standOff. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote shown on the same row, so the borrowing can be checked against the thing borrowed from without leaving this page. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| second reading | alternatives | an independent re-reading of a facet, recorded whether or not it agrees |
| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| content tag | content_tags | a finding, definition, quantity, statistic or variable named in the paper |
| contribution | contribution | what the paper claims to add |
| control | controls | an experiment run to eliminate a rival explanation |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| unrecordable relation | unhoused_relations | a relation the paper asserts that this vocabulary cannot express, with the reason |
The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.
Abstract. Abstract alternativesThe Gene Ontology (GO) knowledgebase ( http://geneontology.org ) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). limitationscontrolscontent_tagsGO 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. alternativesconcept_edgesHere, 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. object_of_studyclaim_typemechanismsconcepts↗concepts↗concepts↗concepts↗concepts↗sensesconcept_edgesconcept_edgesconcept_edgescontent_tagscontent_tagsThe GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project. Keywords: Gene Ontology, gene annotation, gene function, knowledgebase, knowledge graphs Introduction Genes encode gene products, 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 knowledgebase 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. sensesclaim_relationscontent_tagsThe 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. The GO knowledgebase consists of three components: the GO, GO annotations, and GO Causal Activity models (GO-CAMs) ( Fig. 1 ). The GO ( Fig. 1a ) structures our current knowledge of the types of functional characteristics that a gene product may possess into a connected graph-based representation. concepts↗concepts↗concepts↗concepts↗sensesconcept_edgesconcept_edgescontent_tags↗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 . mechanismsconcepts↗concepts↗sensesconcept_edgescontent_tagscontent_tags↗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. GO-CAMs ( Fig. 1c ) link multiple GO annotations together to create models of BPs by (1) connecting the activities of more than one gene product together into causal networks and (2) allowing the specification of the biological context (e.g. cell type and tissue type) in which the activities occur. Fig. 1. Examples of the three components of the GO knowledgebase. a) The GO ontology consists of terms, e.g. DNA binding transcription factor activity , and relationships between the terms (arrows; black = is a , blue = part of , and orange = regulates ). b) GO annotations associate a specific gene product (here, human ZNF410) with GO terms asserting its functional aspects (“GO Class” column, e.g. sequence-specific double-stranded DNA binding) and the evidence for each assertion with its traceable source (“Evidence” and “Reference” columns). c) The GO-CAM model combines individual GO annotations into a model, in this case a very simple model describing how human ZNF410 acts as a transcription factor to positively regulate (denoted by the arrow) transcription of the CHD4 gene, which in turn acts as a corepressor to repress (denoted by dashed lines) transcription of fetal hemoglobin genes ( HBG1 and HBG2 ) in erythroid lineage cells. In this view, each box in the GO-CAM is labeled with the gene product and species abbreviation for simplicity. The GO knowledgebase is large and dynamic research_questionlimitationsclaim_relationscontent_tagsFor 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. 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 knowledgebase consists of the terms used to describe functional characteristics of gene products, which are linked together by relations into a labeled directed acyclic graph (like a hierarchy but with multiple parentages allowed). It also includes term definitions, synonyms, and relations to terms from external ontologies. 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-plus” edition which also includes relationships to terms in other ontologies. 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 binding” are listed separately, since without information about interacting partner(s), protein binding 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 binding Number of literature-based annotations directly to protein binding 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* ( Smith 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* ( Harris 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* ( Skrzypek 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* ( Ramsey 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 concepts↗sensescontent_tags↗content_tags↗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. 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. method↗controlscontent_tagsAdditional 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 ( Saverimuttu et al. 2021 ) and transcription factors ( Gaudet et al. 2021 ). contributionlimitationscontent_tagscontent_tagsAt 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. Table 1. Changes to GO terms 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 Obsoleted 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 obsoleted 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 terms that can now be represented more precisely using GO-CAM 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, mechanismsconcepts↗content_tagscontent_tagsalpha-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]. phenomenonmechanismscontrolscontent_tagsGO 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 GO term 1,6-dihydroxy-5-methylcyclohexa-2,4-dienecarboxylate dehydrogenase activity (GO:0018512) was obsoleted 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 obsoleted 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 homeostasis (GO:0097274) and creatinine homeostasis (GO:0097273): while these compounds are important medical biomarkers, the normal process that they measure is proper renal function; therefore, these terms have been obsoleted. 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. limitationscontent_tagsReaction 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. 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-CAM models and in annotation extensions. 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 substrates : some experiments are easier to perform using analogs of physiological substrates. Because GO terms 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 ). concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensessensesconcept_edgesWe 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. Annotations concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesA 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. controlscontent_tagsThe 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 (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 annotations as a source of highly reviewed annotations concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesThe 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↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationssensesBiocurators 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. 