GO FAQs

These are our most frequently asked questions. If you don’t find your answer below, please contact us.

Browse the GO FAQ by topic:

General: Overview

What is the GO?

The Gene Ontology (GO) project is a collaborative effort to address the need for consistent descriptions of gene products in different databases. The GO collaborators are developing three structured, controlled vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. There are three separate aspects to this effort: first, we write and maintain the ontologies themselves; second, we make cross-links between the ontologies and the genes and gene products in the collaborating databases; and third, we develop tools that facilitate the creation, maintenance and use of ontologies.

The use of GO terms by several collaborating databases facilitates uniform queries across them. The controlled vocabularies are structured so that you can query them at different levels: for example, you can use GO to find all the gene products in the mouse genome that are involved in signal transduction, or you can zoom in on all the receptor tyrosine kinases. This structure also allows annotators to assign properties to gene products at different levels, depending on how much is known about a gene product.

What are all the possible uses of GO?

It would be impossible to list all the potential applications of GO, but applications for which GO has already been used include the following:

integrating proteomic information from different organisms;
assigning functions to protein domains;
finding functional similarities in genes that are overexpressed or underexpressed in diseases and as we age;
predicting the likelihood that a particular gene is involved in diseases that haven’t yet been mapped to specific genes;
analysing groups of genes that are co-expressed during development;
developing automated ways of deriving information about gene function from the literature;
verifying models of genetic, metabolic and product interaction networks.

For references to these and other studies that have used GO, see the GO and the scientific literature page.

Why do we need GO?

To ask meaningful questions, biologists often need to retrieve and analyse data from disparate sources. For example, if you were searching for new targets for antibiotics, you might want to find all the gene products that are involved in bacterial protein synthesis, but that have significantly different sequences or structures from those in humans. But if one database describes these molecules as being involved in ‘translation’, whereas another uses the phrase ‘protein synthesis’, it will be difficult for you - and even harder for a computer - to find functionally equivalent terms.

The Gene Ontology (GO) project is a collaborative effort to address the need for consistent descriptions of gene products in different databases. The GO collaborators are developing three ontologies - a word used by computer scientists to mean ‘specifications of a relational vocabulary’ - that describe biological processes, cellular components and molecular functions in a species-independent manner.

Ontologies provide a vocabulary for representing and communicating knowledge about a topic, and a set of relationships that hold among the terms of the vocabulary. They can be structurally very complex, or relatively simple. Most importantly, ontologies capture domain knowledge in a way that can easily be dealt with by a computer . Because the terms in an ontology and the relationships between the terms are carefully defined, the use of ontologies facilitates making standard annotations, improves computational queries, and can support the construction of inference statements from the information at hand.

Genomic sequencing projects and microarray experiments alike produce electronically-generated data flows that require computer accessible systems to work with the information. As systems that make domain knowledge available to both humans and computers, bio-ontologies such as GO and the many other bio-ontologies being created (see the OBO web page for some examples) for are essential to the process of extracting biological insight from enormous sets of data.

Which biological domains are supported by GO?

The current ontologies of the GO project are molecular function, biological process, and cellular component. The ontologies are developed to include all terms falling into these domains without consideration of whether the biological attribute is restricted to certain taxonomic groups. Therefore, biological processes that occur only in plants (e.g. photosynthesis) or mammals (e.g. lactation) are included. Other biological ontologies are discussed in the OBO web site.

What is GO “content”?

GO content refers to the content of the ontologies themselves and the biology underlying it. It includes anything to do with terms and their organisation, definitions, synonyms and the relationships between terms.

I want to use GO, but I don’t know where to begin…

There are a number of possibilities for how researchers can make use of the GO.

The Gene Ontology website (http://geneontology.org/) is a very good place to begin learning about our resources, how they are produced, and how we maintain them. It also illustrates how the research community most commonly makes use of these resources and how they can contribute. Exploring the items under the “Ontology” and “Annotations” on the menu will provide you with a very informative overview.

