GO mappings to Rhea, which now covers 4,399 GO catalytic activities (in the MF branch of GO). These mappings allow for nonexact matchesterm when the chemical specificity differs between GO and Rhea. For example, Rhea has two reactions, each referring to a different type of beta glucoside (RHEA:69647 and RHEA:69655, narrow match), whereas GO:0008422, beta-glucosidase activity, covers both substrates, as no known enzyme is specific for just one of them. We have recently used the Rhea-GO mappings to include additional linkages between GO MF terms and ChEBI terms in the go-plusterm release (see below). Previously, ChEBI terms were linked only to general terms in the GO BPterm branch (e.g. between folate transport and folate), but the additional Rhea linkages have added a total of 4,334 distinct chemical entities linked via 20,307 relationships. The extensive linkage to chemical entities opens opportunities for using GO in other applications, e.g. metabolomics analysesterm. Accessing and downloading GO data Browsing GO and its annotations GO and associated annotations can be searched directly from the GO home page ( http://geneontology.org/ ), queried using the AmiGO browser ( http://amigo.geneontology.org/amigo ) or the QuickGO tool ( https://www.ebi.ac.uk/QuickGO/ ) ( Munoz-Torres and CarbonpersName 2017 ). Gene set enrichment analysisterm is also directly accessible from the GO home page, which launches the PANTHER gene analysis tool at http://pantherdb.org/webservices/go/overrep.jsp ( Mi et al. 2019 ). Ontology downloads GO provides three editions of the ontologyterm on the download page ( http://geneontology.org/docs/download-ontology/ ) to accommodate various applications: go-basicterm, go, and go-plusterm ( Table 6 ). All GO termsterm, including obsolete terms and term metadata such as definitions, cross-references, and synonyms, are available in all three editions. These editions differ in the set of relationsterm they contain: Table 6. GO editions. Editions are distinguished by the relations and metadata that they include. All editions are updated at each GO release. External ontologiesterm used in GO include the following: ChEBI, Uberon ( Haendel et al. 2014 ), Relation Ontology ( SmithpersName et al. 2005 ), Cell Ontology ( DiehlpersName et al. 2016 ), Sequence Ontology ( MungallpersName, Batchelor and Eilbeck 2011 ), Dicty Anatomy, CARO ( Haendel et al. 2008 ), Fungal Anatomy Ontology ( Fungal-Anatomy-Ontology, 2020 ), Plant Ontology ( Walls et al. 2019 ), PATO ( Gkoutos, Schofield and Hoehndorf 2018 ), and Protein Ontology ( Natale et al. 2017 ). GO edition Format(s) Relations included Links to other ontologiesterm go-basicterm OBO Is a, part of, regulates, negatively regulates, and positively regulates Not available go OBO and OWL-RDF/XML Same as go-basicterm, plus has part, and occurs in Not available go-plusterm OWL-RDF/XML ChEBI, Uberon, Cell Ontology, Sequence Ontology, Dicty Anatomy, CARO, Fungal Anatomy Ontology, Plant Ontology, PATO, and Protein Ontology go-basicterm contains the types of information that has been available for GO from the beginning of the project; hence, it only contains is a, part of, regulates, negatively regulates, and positively regulates relationshipsterm and excludes relationships that cross different aspectsterm (BP, MF, or CC) of the ontology. This edition of the ontology is guaranteed to be acyclicterm and can safely be used to selectively propagate annotationsterm across any relation. It is recommended for most GO-based software tools. go additionally includes has part and occurs in relationships that link terms across different aspects of the ontology (for example, a BP can have a has part relation to a MF term or an occurs in relation to a CC). This edition is not acyclicterm and annotations should not be propagated across all the relationship types that it contains. This edition should not be used in most software tools that rely on the GO. go-plusterm is the fully axiomatized editionterm of the ontology and includes cross-ontology relationshipsterm to external ontologiesterm including ChEBI, Cell Ontology, and Uberon. Ontology subsets (GO slims) GO subsetsterm are condensed versions of the GOterm containing a portion of the termsterm, which are specified by tags within the ontology files that indicate if a given term is a member of a particular subset. GO subsetsterm are particularly useful for providing a global overviewterm of the functions of all the genes in a genome, and even for all the functions of a single gene. GO subsetsterm are particularly useful for providing a global overviewterm of the range of functions and processes found in a given clade or organism's genome. We