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 annotations are available for 82% of human genes (compared with 68% with experimental evidence alone). Other vertebrate genomes have similarly high coverage, with genomes from other taxa covered at lower but still substantial levels ( Tables 3 and 4 ). concepts↗concepts↗concepts↗concept_edgesPAN-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 ( http://pantherdb.org/panther/summaryStats.jsp ). Table 3. Genome coverage of PAN-GO annotations. Percentage of protein-coding genes with at least one PAN-GO-reviewed annotation, for different taxonomic groups. Taxonomic group Number of individually reviewed, 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 annotations for selected genomes. Genome Total IBA 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 binding and protein-containing complex annotations concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesconcept_edgesWe 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. Annotation evidence concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesAll 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↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensessensessensesIt 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 ( Gaudet et al. 2011 ). concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesWhile 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. Table 5. GO evidence codes + reference combinations. Users should consider both the type of evidence and the review level. GO internal references (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 code Reference Evidence-type description Review-level description Inferred from direct assay (IDA) Scientific publication providing experimental data Experimental, most direct evidence 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 binding terms Individually expert-reviewed Inferred from high throughput direct assay (HDA) Scientific publication providing experimental data Experimental (high throughput), direct Not individually reviewed; expert review methodology to exclude high false positive rate observations Inferred from high throughput mutant phenotype (HMP) Scientific publication providing experimental data Experimental (high throughput), mutant phenotype Not individually reviewed; expert review to exclude high false positive rate observations Inferred from high throughput genetic interaction (HGI) Scientific publication providing experimental data Experimental (high throughput), genetic interaction Not individually reviewed; expert review to exclude high false positive rate observations Inferred from high throughput expression pattern (HEP) Scientific publication providing experimental data Experimental (high throughput), expression pattern Not individually reviewed; expert review to exclude high false positive rate observations Inferred from biological ancestor (IBA) ( Gaudet et al . 2011 ) Homology, from experimental evidence propagated through a phylogenetic tree and/or from direct experimental evidence Individually expert-reviewed in the context of all experimental annotations for related genes Inferred from sequence similarity (ISS) Scientific publication providing sequence similarity evidence Homology, from experimental evidence 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 evidence propagated from one gene to one related gene, asserted by a biocurator Individually expert-reviewed Inferred from sequence orthology (ISO) Scientific publication providing orthology evidence Homology, from experimental evidence propagated from one gene to one orthologous gene Individually expert-reviewed Inferred from sequence Orthology GO_REF:0000008 Homology, from experimental evidence 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 evidence propagated from one gene to one orthologous gene Individually expert-reviewed Inferred from sequence orthology (ISO) GO_REF:0000096 Homology, from experimental evidence propagated from one gene to one orthologous gene among human, mouse, and rat orthologs Not individually reviewed; orthology manually reviewed Inferred from sequence orthology (ISO) GO_REF:0000101 Homology, from experimental evidence propagated from one gene to one orthologous gene Not individually reviewed; orthology computed using OrthoMCL Inferred from electronic annotation (IEA) GO_REF:0000107 Homology, from experimental evidence propagated from one gene to one orthologous gene Not individually reviewed; one-to-one orthology computed using Ensembl Compara phylogenetic trees Inferred from electronic annotation (IEA) GO_REF:0000002 Homology, from a hit to an InterPro signature Not individually reviewed; expert review of annotations of signatures to ensure low or no false positives Inferred from electronic annotation (IEA) GO_REF:0000003 Imported from another resource, from mapping an EC number assigned in UniProt Not individually reviewed; expert review of mappings; EC assignments are manually reviewed 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 reviewed; expert review of keywords and mappings Inferred from electronic annotation (IEA) GO_REF:0000104 Homology, from manually curated UniRule Not individually reviewed; expert curation of UniRules to ensure low or no false positives Inferred from electronic annotation (IEA) GO_REF:0000108 Logical assertion using the ontology, from asserted relation between different aspects of GO Not individually reviewed; expert curation of ontology links Inferred from electronic annotation (IEA) GO_REF:0000117 Computational, from machine learning Not individually reviewed; assigned by machine learning from curated training sets Traceable author statement (TAS) Scientific publication citing original data From a published statement referencing experimental evidence in a different paper Individually reviewed Nontraceable author statement (NAS) Scientific publication with general biological knowledge statement From an unreferenced published statement Individually reviewed concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsGO 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 ( https://github.com/evidenceontology/evidenceontology/blob/master/gaf-eco-mapping-derived.txt ). GO causal activity models concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationssensessensesconcept_edgesGO-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↗concepts↗The context can be further specified with other ontologies to capture the cell type [using the Cell Type Ontology ( Diehl et al. 2016 )], tissue/anatomical location (using several different ontologies depending on the species, e.g. Uberon ( Mungall et al. 2012 ) for most vertebrates, other metazoan ontologies such as the Drosophila anatomy ontology ( Costa et al. 2013 ), Caenorhabditis elegans anatomy ontology ( Lee and Sternberg 2003 ), or nonanimal ontologies as the Plant Ontology ( Cooper and Jaiswal 2016 ), or a temporal period (e.g. GO biological phase). concepts↗concepts↗concepts↗concepts↗unhoused_relationsActivity 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↗concepts↗concepts↗concepts↗sensesconcept_edgesAs 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 ( Fig. 2 ) and are currently available on GitHub at https://github.com/geneontology/noctua-models . Fig. 2. GO-CAM model of the SARS-CoV2—host interactions as displayed using the GO-CAM Pathway Widget (code available at https://github.com/geneontology/wc-gocam-viz ) on the Alliance of Genome Resources gene pages ( https://www.alliancegenome.org/gene/HGNC:20144#pathways ). The model includes proteins from both humans (Hsap) and the SARS-CoV-2 virus (Scov2). A simplified representation of the causal model is shown on the main figure, which is simplified by labeling each activity with the gene and organism. concepts↗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. 