For more detail, please consult the open access The Gene Ontology Handbook, available online and as a downloadable PDF. As well as GO best practices and a discussion on the meaning of “function”, this text covers everything from introducing ontologies, to using GO resources in Python, and even how GO and similar ontologies may be used in clinical settings.

What am I allowed to do with the data?

The use and license of all GO data, software, and materials are covered on the Use and license page.

How is the GO used in genome analysis?

Functional annotation of newly sequenced genomes: Genome and full-length cDNA sequence projects often include computational (putative) assignments of molecular function based on sequence similarity to annotated genes or sequences. A common tactic now is to use a computational approach to establish some threshold sequence similarity to a SWISS-PROT sequence. Then the GO associations to the SWISS-PROT sequence can be retrieved and associated with the gene model. Under the GO guidelines, the evidence code for this event would be ‘inferred from electronic annotation’ (IEA).

Functional groupings of gene products: One aspect of the use of the GO for annotation of large data sets is the ability to group gene products to some high level term. For example, while gene products may be precisely annotated as having role in a particular function in carbohydrate metabolism (i.e., glucose catabolism), in the summary documentation of the data set, all gene products functioning in carbohydrate metabolism could be grouped together as being involved in the more general phenomena ‘carbohydrate metabolism’. Various sets of GO terms have been used to summarize experimental data sets in this way. The expectation is that published sets of high-level GO terms used in genome annotations and publications will be archived at the GO site. Some of these ‘GO slims’ are already available.

General: People & Funding

How can I contribute to GO?

We welcome your contributions!

The GO project is constantly evolving, and we welcome feedback from all users. Research groups may contribute to the GOC by either providing suggestions for updating the ontology (e.g. requests for new terms) or by providing annotations, that is, associations between genes or gene products and ontology terms. Suggested edits are reviewed by the ontology editors and implemented where appropriate.

To learn more about the best approach to contributing GO annotations, please visit our documentation on Contributing annotations. To suggest updates to the ontology please visit our documentation on Contributing to GO.

Please be sure to contact the GOC before carrying out any annotation work you intend to submit; this will ensure that GOC mentors and trainers can be of assistance in producing data sets in agreement with the GOC annotation policies and format requirements.

Who makes up the GO Consortium?

Back in 1998, the GO project began as a collaboration between three model organism databases, namely Flybase (Drosophila), the Saccharomyces Genome Database (SGD), and the Mouse Genome Database (MGD). Today, the GO Consortium is formed by many databases, including several of the world’s major repositories for plant, animal, and microbial genomes. Visit this page to see a complete list of member organizations of the Gene Ontology Consortium.

How do I become a member of the GO Consortium?

The most important criterion for GO Consortium membership is that the members contribute something to the collection of resources that we make available to the public (almost all members contribute annotations; several contribute to the ontologies; a few contribute software). The scientists involved in working with GO in these member groups communicate via the GO mailing lists and GitHub to discuss development issues in the ontologies. If you represent a database that wishes to join the GO Consortium please contact the GOC.

Anyone with a more general interest in the GO should subscribe to the Twitter feed (@news4go) to receive updates about the GO.

Who funds GO?

Direct support for the Gene Ontology Consortium is provided by an R01 grant from the National Human Genome Research Institute (NHGRI) [grant HG02273]. Funding for the GO member organizations is detailed on our annotation contributors page.

General: Citing the GO

How do I cite the GO?

Citation information for the Gene Ontology can be found on the GO Citation Policy page.

What is the minimum information to include in a functional analysis paper?

Most journals require authors to submit high-throughput data to public repository as a prerequisite for publication. As part of this process, the methods used to analyse data need to be reported in detail; this applies to both statistical and functional analysis. For papers describing enrichment analysis using GO, this means that the methods section should include the following information, to ensure the analysis is reproducible (an important criteria for reviewers’ approval):

What analysis tool was used and what version
What statistical analysis method was applied, and what correction factors were applied if any
Date/release version of both the GO ontology file and the GO annotation file used
Background genome/proteome/dataset used in the analysis, including strain if applicable
Whether any enriched terms were excluded from the results due to low numbers of query genes associated with the term (e.g., if you only included GO terms in the results which have more than 3 query genes)
Please cite the appropriate GO papers