have recently revised the “GO Generic subsetterm,” a subset maintained by the GO Consortium that aims to be general and applicable to any species. We have tested that the subset covers as many geneterm products as possible in various organisms (human, D. melanogaster , fission yeast, Arabidopsis thaliana , and Escherichia coli ) with as little redundancy as possibleterm. This new GO Generic Subsetterm contains 75 BP terms, 40 MF terms, and 29 CC terms. The GO generic subsetterm can be accessed at http://current.geneontology.org/ontology/subsets/goslim_generic.obo . Versions in .owl, .json, and .tsv are also available from http://current.geneontology.org/ontology/subsets/index.html . As part of the Alliance of Genome Resources, we have developed a widget that provides a graphical visualization of a gene's function in a “ribbon”-like display ( Fig. 3 ). The widget can be customized to use any GO subsetterm and uses the goslim_agr subset by default. This widget is implemented in the Alliance gene pages and in the UniProt entry pages. It accesses GO annotationsterm using the GO API (application programming interface) and can be easily added to any webpage. 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. GO annotationsterm The two major sites for downloading GO annotationsterm are geneontology.org and UniProt-GOA. geneontology.org is the website developed by the GO Consortium. This downloads site ( http://current.geneontology.org/products/pages/downloads.html ) provides a total of 7.5 million human and model organism annotations contributed by multiple groups. It contains all manually reviewedterm GO annotationsterm and electronic (computationally predicted) annotations for the most commonly used organisms. For model organisms, all annotations use gene identifiers from the authoritative database (for example, FlyBase (FBgn), WormBase (WBGene), and SGD (S)). Human and other organisms without an authoritative dedicated database are represented by UniProtKB accession numbers. For these organisms, the GO website provides annotations to UniProt referenceterm proteomes ( https://www.uniprot.org/help/referenceterm_proteome ), which are generally one entry per gene, thus limiting redundancy in annotations. UniProt-GOA ( https://www.ebi.ac.uk/GOA/uniprot_release ) contains 1 billion annotations for all entries in UniProt (1,264,340 taxa), covering both reviewed (Swiss-Prot) entries of UniProt, and unreviewed (TrEMBL) entries. All annotations for the model organism genes are converted to UniProt protein identifiers. For most organisms, all annotations are electronic annotations generated via various pipelines (see above for evidence codesterm and referencesterm for different methods). In addition to these resources, GO annotationsterm are also viewable in a number of biological databases, including model organism databases, UniProt ( UniProt: the universal protein knowledgebase 2017 ), NCBI ( Sayers et al. 2020 ), and The Alliance of Genome Resources ( Alliance of Genome Resources Consortium 2022 ). These sites show GO annotationsterm in the broader context of a gene productterm's expression pattern, phenotypes, metabolic and signaling pathways, etc. Conclusions and future directions The extensive and wide-ranging use of the GO knowledgebaseterm, evidenced by its recent, peer-reviewed designation as a Global Core Biodata Resource ( https://globalbiodata.org/scientific-activities/global-core-biodata-resources/ ), demands its continued development and expansion. We are focusing on several high-priority areas of development for the near future. For pathways, we will continue to accumulate GO-CAMterm models. The UniProt/Swiss-Prot curation team has ramped up their production of GO-CAMterm models and we expect to add models at a rapid rate. In parallel, we have started converting Reactome pathways into GO-CAMsterm ( Good et al. 2021 ) and expect to release GO-CAMterm representations of most Reactome metabolic pathways in the near future. This will provide a complementary, causal flow representationterm of the chemical reaction-centered representationterm in Reactome. Conversion of Reactome signaling pathways is more challenging and will be released somewhat later. We are also working on converting the YeastPathways resource ( https://pathways.yeastgenome.org ) into GO-CAMsterm, making a large number of yeast metabolic pathways available. The increasing number of GO-CAMterm models will allow us to expand on the utility of these highly structured pathway and process representations. Some potential areas are automated pathway visualizationterm, using the causal linksterm and more granular gene setsterm to enhance enrichment analysisterm, and