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 annotations and GO-CAMs. 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 ( Quaglia et al. 2022 ) on improving the ontology and annotations for intrinsically disordered proteins (IDPs); revising processes that involve molecular pathways 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. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationssensesIDPs 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. 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. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesconcept_edgesThe 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, 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 concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensesFor 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. 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 Carbon 2017 ). Gene set enrichment analysis 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 concepts↗concepts↗concepts↗concepts↗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: Table 6. GO editions. Editions are distinguished by the relations and metadata that they include. All editions are updated at each GO release. External ontologies used in GO include the following: ChEBI, Uberon ( Haendel et al. 2014 ), Relation Ontology ( Smith et al. 2005 ), Cell Ontology ( Diehl et al. 2016 ), Sequence Ontology ( Mungall, 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 ontologies go-basic OBO Is a, part of, regulates, negatively regulates, and positively regulates Not available go OBO and OWL-RDF/XML Same as go-basic, plus has part, and occurs in Not available go-plus OWL-RDF/XML ChEBI, Uberon, Cell Ontology, Sequence Ontology, Dicty Anatomy, CARO, Fungal Anatomy Ontology, Plant Ontology, PATO, and Protein Ontology concepts↗concepts↗concepts↗concepts↗sensesgo-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↗concepts↗concepts↗sensesconcept_edgesgo 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). limitationscontent_tagsThis 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. Ontology subsets (GO slims) concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesconcept_edgesGO 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. This new GO Generic Subset contains 75 BP terms, 40 MF terms, and 29 CC terms. The GO generic subset 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 subset 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 annotations 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 annotations The two major sites for downloading GO annotations 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 reviewed GO annotations 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 reference proteomes ( https://www.uniprot.org/help/reference_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 codes and references for different methods). In addition to these resources, GO annotations 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 annotations in the broader context of a gene product's expression pattern, phenotypes, metabolic and signaling pathways, etc. Conclusions and future directions The extensive and wide-ranging use of the GO knowledgebase, 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-CAM models. The UniProt/Swiss-Prot curation team has ramped up their production of GO-CAM models and we expect to add models at a rapid rate. concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsIn 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 (e.g. Kishore et al. 2020 ). concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationssensesconcept_edgesWith 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. We will continue to make the GO knowledgebase 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 Aleksander , James Balhoff , Seth Carbon , J Michael Cherry , Harold J Drabkin , Dustin Ebert , Marc Feuermann , Pascale Gaudet , Nomi L Harris , David P Hill , Raymond Lee , Huaiyu Mi , Sierra Moxon , Christopher J Mungall , Anushya Muruganugan , Tremayne Mushayahama , Paul W Sternberg , Paul D Thomas , Kimberly Van Auken , CACAO, EcoliWiki: Jolene Ramsey , Deborah A Siegele , dictyBase: Rex L Chisholm , Petra Fey , DisProt, Dept. of Biomedical Sciences, University of Padova, Italy: Maria Cristina Aspromonte , Maria Victoria Nugnes , Federica Quaglia , Silvio Tosatto , ECO: Michelle Giglio , Suvarna Nadendla , FlyBase: Giulia Antonazzo , Helen Attrill , Gil dos Santos , Steven Marygold , Victor Strelets , Christopher J Tabone , Jim Thurmond , Pinglei Zhou , Functional Gene Annotation, Institute of Cardiovascular Science, University College London (London, UK): Saadullah H Ahmed , Praoparn Asanitthong , Diana Luna Buitrago , Meltem N Erdol , Matthew C Gage , Mohamed Ali Kadhum , Kan Yan Chloe Li , Miao Long , Aleksandra Michalak , Angeline Pesala , Armalya Pritazahra , Shirin C C Saverimuttu , Renzhi Su , Kate E Thurlow , Ruth C Lovering , GREEKC: Colin Logie , JaponicusDB (London, UK): Snezhana Oliferenko , MGI: Judith Blake , Karen Christie , Lori Corbani , Mary E Dolan , Harold J Drabkin , David P Hill , Li Ni , Dmitry Sitnikov , Cynthia Smith , PHI-base: Alayne Cuzick , James Seager , Planteome: Laurel Cooper , Justin Elser , Pankaj Jaiswal , Plant Reactome: Parul Gupta , Pankaj Jaiswal , Sushma Naithani , PomBase: Manuel Lera-Ramirez , Kim Rutherford , Valerie Wood , RGD: Jeffrey L De Pons , Melinda R Dwinell , G Thomas Hayman , Mary L Kaldunski , Anne E Kwitek , Stanley J F Laulederkind , Marek A Tutaj , Mahima Vedi , Shur-Jen Wang , Reactome: Peter D’Eustachio , Rhea, Swiss-Prot group, SIB Swiss Institute of Bioinformatics (SIB) (Geneva, Switzerland): Lucila Aimo , Kristian Axelsen , Alan Bridge , Nevila Hyka-Nouspikel , Anne Morgat , SGD, Department of Genetics, Stanford University (Stanford, CA): Suzi A Aleksander , J Michael Cherry , Stacia R Engel , Kalpana Karra , Stuart R Miyasato , Robert S Nash , Marek S Skrzypek , Shuai Weng , Edith D Wong , The Arabidopsis Information Resource (TAIR), Phoenix Bioinformatics (Newark, CA, USA): Erika Bakker , Tanya Z Berardini , Leonore Reiser , UniProt, Swiss-Prot group, SIB Swiss Institute of Bioinformatics (SIB) (Geneva, Switzerland): Andrea Auchincloss , Kristian Axelsen , Ghislaine Argoud-Puy , Marie-Claude Blatter , Emmanuel Boutet , Lionel Breuza , Alan Bridge , Cristina Casals-Casas , Elisabeth Coudert , Anne Estreicher , Maria Livia Famiglietti , Marc Feuermann , Arnaud Gos , Nadine Gruaz-Gumowski , Chantal Hulo , Nevila Hyka-Nouspikel , Florence Jungo , Philippe Le Mercier , Damien Lieberherr , Patrick Masson , Anne Morgat , Ivo Pedruzzi , Lucille Pourcel , Sylvain Poux , Catherine Rivoire , Shyamala Sundaram , UniProt, EMBL-EBI, (Hinxton, UK): Alex Bateman , Emily Bowler-Barnett , Hema Bye-A-Jee , Paul Denny , Alexandr Ignatchenko , Rizwan Ishtiaq , Antonia Lock , Yvonne Lussi , Michele Magrane , Maria J Martin , Sandra Orchard , Pedro Raposo , Elena Speretta , Nidhi Tyagi , Kate Warner , Rossana Zaru , University at Buffalo, Department of Biomedical Informatics (Buffalo, NY, USA): Alexander D Diehl , WormBase: Raymond Lee , Juancarlos Chan , Stavros Diamantakis , Daniela Raciti , Magdalena Zarowiecki , Xenbase: Malcolm Fisher , Christina James-Zorn , Virgilio Ponferrada , Aaron Zorn , ZFIN: Sridhar Ramachandran , Leyla Ruzicka , and Monte Westerfield 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. alternativesWe describe the latest improvements to the GO resource and give an overview of the current contents of the knowledgebase. We also include guidance on the use of GO, annotations, and the growing number of pathways represented as GO-Causal Activity Models (GO-CAMs). Literature cited Alliance of Genome Resources Consortium . Harmonizing model organism data in the Alliance of Genome Resources. Genetics. 