The supplemental data files should include:

List of the IDs used and the IDs which were rejected by the analysis tool, if any
Full list of enriched terms

When undertaking the functional analysis and interpreting the results, consider:

Is the number of genes analysed statistically valid? Or is the number too small to observe enrichment, or too large for the enrichment to be meaningful. (E.g., for a microarray experiment with ~25,000 interrogated transcripts, it would be difficult to observe enrichment when analysing less than 150 IDs; if the list were longer than 3,000 IDs, clearer results would require further filtering, e.g. based on significance threshold or fold change. This is only a rough guide.)
Consider using more than one functional analysis tool, as well as fine-tuning the parameters used. You may also wish to look at overlap between results from different approaches.
Think about the biology. E.g., if you need/wish to make a choice among enriched terms to show in a summary table, use descriptive GO terms. Do not only pick terms you’re particularly interested in, and consider that very broad or generic terms, such as ‘metabolic process’, can be uninformative.

Annotation

What is annotation?

What does it mean to do GO annotation of genes or proteins? Terms from the Gene Ontology are applied in the annotation of gene products or protein complexes in biological databases. GO annotations are associations made between gene products or protein complexes and the GO terms that describe them. An annotation also includes an evidence code and a reference that supports the gene product/term association.

How do I submit annotations to GO?

We welcome your contributions!

We welcome contributions to the Gene Ontology project, both in terms of annotations and for feedback and additions to the ontology.

Before making contributions, we recommend that you contact the Gene Ontology Consortium (GOC) before annotation work is carried out; this will ensure that GOC mentors and trainers can be of assistance in producing data sets in agreement with the GOC annotation policies and format requirements.

To learn more details, visit the page on contributing to GO.

What is a ‘gene product’?

GO uses the term ‘gene product’ to refer collectively to genes and any entities encoded by the gene, e.g. proteins and functional RNAs.

How are gene products associated with GO terms?

A gene product can be annotated to zero or more nodes of each ontology, at any level within each ontology; annotation of a gene product to one ontology is independent of its annotation to other ontologies. Annotations should reflect the normal function, process, or localization (component) of the gene product; an activity or location observed only in a mutant or disease state is therefore not usually included. The member databases of the GO Consortium use manual and automated methods to annotate genes or gene products using GO terms. Both manual and automated annotations are made according to two principles: first, every annotation must be attributed to a source, which may be a literature reference, another database or a computational analysis; second, the annotation must indicate what kind of evidence is found in the cited source to support the association between the gene product and the GO term. GO uses a simple controlled vocabulary to indicate the type of evidence found in the cited reference to support the annotation.

Can a single gene product be annotated with more than one GO term?

Yes!

It is possible and usually expected for a single gene/gene product to be associated with more than one GO term. The fact that you may have found that there are two or more different GO terms associated with a single gene/gene product in your results should not be a cause for concern.

The Gene Ontology allows users to describe a gene/gene product in detail, considering three main aspects: its molecular function, the biological process in which it participates, and its cellular location.

For example, the homeobox D9a gene product from zebrafish has numerous GO terms associated with it. Each term describes details about this gene’s molecular function, localization in the cell, or its involvement in certain biological processes. One GO term explains that this gene product carries out the molecular function of selectively interacting with DNA (DNA binding), while a different GO term explains that this gene product is found in the nucleus of the cell.

Trying to write one single term that describes in detail everything about a gene/gene product in a single statement would require the existence of as many terms as genes there are - for all species - in the planet. This would be very unpractical and not easily scalable. Instead, the use of ontologies help us organize information in a way that allows researchers to use the same term to describe a characteristic that is shared by more than one gene product (e.g. all the genes involved in the process ‘translation’), and more than one term to describe all the characteristics of each gene product, as in the example above. This is a reason why you would see more than one GO term associated to a single gene/gene product.

Can a gene or gene product be annotated to more than one term from an ontology?

Yes, a gene product can be annotated to zero or more nodes of each ontology, at any level within each ontology. See the introduction to GO annotations for more information.

Why are some gene products annotated to both a parent term and a child term?