better generation of automated descriptions of gene functionterm (e.g. Kishore et al. 2020 ). With respect to ontology development, in addition to continuing to revise the ontology in response to recent discoveries, we see an immediate need for clearly delineating the level of biological organizationterm at which a function is described. This includes distinguishing MFsterm from BPsterm, and distinguishing BPsterm that occur at the level of individual cellsterm, versus those that occur at the level of multicellular organismsterm. For example, the term "homeostasisterm"—the maintenance of a roughly steady level of a molecule or ion—is used very broadly in the literature to refer to both processes that maintain a steady-state level within a cellterm and processes that maintain a steady state in blood or other fluidterm that is transported within a multicellular organismterm. Even in some publications, it is difficult to know which type of homeostasisterm is being tested. We will continue to make the GO knowledgebaseterm easier to use and more community-driven. One near-term priority is to make annotations available for download by species, with a single identifier for each distinct gene. We are also planning to create quick-start guides for common GO use cases, in both written and video forms. The immense user base of the GO and the need for much improvement and extension drives us to consider how to expand the number of people that contribute to the GO. From its inception, the GO has been a large, open, community project. However, we are planning additional routes through which the broader GO user community can contribute their expert feedback and knowledge to GO, improving the resource for all users. For now, users are encouraged to contact the GO Helpdesk ( http://help.geneontology.org/ ) with any questions or to report any GO ontology terms or annotations that may be inaccurate or difficult to interpret. Contributor Information The Gene Ontology Consortium: GO Central: Suzi A AleksanderpersName , James BalhoffpersName , Seth CarbonpersName , J Michael CherrypersName , Harold J DrabkinpersName , Dustin EbertpersName , Marc FeuermannpersName , Pascale GaudetpersName , Nomi L HarrispersName , David P HillpersName , Raymond LeepersName , HuaiyupersName Mi , Sierra MoxonpersName , Christopher J MungallpersName , Anushya MuruganuganpersName , Tremayne MushayahamapersName , Paul W SternbergpersName , Paul D ThomaspersName , Kimberly Van AukenpersName , CACAO, EcoliWiki: Jolene RamseypersName , Deborah A SiegelepersName , dictyBase: Rex L ChisholmpersName , Petra FeypersName , DisProt, Dept. of Biomedical Sciences, University of Padova, Italy: Maria Cristina AspromontepersName , Maria Victoria NugnespersName , Federica QuagliapersName , Silvio TosattopersName , ECO: Michelle GigliopersName , Suvarna NadendlapersName , FlyBase: Giulia AntonazzopersName , Helen AttrillpersName , Gil dos SantospersName , Steven MarygoldpersName , Victor StreletspersName , Christopher J TabonepersName , Jim ThurmondpersName , Pinglei ZhoupersName , Functional Gene Annotation, Institute of Cardiovascular Science, University College London (London, UK): Saadullah H AhmedpersName , Praoparn AsanitthongpersName , Diana Luna BuitragopersName , Meltem N ErdolpersName , Matthew C GagepersName , Mohamed Ali KadhumpersName , Kan Yan ChloepersName Li , Miao LongpersName , Aleksandra MichalakpersName , Angeline PesalapersName , Armalya PritazahrapersName , Shirin C C SaverimuttupersName , RenzhipersName Su , Kate E ThurlowpersName , Ruth C LoveringpersName , GREEKC: Colin LogiepersName , JaponicusDB (London, UK): Snezhana OliferenkopersName , MGI: Judith BlakepersName , Karen ChristiepersName , Lori CorbanipersName , Mary E DolanpersName , Harold J DrabkinpersName , David P HillpersName , Li Ni , Dmitry SitnikovpersName , Cynthia SmithpersName , PHI-base: Alayne CuzickpersName , James SeagerpersName , Planteome: Laurel CooperpersName , Justin ElserpersName , Pankaj JaiswalpersName , Plant Reactome: Parul GuptapersName , Pankaj JaiswalpersName , Sushma NaithanipersName , PomBase: Manuel Lera-Ramirez , Kim RutherfordpersName , Valerie WoodpersName , RGD: Jeffrey L De PonspersName , Melinda R DwinellpersName , G ThomaspersName HaymanpersName , Mary L KaldunskipersName , Anne E KwitekpersName , Stanley J F LaulederkindpersName , Marek A TutajpersName , Mahima VedipersName , Shur-Jen WangpersName , Reactome: Peter D’EustachiopersName , Rhea, Swiss-Prot group, SIB Swiss Institute of Bioinformatics (SIB) (Geneva, Switzerland): Lucila AimopersName , Kristian AxelsenpersName , Alan BridgepersName , Nevila Hyka-NouspikelpersName , Anne MorgatpersName , SGD, Department of