2022;220:iyac022. doi: 10.1093/genetics/iyac022. Altman T, Travers M, Kothari A, Caspi R, Karp PD. A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinformatics. 2013;14(1):112. doi: 10.1186/1471-2105-14-112. Ambrus JL Jr, Pippin J, Joseph A, Xu C, Blumenthal D, Tamayo A, Claypool K, McCourt D, Srikiatchatochorn A, Ford RJ. 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Smith JR, Hayman GT, Wang S-J, Laulederkind SJF, Hoffman MJ, Kaldunski ML, Tutaj M, Thota J, Nalabolu HS, Ellanki SLR, et al. The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform. Nucleic Acids Res. 2020;48:D731–D742. doi: 10.1093/nar/gkz1041. Thomas PD. The Gene Ontology and the meaning of biological function. Methods Mol Biol. 2017;1446:15–24. doi: 10.1007/978-1-4939-3743-1_2. Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou LP, Mi H. PANTHER: making genome-scale phylogenetics accessible to all. Protein Sci. 2022;31(1):8–22. doi: 10.1002/pro.4218. Thomas PD, Hill DP, Mi H, Osumi-Sutherland D, Van Auken K, Carbon S, Balhoff JP, Albou L-P, Good B, Gaudet P, et al. Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems. Nat Genet. 2019;51(10):1429–1433. doi: 10.1038/s41588-019-0500-1. Thompson W, Zuk RT. Acylation of CDP-monoacylglycerol cannot be confirmed. J Biol Chem. 1983;258(16):9623. doi: 10.1016/S0021-9258(17)44541-8. Torto-Alalibo T, Collmer CW, Gwinn-Giglio M. The Plant-Associated Microbe Gene Ontology (PAMGO) Consortium: community development of new Gene Ontology terms describing biological processes involved in microbe-host interactions. BMC Microbiol. 2009;9 Suppl1:S1. doi: 10.1186/1471-2180-9-S1-S1 The UniProt Consortium . Uniprot: the universal protein knowledgebase. Nucleic Acids Res. 2017;45(D1):D158–D169. doi: 10.1093/nar/gkw1099. Walls RL, Cooper L, Elser J, Gandolfo MA, Mungall CJ, Smith B, Stevenson DW, Jaiswal P. The Plant Ontology facilitates comparisons of plant development stages across species. Front Plant Sci. 2019;10:631. doi: 10.3389/fpls.2019.00631. Winsor GL, Lo R, Ho Sui SJ, Ung KS, Huang S, Cheng D, Ching WK, Hancock RE, Brinkman FS. Pseudomonas aeruginosa Genome Database and PseudoCAP: facilitating community-based, continually updated, genome annotation. Nucleic Acids Res. 2005;33(Database issue):D338–D343. doi: 10.1093/nar/gki047. Associated Data Data Availability Statement 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/
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 12 of 16 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | A CURATED, VERSIONED, EVIDENCE-BEARING KNOWLEDGEBASE OF WHAT GENES DO - and the rules by which its terms are added, merged, obsoleted and traced back to the paper that justified them | object_of_study | the paper's words | |
| research_question | what the Gene Ontology knowledgebase currently contains, how it has changed in two years, and how users should use it | research_question | the paper's words | |
| 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 | contribution | the paper's words | |
| claim_type | method | claim_type | the paper's words | |
| phenomenon | gene function as a thing that must be represented, evidenced, versioned and sometimes withdrawn when the paper behind it fails | phenomenon | the paper's words | |
| method | system-or-tool-building also in: system-or-tool-building | method | the paper's words | |
| mechanisms | 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` | mechanisms[0] | 0.95 | the tagger's words |
| mechanisms | 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` | mechanisms[1] | 0.95 | the tagger's words |
| mechanisms | 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 | mechanisms[2] | 0.85 | the tagger's words |
| mechanisms | 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 | mechanisms[3] | 0.95 | the tagger's words |
| limitations | 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` | limitations[0] | 0.95 | the tagger's words |
| limitations | 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` | limitations[1] | 0.95 | the tagger's words |
| limitations | 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` | limitations[2] | 0.95 | the tagger's words |
| limitations | 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 | limitations[3] | 0.95 | the tagger's words |
| limitations | 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 | limitations[4] | 0.95 | the tagger's words |
| controls | traceability of every annotation to the paper or method behind it eliminates: 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 · outcome: held | controls[0] | 0.95 | the tagger's words |
| controls | obsoletion of terms whose only supporting article has been retracted eliminates: vocabulary entries with no surviving warrant - and ONLY those: a retracted source obsoletes the term 'for which no other supporting evidence exists' · outcome: held | controls[1] | 0.85 | the tagger's words |
| controls | internal review, consistency and QA checks, and expert biocurator collaboration eliminates: 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' · outcome: held | controls[2] | 0.95 | the tagger's words |
| controls | whether the knowledge is representative of life rather than of laboratories eliminates: nothing, and the abstract says so: 'most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms' · outcome: absent-declared-unreported | controls[3] | 0.95 | the tagger's words |
| concepts | GO knowledgebase also in: GO knowledgebase | concepts[0] | 0.95 | the paper's words |
| concepts | three components also in: three components | concepts[1] | 0.95 | the paper's words |
| concepts | GO annotations also in: GO annotations | concepts[2] | 0.95 | the paper's words |
| concepts | GO annotation also in: GO annotation | concepts[3] | 0.95 | the paper's words |
| concepts | an association between a specific gene | concepts[4] | 0.95 | the paper's words |
| concepts | subclass also in: subclass | concepts[5] | 0.95 | the paper's words |
| concepts | DNA-binding transcription factor activity | concepts[6] | 0.95 | the paper's words |
| concepts | transcription regulator activity also in: transcription regulator activity | concepts[7] | 0.95 | the paper's words |
| concepts | more specific than also in: more specific than | concepts[8] | 0.95 | the paper's words |
| concepts | obsoleted also in: obsoleted | concepts[9] | 0.95 | the paper's words |
| concepts | interleukin-14 also in: interleukin-14 | concepts[10] | 0.95 | the paper's words |
| concepts | GO-CAMs also in: GO-CAMs | concepts[11] | 0.95 | the paper's words |
| concepts | GO Causal Activity Models also in: GO Causal Activity Models | concepts[12] | 0.9 | the paper's words |
| concepts | range of in vivo substrate specificity | concepts[13] | 0.95 | the paper's words |