This is done when there is explicit evidence to support separate annotations; usually it means that there is strong evidence for a more general annotation (parent term) and weaker evidence supporting a more specific annotation (child term).

From the GO annotation guide:

Uncertain knowledge of where a gene product operates should be denoted by annotating it to two nodes, one of which can be a parent of the other. For instance, a yeast gene product known to be in the nucleolus, but also experimentally observed in the nucleus generally, can be annotated to both nucleolus and nucleus in the cell component ontology. Even though annotation to nucleolus alone implies that a gene product is also in the nucleus, annotate to both so as to explicitly indicate that it has been reported in the two locations. The two annotations may have the same or different supporting evidence.

What is an evidence code?

Every annotation must be attributed to a source, which may be a literature reference, another database or a computational analysis. The annotation must indicate what kind of evidence is found in the cited source to support the association between the gene product and the GO term. A simple controlled vocabulary is used to record evidence; and the evidence codes are simply the three-letter codes used to signify the type of evidence cited. More information on the meaning and use of the evidence codes can be found in the GO evidence codes documentation.

Where can I view or download the complete sets of GO annotations?

Annotations from GO Consortium member groups can be downloaded here. These are taxon-specific, although some have multiple taxons in the file. For example, the CGD GAF includes several Candida as well as Debaryomyces hansenii and Lodderomyces elongisporus.

If your organism is included in a multi-organism file, and you would like to extract just your organism of interest, you can filter by taxon. Verify your taxon ID and refer to the file format guides under “Types of GO annotation files” to determine which column to sort on.

If your organism is not available from our downloads page, you can use AmiGO to view or download annotations. Filter for your taxon under “Organism”; AmiGO allows users to download up to 100,000 annotations. QuickGO is another GO browser, but has a download limit of 50,000.
Please see our FAQ on AmiGO and QuickGO data, software to browse the GO and finding species that are not available in AmiGO.

How do I access older versions of gene association files?

For files produced after 2018-03-06, please use the /annotation directory from http://release.geneontology.org/. GO annotations from 2018-03-06 to present are also accessible through the DOI-versioned release stored in Zenodo and can be retrieved using BDBag. Read more about programmatic access.

For files older than 2018-03-06, there is some data that is no longer maintained on our archive: http://archive.geneontology.org/full/ Please note, even though some files appear to be recently updated, this archive is no longer maintained and may refer to URLs that are no longer active.

If you do not find the release you are looking for, please contact us.

How do I find all annotations for species X that I can’t find in AmiGO?

Open QuickGO. Click on the “View GO annotations” box located beneath the search box. This will lead you to an Annotation browser page where you can filter icon based on taxon, evidence, GO term, gene product and more.

Click on taxon to input the taxon identifier of the species you would like to get GO annotations for (Example: 6279 for Brugia malayi). You can input more than one taxon.

Click Apply to get your results. Use the Statistics link to see the breakdown of the different annotations including Annotations per Reference, Gene products per Evidence, Annotations per Aspect, Gene products per GO ID, etc.

Sometimes the number of GO annotations changes significantly over a short period of time. Why?

Most annotations in association files are electronically inferred (IEA). As with all types of annotations, IEAs change over time, with an overall increasing trend. However, in the specific case of IEAs, significant fluctuations in numbers may sometimes be observed over a short period of time. Nearly always, these are not due to bugs, but rather to the following reasons and/or to a combination thereof:

All IEA annotations that are over one year old are removed from association files. This is part of quality control procedures. Another procedure the GO started implementing in mid-2014 are taxonomic checks. A technical summary of annotation QC checks may be found here: http://geneontology.org/page/annotation-quality-control
Electronic annotations are provided to UniProt-GOA by various groups, including Ensembl, InterPro and UniProt. UniProt-GOA then includes these in their annotation files that they submit to the GO Consortium. There are numerous reasons why electronic annotations can fluctuate; e.g., InterPro may have changed a mapping that affected a large number of annotations; a mapping between a GO term and a UniProt keyword may have been added or removed; Ensembl may have changed their orthology sets; new quality checking procedures may have been introduced; a supplying group may have had a problem providing the annotations. Since electronic annotations tend to hit a large number of proteins, it is more likely to observe larger fluctuations than one would in a manual annotation set. UniProt-GOA aims to record all the known changes to the datasets they provide in the release notes here: http://www.ebi.ac.uk/GOA/news
New genome assemblies for various species are periodically released, and that may contribute to changes in gene annotations.
Changes are good. Our knowledge foundation is growing and increasing and information is continuing to be added based on existing, older literature.
Relevant paper: Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt.