Genetics, Stanford University (Stanford, CA): Suzi A AleksanderpersName , J Michael CherrypersName , Stacia R EngelpersName , Kalpana KarrapersName , Stuart R MiyasatopersName , Robert S NashpersName , Marek S SkrzypekpersName , Shuai WengpersName , Edith D WongpersName , The Arabidopsis Information Resource (TAIR), Phoenix Bioinformatics (Newark, CA, USA): Erika BakkerpersName , Tanya Z BerardinipersName , Leonore ReiserpersName , UniProt, Swiss-Prot group, SIB Swiss Institute of Bioinformatics (SIB) (Geneva, Switzerland): Andrea AuchinclosspersName , Kristian AxelsenpersName , Ghislaine Argoud-PuypersName , Marie-Claude BlatterpersName , Emmanuel BoutetpersName , Lionel BreuzapersName , Alan BridgepersName , Cristina Casals-CasaspersName , Elisabeth CoudertpersName , Anne EstreicherpersName , Maria Livia FamigliettipersName , Marc FeuermannpersName , Arnaud GospersName , Nadine Gruaz-GumowskipersName , Chantal HulopersName , Nevila Hyka-NouspikelpersName , Florence JungopersName , Philippe Le MercierpersName , Damien LieberherrpersName , Patrick MassonpersName , Anne MorgatpersName , Ivo PedruzzipersName , Lucille PourcelpersName , Sylvain PouxpersName , Catherine RivoirepersName , Shyamala SundarampersName , UniProt, EMBL-EBI, (Hinxton, UK): Alex BatemanpersName , Emily Bowler-BarnettpersName , Hema Bye-A-JeepersName , Paul DennypersName , Alexandr IgnatchenkopersName , Rizwan IshtiaqpersName , Antonia LockpersName , Yvonne LussipersName , Michele MagranepersName , Maria J MartinpersName , Sandra OrchardpersName , Pedro RaposopersName , Elena SperettapersName , Nidhi TyagipersName , Kate WarnerpersName , Rossana ZarupersName , University at Buffalo, Department of Biomedical Informatics (Buffalo, NY, USA): Alexander D DiehlpersName , WormBase: Raymond LeepersName , Juancarlos ChanpersName , Stavros DiamantakispersName , Daniela RacitipersName , Magdalena ZarowieckipersName , Xenbase: Malcolm FisherpersName , Christina James-ZornpersName , Virgilio PonferradapersName , Aaron ZornpersName , ZFIN: Sridhar RamachandranpersName , Leyla RuzickapersName , and Monte WesterfieldpersName Data availability All GO code and resources are freely available for download and reuse. Software ( https://github.com/geneontology/ ) is under the BSD 3-Clause open-source license. Downloads are available under the CC BY 4.0 license from http://geneontology.org/docs/downloads/ Funding The core funding for the GOC is from the National Human Genome Research Institute with co-funding from the National Institute of General Medical Sciences (U41HG002273, U24HG012212). Curation activities are supported by National Human Genome Research Institute grants U24HG002659 (ZFIN), U24HG002223 (WormBase), U41HG000739 (FlyBase), U24HG001315 (SGD), U24HG000330 (MGD), U24HG012198 (Reactome curation), and U24HG011851 (Reactome—GO harmonization) and grant R01HL064541 from the National Heart, Lung and Blood Institute (RGD). Additional funding for GO curation at FlyBase is provided by UK Medical Research Council Award MR/W024233/1. PomBase is supported by Wellcome Trust 218236/Z/19/Z. Xenbase is supported by grant P41 HD064556 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Functional Gene Annotation, University College London, is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. Planteome and Plant Reactome are supported by National Science Foundation awards #1340112, #1127112, and USDA-ARS. Some software development was funded by National Human Genome Research Institute, grant U24HG010859 (Alliance of Genome Resources Central). The TAIR project is funded by academic, institutional, corporate, and individual subscriptions; TAIR is administered by the 501(c)(3) non-profit Phoenix Bioinformatics. CJM, SC, and SM were supported in part by Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231. UniProt is funded by the National Human Genome Research Institute (NHGRI), Office of Director (OD/DPCPSI/ODSS); National Institute of Allergy and Infectious Diseases (NIAID), National Institute on Aging (NIA), National Institute of General Medical Sciences (NIGMS), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Eye Institute (NEI), National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (U24HG007822); Biotechnology and Biological Sciences Research Council (BB/T010541/1, BB/S01781X/1); Open Targets; Swiss Federal Government through the State Secretariat for Education, Research and Innovation (SERI); and European Molecular Biology Laboratory core funds. Summary The Gene Ontology (GO) knowledgebase has broad applications in genetic and genomic research and has been continually updated and improved for more than 20 years. 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