| concepts | each separate molecular substrate tested in a single, isolated experimental assay or result | concepts[14] | 0.95 | the paper's words |
| concepts | nonphysiological substrates also in: nonphysiological substrates | concepts[15] | 0.95 | the paper's words |
| concepts | biological substrate specificity range | concepts[16] | 0.95 | the paper's words |
| concepts | broad specificity enzymes and transporters | concepts[17] | 0.95 | the paper's words |
| concepts | activity on a specific substrate in a specific pathway | concepts[18] | 0.95 | the paper's words |
| concepts | GO-CAM also in: GO-CAM | concepts[19] | 0.95 | the paper's words |
| concepts | annotation extension also in: annotation extension | concepts[20] | 0.95 | the paper's words |
| concepts | GO term also in: GO term | concepts[21] | 0.95 | the paper's words |
| concepts | experimentally supported gene function annotations | concepts[22] | 0.95 | the paper's words |
| concepts | annotation groups also in: annotation groups | concepts[23] | 0.95 | the paper's words |
| concepts | invalidated by later experimental data | concepts[24] | 0.95 | the paper's words |
| concepts | manually reviewed also in: manually reviewed | concepts[25] | 0.95 | the paper's words |
| concepts | additional quality control also in: additional quality control | concepts[26] | 0.95 | the paper's words |
| concepts | underlying evidence also in: underlying evidence | concepts[27] | 0.95 | the paper's words |
| concepts | accuracy of a given annotation also in: accuracy of a given annotation | concepts[28] | 0.95 | the paper's words |
| concepts | Phylogenetic Annotation using GO (PAN-GO) project | concepts[29] | 0.95 | the paper's words |
| concepts | biocurator-reviewed, selected GO annotations | concepts[30] | 0.95 | the paper's words |
| concepts | PAINT software tool also in: PAINT software tool | concepts[31] | 0.95 | the paper's words |
| concepts | experimentally supported GO annotations | concepts[32] | 0.95 | the paper's words |
| concepts | protein family also in: protein family | concepts[33] | 0.95 | the paper's words |
| concepts | phylogenetic tree also in: phylogenetic tree | concepts[34] | 0.95 | the paper's words |
| concepts | PANTHER resource also in: PANTHER resource | concepts[35] | 0.95 | the paper's words |
| concepts | informative and nonredundant GO terms | concepts[36] | 0.95 | the paper's words |
| concepts | evolution of these characteristics also in: evolution of these characteristics | concepts[37] | 0.95 | the paper's words |
| concepts | gained or lost also in: gained or lost | concepts[38] | 0.95 | the paper's words |
| concepts | duplications also in: duplications | concepts[39] | 0.95 | the paper's words |
| concepts | mutations also in: mutations | concepts[40] | 0.95 | the paper's words |
| concepts | horizontal gene transfers also in: horizontal gene transfers | concepts[41] | 0.95 | the paper's words |
| concepts | taxonomic specificity also in: taxonomic specificity | concepts[42] | 0.95 | the paper's words |
| concepts | experimental evidence also in: experimental evidence | concepts[43] | 0.95 | the paper's words |
| concepts | PAN-GO annotations also in: PAN-GO annotations | concepts[44] | 0.95 | the paper's words |
| concepts | IBA also in: IBA | concepts[45] | 0.95 | the paper's words |
| concepts | inferred from biological ancestor also in: inferred from biological ancestor | concepts[46] | 0.95 | the paper's words |
| concepts | protein binding also in: protein binding | concepts[47] | 0.95 | the paper's words |
| concepts | protein binding partner also in: protein binding partner | concepts[48] | 0.95 | the paper's words |
| concepts | gene set enrichment analysis also in: gene set enrichment analysis | concepts[49] | 0.95 | the paper's words |
| concepts | protein–protein interaction network construction | concepts[50] | 0.95 | the paper's words |
| concepts | molecular activity of proteins also in: molecular activity of proteins | concepts[51] | 0.95 | the paper's words |
| concepts | limited functional description also in: limited functional description | concepts[52] | 0.95 | the paper's words |
| concepts | evidence also in: evidence | concepts[53] | 0.95 | the paper's words |
| concepts | two fields also in: two fields | concepts[54] | 0.95 | the paper's words |
| concepts | evidence code also in: evidence code | concepts[55] | 0.95 | the paper's words |
| concepts | reference also in: reference | concepts[56] | 0.95 | the paper's words |
| concepts | persistent identifier also in: persistent identifier | concepts[57] | 0.95 | the paper's words |
| concepts | provenance also in: provenance | concepts[58] | 0.95 | the paper's words |
| concepts | type of experimental evidence also in: type of experimental evidence | concepts[59] | 0.95 | the paper's words |
| concepts | level of review also in: level of review | concepts[60] | 0.95 | the paper's words |
| concepts | many genes also in: many genes | concepts[61] | 0.95 | the paper's words |
| concepts | individually reviewed also in: individually reviewed | concepts[62] | 0.95 | the paper's words |
| concepts | nonexperimental evidence types also in: nonexperimental evidence types | concepts[63] | 0.95 | the paper's words |
| concepts | Phylogenetic annotations also in: Phylogenetic annotations | concepts[64] | 0.95 | the paper's words |
| concepts | expert assessment also in: expert assessment | concepts[65] | 0.95 | the paper's words |
| concepts | Inferred from Electronic Annotation (IEA) evidence code | concepts[66] | 0.95 | the paper's words |
| concepts | automated also in: automated | concepts[67] | 0.95 | the paper's words |
| concepts | expert review also in: expert review | concepts[68] | 0.95 | the paper's words |
| concepts | false positives also in: false positives | concepts[69] | 0.95 | the paper's words |
| concepts | accurate also in: accurate | concepts[70] | 0.95 | the paper's words |
| concepts | less specific also in: less specific | concepts[71] | 0.95 | the paper's words |
| concepts | GO evidence codes also in: GO evidence codes | concepts[72] | 0.95 | the paper's words |
| concepts | Evidence and Conclusion Ontology (ECO) | concepts[73] | 0.95 | the paper's words |
| concepts | GO internal references (GO_REFs) also in: GO internal references (GO_REFs) | concepts[74] | 0.95 | the paper's words |
| concepts | granular ECO terms also in: granular ECO terms | concepts[75] | 0.95 | the paper's words |
| concepts | GO evidence code abbreviations also in: GO evidence code abbreviations | concepts[76] | 0.95 | the paper's words |
| concepts | models of causal influences also in: models of causal influences | concepts[77] | 0.95 | the paper's words |
| concepts | gene products also in: gene products | concepts[78] | 0.95 | the paper's words |
| concepts | causal relations also in: causal relations | concepts[79] | 0.95 | the paper's words |
| concepts | effect of one activity on the other also in: effect of one activity on the other | concepts[80] | 0.95 | the paper's words |
| concepts | element of a GO-CAM also in: element of a GO-CAM | concepts[81] | 0.95 | the paper's words |
| concepts | ontology class also in: ontology class | concepts[82] | 0.95 | the paper's words |
| concepts | standard database identifiers also in: standard database identifiers | concepts[83] | 0.95 | the paper's words |
| concepts | computational analysis also in: computational analysis | concepts[84] | 0.95 | the paper's words |