However, if you think that an observed change in the size of an annotation file cannot be explained by any of the above, and suspect a bug, please contact us using our Contact Form.

What is the difference between the filtered and unfiltered versions of the GOA UniProt gene associations files?

The filtered version available on the GO Download’s site (gene_association.goa_uniprot_noiea) does not contain annotations for those species where a different Consortium group is primarily responsible for providing GO annotations and also excludes annotations made using automated methods. For example, SGD is responsible for annotations for S. cerevisiae; GO annotations for S. cerevisiae are not part of the filtered version of the gene_association.goa_uniprot_noiea file. A filtered version of the UniProt GAF is available on the GO website (http://geneontology.org/page/download-annotations). The unfiltered version of the file is available on GOA’s FTP site- ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_uniprot_all.gaf.gz

What criteria are used to annotate genes with GO terms?

A variety of criteria are used for each annotation including experimental results, sequence similarity and curator judgement. See the GO annotation guide for more information.

How is annotation quality controlled to ensure consistency between databases?

The accuracy of GO annotations is a high priority for all members of the GO Consortium. Each member organization is responsible for keeping its own annotations accurate and up to date, and for correcting any errors. Users can report errors to the GO helpdesk; any comments on annotations will be forwarded to the appropriate contributing group.

The GO Consortium is also looking into possible ways to improve quality assurance further, such as manually reviewing selected annotations and developing tools to automate detection of potentially erroneous annotations.

What are the advantages and disadvantages of manual annotation?

The most reliable annotations are those made manually by database curators based on primary and occasionally on review literature. Manual annotations usually cite experimental evidence that provides strong support for the association of a GO term with a gene product, and can be done at a very detailed level. The chief disadvantage of manual annotation is that it is labor-intensive, requiring a lot of time and effort from trained biologists.

What are the advantages and disadvantages of automatic annotation?

One advantage of automatic annotation is speed: wholly or partially automated methods facilitate the annotation of much larger sets of known or predicted gene products than can be produced manually. Automated annotation methods generally yields more broad (less detailed) annotations compared to manual annotation.

How often does automatic annotation give results that are consistent with manual annotation?

In general, electronic annotations are rarely incorrect, as they are annotations to very high-level GO terms. For example, the GOA group at EBI reports:

Usually manual annotation simply provides deeper-level terms in GO. In 93% of cases GOA’s electronic annotation is in the same GO lineage as the manual annotation. Some users have used our manual annotation to assess the quality of their automatic GO annotation techniques. They have found a few manual annotation errors by Proteome Inc. but no errors (so far) of manual annotation by Swiss-Prot staff have been reported to GOA. A few InterPro2GO errors have been reported but not very many. So, in general, our electronic techniques are very accurate, and are sometimes based on manual annotation. For example, Swiss-Prot keywords are usually manually annotated to Swiss-Prot entries; by using a mapping of Swiss-Prot keywords to GO, GOA inherits the high quality of Swiss-Prot manual annotation.

Text from Clark, et al., 2005

“The quality of electronic annotation has recently been assessed in some detail (Camon et al., 2005). This research found that in the worst case scenario, the generation of electronic annotations using the interpro2go, spkw2go, and ec2go mapping files precisely predicted the correct GO term 60% to 70% of the time, with the remainder of the predictions being to insufficiently specific GO terms. The high precision was found to be due to the basing of electronic annotations on manually curated mapping files. Curators noted that it was more important for database curation to be accurate than to have complete coverage, and the figures above demonstrate that this is the tendency with electronic annotation.”

How are binary interactions curated by the IntAct group selected for export to GO as protein binding (GO:0005515) annotations?