| concepts | gene product activity unit also in: gene product activity unit | concepts[85] | 0.95 | the paper's words |
| concepts | GO MF annotation also in: GO MF annotation | concepts[86] | 0.95 | the paper's words |
| concepts | GO CC also in: GO CC | concepts[87] | 0.95 | the paper's words |
| concepts | GO BP also in: GO BP | concepts[88] | 0.95 | the paper's words |
| concepts | biological context also in: biological context | concepts[89] | 0.95 | the paper's words |
| concepts | other ontologies also in: other ontologies | concepts[90] | 0.95 | the paper's words |
| concepts | cell type also in: cell type | concepts[91] | 0.95 | the paper's words |
| concepts | activity units also in: activity units | concepts[92] | 0.95 | the paper's words |
| concepts | causal relationships also in: causal relationships | concepts[93] | 0.95 | the paper's words |
| concepts | Relations Ontology also in: Relations Ontology | concepts[94] | 0.95 | the paper's words |
| concepts | larger pathways, modules, or processes | concepts[95] | 0.95 | the paper's words |
| concepts | molecular pathways also in: molecular pathways | concepts[96] | 0.95 | the paper's words |
| concepts | at least three distinct gene product activities linked into a causal chain | concepts[97] | 0.95 | the paper's words |
| concepts | curation priorities also in: curation priorities | concepts[98] | 0.95 | the paper's words |
| concepts | pathway diagrams also in: pathway diagrams | concepts[99] | 0.95 | the paper's words |
| concepts | evidence for each assertion in the model | concepts[100] | 0.95 | the paper's words |
| concepts | stable three-dimensional structure also in: stable three-dimensional structure | concepts[101] | 0.95 | the paper's words |
| concepts | intrinsically disordered regions (IDRs) | concepts[102] | 0.95 | the paper's words |
| concepts | Intrinsically Disordered Proteins Ontology (IDPO) | concepts[103] | 0.95 | the paper's words |
| concepts | structural states also in: structural states | concepts[104] | 0.95 | the paper's words |
| concepts | modes of action also in: modes of action | concepts[105] | 0.95 | the paper's words |
| concepts | self-regulatory also in: self-regulatory | concepts[106] | 0.95 | the paper's words |
| concepts | intrinsic disorder-specific functions | concepts[107] | 0.95 | the paper's words |
| concepts | host–symbiont branch also in: host–symbiont branch | concepts[108] | 0.95 | the paper's words |
| concepts | current scientific knowledge also in: current scientific knowledge | concepts[109] | 0.95 | the paper's words |
| concepts | Symbionts in GO also in: Symbionts in GO | concepts[110] | 0.95 | the paper's words |
| concepts | pathogens that infect a host organism | concepts[111] | 0.95 | the paper's words |
| concepts | hijack various host cellular processes | concepts[112] | 0.95 | the paper's words |
| concepts | evolved role also in: evolved role | concepts[113] | 0.95 | the paper's words |
| concepts | Rhea database of reactions also in: Rhea database of reactions | concepts[114] | 0.95 | the paper's words |
| concepts | ChEBI ontology of chemical entities also in: ChEBI ontology of chemical entities | concepts[115] | 0.95 | the paper's words |
| concepts | in vivo biochemical reactions also in: in vivo biochemical reactions | concepts[116] | 0.95 | the paper's words |
| concepts | chemical entity participants also in: chemical entity participants | concepts[117] | 0.95 | the paper's words |
| concepts | stoichiometry also in: stoichiometry | concepts[118] | 0.95 | the paper's words |
| concepts | standardized, consistent manner also in: standardized, consistent manner | concepts[119] | 0.95 | the paper's words |
| concepts | catalytic activity branch also in: catalytic activity branch | concepts[120] | 0.95 | the paper's words |
| concepts | additional detailed reaction information | concepts[121] | 0.95 | the paper's words |
| concepts | nonexact matches also in: nonexact matches | concepts[122] | 0.95 | the paper's words |
| concepts | metabolomics analyses also in: metabolomics analyses | concepts[123] | 0.95 | the paper's words |
| concepts | three editions of the ontology also in: three editions of the ontology | concepts[124] | 0.95 | the paper's words |
| concepts | go-basic also in: go-basic | concepts[125] | 0.95 | the paper's words |
| concepts | go-plus also in: go-plus | concepts[126] | 0.95 | the paper's words |
| concepts | set of relations also in: set of relations | concepts[127] | 0.95 | the paper's words |
| concepts | is a, part of, regulates, negatively regulates, and positively regulates relationships | concepts[128] | 0.95 | the paper's words |
| concepts | cross different aspects also in: cross different aspects | concepts[129] | 0.95 | the paper's words |
| concepts | acyclic also in: acyclic | concepts[130] | 0.95 | the paper's words |
| concepts | selectively propagate annotations also in: selectively propagate annotations | concepts[131] | 0.95 | the paper's words |
| concepts | fully axiomatized edition also in: fully axiomatized edition | concepts[132] | 0.95 | the paper's words |
| concepts | cross-ontology relationships also in: cross-ontology relationships | concepts[133] | 0.95 | the paper's words |
| concepts | external ontologies also in: external ontologies | concepts[134] | 0.95 | the paper's words |
| concepts | GO subsets also in: GO subsets | concepts[135] | 0.95 | the paper's words |
| concepts | condensed versions of the GO also in: condensed versions of the GO | concepts[136] | 0.95 | the paper's words |
| concepts | portion of the terms also in: portion of the terms | concepts[137] | 0.95 | the paper's words |
| concepts | global overview also in: global overview | concepts[138] | 0.95 | the paper's words |
| concepts | GO Generic subset also in: GO Generic subset | concepts[139] | 0.95 | the paper's words |
| concepts | as little redundancy as possible also in: as little redundancy as possible | concepts[140] | 0.95 | the paper's words |
| concepts | causal flow representation also in: causal flow representation | concepts[141] | 0.95 | the paper's words |
| concepts | chemical reaction-centered representation | concepts[142] | 0.95 | the paper's words |
| concepts | automated pathway visualization also in: automated pathway visualization | concepts[143] | 0.95 | the paper's words |
| concepts | causal links also in: causal links | concepts[144] | 0.95 | the paper's words |
| concepts | more granular gene sets also in: more granular gene sets | concepts[145] | 0.95 | the paper's words |
| concepts | enrichment analysis also in: enrichment analysis | concepts[146] | 0.95 | the paper's words |
| concepts | automated descriptions of gene function | concepts[147] | 0.95 | the paper's words |
| concepts | level of biological organization also in: level of biological organization | concepts[148] | 0.95 | the paper's words |
| concepts | MFs also in: MFs | concepts[149] | 0.95 | the paper's words |
| concepts | BPs also in: BPs | concepts[150] | 0.95 | the paper's words |
| concepts | individual cells also in: individual cells | concepts[151] | 0.95 | the paper's words |
| concepts | multicellular organisms also in: multicellular organisms | concepts[152] | 0.95 | the paper's words |
| concepts | homeostasis also in: homeostasis | concepts[153] | 0.95 | the paper's words |
| concepts | steady-state level within a cell also in: steady-state level within a cell | concepts[154] | 0.95 | the paper's words |
| concepts | steady state in blood or other fluid also in: steady state in blood or other fluid | concepts[155] | 0.95 | the paper's words |
| unhoused_relations | 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. | unhoused_relations[0] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[1] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[2] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[3] | 0.95 | the tagger's words |
| unhoused_relations | causal relationships → Relations Ontology The causal relationships linking activity units come from the Relations Ontology. Sourced-from is absent from the eight types. | unhoused_relations[4] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[5] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[6] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[7] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[8] | 0.95 | the tagger's words |
| unhoused_relations | 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. | unhoused_relations[9] | 0.95 | the tagger's words |
| senses | `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 | senses[0] | 0.95 | the tagger's words |
| senses | `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 | senses[1] | 0.95 | the tagger's words |
| senses | `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 | senses[2] | 0.95 | the tagger's words |
| senses | 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` | senses[3] | 0.95 | the tagger's words |
| senses | `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. | senses[4] | 0.95 | the tagger's words |
| senses | Ontology-term granularity - represent an in-vivo specificity range, not every isolated assay substrate | senses[5] | 0.95 | the tagger's words |
| senses | Contextual substrate activity - capture pathway-specific detail in a GO-CAM or annotation extension rather than a GO term | senses[6] | 0.95 | the tagger's words |
| senses | Annotation maintenance - edit or remove claims after ontology revision or experimental invalidation, with manual reassignment where possible | senses[7] | 0.95 | the tagger's words |
| senses | PAN-GO curation - review a whole protein family in its phylogenetic tree and select informative, nonredundant terms | senses[8] | 0.95 | the tagger's words |
| senses | Phylogenetic propagation - model gains and losses along branches while retaining traceability to experimental evidence | senses[9] | 0.95 | the tagger's words |
| senses | Direct protein-binding annotation - partner-specific data suited to interaction-network construction but not gene-set enrichment | senses[10] | 0.95 | the tagger's words |
| senses | Annotation evidence - a code for evidence type plus a persistent-reference provenance field | senses[11] | 0.95 | the tagger's words |
| senses | Evidence reliability - evidence type and annotation review level must be considered jointly | senses[12] | 0.95 | the tagger's words |
| senses | High-throughput evidence - many-gene experiment annotations not individually reviewed by authors or a GO biocurator | senses[13] | 0.95 | the tagger's words |
| senses | IBA review - experimental annotation reviewed once directly and again in the context of related genes | senses[14] | 0.95 | the tagger's words |
| senses | IEA annotation - automated but often pipeline-level expert-reviewed, regarded as accurate though less specific | senses[15] | 0.95 | the tagger's words |
| senses | GO-CAM - a highly structured model linking gene-product molecular activities by causal relations and adding location and process context | senses[16] | 0.95 | the tagger's words |
| senses | Gene product activity unit - molecular activity combined with cellular location and larger functional-module context | senses[17] | 0.95 | the tagger's words |
| senses | Molecular pathway in the reported model count - at least three distinct gene-product activities connected as a causal chain | senses[18] | 0.95 | the tagger's words |
| senses | Intrinsically disordered protein - lacks stable three-dimensional structure and contains flexible, unstructured regions | senses[19] | 0.95 | the tagger's words |
| senses | Broad GO symbiont - includes pathogens infecting a host, not only mutually beneficial organisms | senses[20] | 0.95 | the tagger's words |
| senses | Rhea–GO division of labor - overlapping catalytic activities with Rhea carrying more precise participants, stoichiometry, and sometimes specificity | senses[21] | 0.95 | the tagger's words |
| senses | go-basic - acyclic relation set safe for annotation propagation and recommended for most GO software | senses[22] | 0.95 | the tagger's words |
| senses | go-plus - fully axiomatized GO edition that links into external ontologies | senses[23] | 0.95 | the tagger's words |
| senses | GO subset - tagged condensed selection providing a low-redundancy functional overview | senses[24] | 0.95 | the tagger's words |
| senses | Homeostasis scale ambiguity - the same term can denote intracellular steady level or organismal blood/fluid regulation | senses[25] | 0.95 | the tagger's words |
| alternatives | claim_type: method a second reading reaching the same value. Recorded: method — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.claim_type | 0.75 | the tagger's words |
| alternatives | method: database-resource-description a second reading DIFFERING from the recorded value. Recorded: system-or-tool-building — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.method | 0.8 | the tagger's words |
| alternatives | 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 — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.phenomenon | 0.9 | the tagger's words |
| claim_relations | 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 | claim_relations[0] | 0.95 | the tagger's words |
| claim_relations | supports → the position that a knowledge resource must be curated and versioned rather than accumulated - 'not just an archive of all public data' | claim_relations[1] | 0.95 | the tagger's words |
| concept_edges | three components —part-of→ GO knowledgebase strength of the relation: 0.95 · note: 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
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. also in: three components · GO knowledgebase | concept_edges[0] | 0.95 | the tagger's words |
| concept_edges | GO annotation —is-a→ an association between a specific gene strength of the relation: 0.95 · note: '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 | concept_edges[1] | 0.95 | the tagger's words |
| concept_edges | DNA-binding transcription factor activity —is-a→ transcription regulator activity strength of the relation: 0.95 · note: the paper's demonstration of its own relation type, given in the sentence that defines it | concept_edges[2] | 0.95 | the tagger's words |
| concept_edges | subclass —same-as→ more specific than strength of the relation: 0.95 · note: '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 also in: subclass · more specific than | concept_edges[3] | 0.95 | the tagger's words |
| concept_edges | GO-CAMs —part-of→ GO knowledgebase the relation itself is separately evidenced · strength of the relation: 1 · note: '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 relation stated: The GO knowledgebase consists of three components: (1) the GO ... (2) GO annotations ... and (3) GO Causal Activity Models (GO-CAMs) also in: GO-CAMs · GO knowledgebase | concept_edges[4] | 0.95 | the tagger's words |
| concept_edges | GO annotations —part-of→ GO knowledgebase the relation itself is separately evidenced · strength of the relation: 1 · note: The second component. Both endpoints were already concept nodes; the edge joining them was not there. · the relation stated: The GO knowledgebase consists of three components: (1) the GO ... (2) GO annotations ... and (3) GO Causal Activity Models (GO-CAMs) also in: GO annotations · GO knowledgebase | concept_edges[5] | 0.95 | the tagger's words |
| concept_edges | GO-CAMs —same-as→ GO Causal Activity Models the relation itself is separately evidenced · strength of the relation: 1 · note: The parenthetical first-use expansion directly equates the abbreviation and full name. also in: GO-CAMs · GO Causal Activity Models | concept_edges[6] | 0.95 | the tagger's words |
| concept_edges | range of in vivo substrate specificity —distinct-from→ each separate molecular substrate tested in a single, isolated experimental assay or result the relation itself is separately evidenced · strength of the relation: 1 · note: The paper explicitly contrasts its current biological-range guidance with earlier assay-by-assay terms. | concept_edges[7] | 0.95 | the tagger's words |
| concept_edges | IBA —same-as→ inferred from biological ancestor the relation itself is separately evidenced · strength of the relation: 1 · note: The abbreviation is immediately expanded in parentheses. also in: IBA · inferred from biological ancestor | concept_edges[10] | 0.95 | the tagger's words |
| concept_edges | gene set enrichment analysis —contrasted-with→ protein–protein interaction network construction the relation itself is separately evidenced · strength of the relation: 1 · note: The former is discouraged and the latter recommended for direct protein-binding annotations. | concept_edges[11] | 0.95 | the tagger's words |
| concept_edges | element of a GO-CAM —is-a→ ontology class the relation itself is separately evidenced · strength of the relation: 1 · note: Each model element is explicitly said to be an instance of an ontology class or standard identifier. also in: element of a GO-CAM · ontology class | concept_edges[12] | 0.95 | the tagger's words |
| concept_edges | molecular pathways —operationalised-as→ at least three distinct gene product activities linked into a causal chain the relation itself is separately evidenced · strength of the relation: 1 · note: This is the paper's stated definition for counting a GO-CAM as a molecular pathway. | concept_edges[13] | 0.95 | the tagger's words |
| concept_edges | pathogens that infect a host organism —part-of→ Symbionts in GO the relation itself is separately evidenced · strength of the relation: 1 · note: The GO category is explicitly defined broadly to include infecting pathogens. | concept_edges[14] | 0.95 | the tagger's words |
| concept_edges | cross-ontology relationships —part-of→ go-plus the relation itself is separately evidenced · strength of the relation: 1 · note: The fully axiomatized edition includes these links to external ontologies. also in: cross-ontology relationships · go-plus | concept_edges[15] | 0.95 | the tagger's words |
| concept_edges | portion of the terms —part-of→ GO subsets the relation itself is separately evidenced · strength of the relation: 1 · note: A subset contains the tag-specified portion of GO terms. also in: portion of the terms · GO subsets | concept_edges[16] | 0.95 | the tagger's words |
| concept_edges | MFs —distinct-from→ BPs the relation itself is separately evidenced · strength of the relation: 1 · note: The paper names distinguishing molecular functions from biological processes as an immediate ontology need. | concept_edges[17] | 0.95 | the tagger's words |
| content_tags | the GO knowledgebase has three components: the ontology, evidence-supported GO annotations, and GO-CAM models linking annotations by defined relations | content_tags.findings[0] | 0.95 | the tagger's words |
| content_tags | AND ONE TAG CARRIES ONE CLAIM: `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] | 0.95 | the tagger's words |
| content_tags | GO terms from retracted articles with no other supporting evidence are obsoleted | content_tags.findings[2] | 0.85 | the tagger's words |
| content_tags | AN ERRATUM CASCADING THROUGH A VOCABULARY, EVERY CASUALTY NAMED: an erratum showed `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] | 0.95 | the tagger's words |
| content_tags | biological process terms show a net decrease of over 800 over two years, while MF and CC stayed roughly constant | content_tags.findings[4] | 0.95 | the tagger's words |
| content_tags | and the principle behind it is stated: the knowledgebase must `represent the current state of knowledge and are not just an archive of all public data ... citable, versioned updates are released on a monthly basis` | content_tags.findings[5] | 0.95 | the tagger's words |
| content_tags | A VOCABULARY DECLINING TO REPRESENT SOMETHING TRUE: two terms `were obsoleted because they represent substeps of reactions which are beyond the scope of GO` | content_tags.findings[6] | 0.95 | the tagger's words |
| content_tags | TRACEABILITY IS WHAT MAKES CORRECTION POSSIBLE: `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 authors can `retrieve and review all annotations from a given paper and suggest changes` | content_tags.findings[7] | 0.95 | the tagger's words |
| content_tags | AN EDITION 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` | content_tags.findings[8] | 0.95 | the tagger's words |
| content_tags | THE COVERAGE BIAS IS IN THE ABSTRACT: `most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms` | content_tags.findings[9] | 0.95 | the tagger's words |
| content_tags | THREE SENSES OF `FUNCTION` IN ONE ONTOLOGY: molecular function is an ACTIVITY, cellular component is a LOCATION, biological process is a PROGRAMME `comprising molecular activities acting in concert to achieve a particular outcome` | content_tags.findings[10] | 0.95 | the tagger's words |
| content_tags | revisions also come from internal review, consistency and quality checks, and user feedback | content_tags.findings[11] | 0.95 | the tagger's words |
| content_tags | `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`, with `the evidence upon which it is based` also in: GO annotation | content_tags.definitions[0] | 0.95 | the tagger's words |
| content_tags | `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` also in: obsoleted | content_tags.definitions[1] | 0.95 | the tagger's words |
| content_tags | `one term being MORE SPECIFIC THAN another term (ALSO CALLED subclass)` also in: subclass | content_tags.definitions[2] | 0.95 | the tagger's words |
| content_tags | 782 — biological-process terms obsoleted in two years, against 217 added - a net decrease of over 800 once merges are counted value source note: 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] | 0.95 | the tagger's words |
| content_tags | 7 — GO terms obsoleted because one erratum showed interleukin-14 had been incorrectly predicted | content_tags.quantities[1] | 0.95 | the tagger's words |
| content_tags | 3 — the ontology, the evidence-supported annotations, and the causal activity models | content_tags.quantities[2] | 0.95 | the tagger's words |
| content_tags | the change type counted per aspect per release, alongside added and merged also in: obsoleted | content_tags.variables[0] | 0.95 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.