All binary interactions evidences in the IntAct database, including those generated by Spoke expansion of co-complex data, are clustered to produce a non-redundant set of protein pairs. Each binary pair is then scored, using a simple addition of the cumulated value of a weighted score for the interaction detection method and the interaction type for each interaction evidence associated with that binary pair. Only experimental data is scored, inferred interactions, for example, would not be scored, and any low confidence data, or data manually tagged by a curator for exclusion from the process, are also not scored. Isoforms and post-processed protein chains are regarded as individual proteins for scoring purposes. Score weightings were determined using the PSI-MI CV.

Once the interactions have been scored, a cut-off filter of 9 has been established, below which the interaction is not exported to UniProtKB and to the Gene Ontology annotation files. Additional rules ensure that any protein pair scoring above 9 must also include at least one evidence of a binary pair, excluding spoke expanded data, before export to UniProtKB/GOA.

These criteria ensure that:

* Only experimental data is used for making the decision to export the protein pair to UniProtKB/GOA as a true binary interacting pair

* The export decision is always based on at least two pieces of experimental data. A single evidence cannot score highly enough to trigger an export

* An export cannot be triggered if the protein pair only ever co-occurs in larger complexes, there must be at least one evidence that the proteins are probably in physical contact.

IMEx will only call an interaction ‘direct’ when performed with 2 purified molecules in vitro so any method using whole cells or cell lysates would not be regarded as direct. The described methodology will give you binary i.e. either direct or involved in the same small complex.

For more details, please see the corresponding entry on the the IntAct FAQ page.

Where can I find software to allow me to make or edit GO annotations?

GO’s Noctua tool is a curation platform that can be used to make GO annotations as well as GO Causal Activity Models (GO-CAMs). See an overview on the Tools page.

GO annotations can also be made and edited using various database-specific tools. Please contact the relevant database to find out how their GO annotation is done. The GMOD online tool, Canto, supports functional gene annotation by community researchers as well as by professional curators.

How do I annotate a novel genome with GO annotations?

Currently, GO recommends groups submit their transcriptomes to NCBI. These submissions will reach UniProt, where InterPro2GO automatically creates GO annotations. These annotations, made with the IEA evidence codes (Inferred from Electronic Annotation), will be made available in a future GO release.

GO does not recommend groups create their own IEAs with internal tools due to reproducibility and accuracy concerns.

What gene or protein IDs should I use?

The list of authoritative database groups for certain species lists the database groups who assume sole responsibility for collecting and submitting annotations for one or more species. If you can convert your IDs into the IDs used by that database group, you will be able to find the data you are looking for far more quickly and efficiently.

We maintain a list of suggested resources for mapping gene and protein IDs.

I have a question about gene or protein nomenclature

The GO Consortium is not involved in naming genes at all, in any organism. The GO vocabularies describe attributes of gene products; they are not collections of gene names or protein names.

Gene names are generally standardized within an organism but not necessarily between organisms (with some notable exceptions, such as the ongoing effort to make human and mouse gene names consistent). We suggest that you direct your query to the database or nomenclature committee for your organism. For example, human gene names are maintained by the HUGO Gene Nomenclature Committee (HGNC), mouse gene names by MGI, etc.

Ontology

What is an ontology?

Ontologies are ‘specifications of a relational vocabulary’. In other words they are sets of defined terms like the sort that you would find in a dictionary, but the terms are given hierarchical relationships to one another. The terms in a given vocabulary are likely to be restricted to those used in a particular field or domain, and in the case of GO, the terms are all biological.

How can I suggest new GO terms?

The GO vocabularies are updated on a regular basis, and suggestions from the community for additional terms or for other improvements are very welcome. Please visit the page on contributing to GO for details on how to submit your contributions.

Does the GO ID have any meaning?

GO IDs are unique identifiers, however, they do not encode any information about a term or its position relative to other terms in the tree. See more about GO terms.

Where can I find the number of terms in each of the ontologies?

You can find the number of terms on each of the ontologies by going to AmiGO:

Under the ‘Advanced Search’ section in the middle of the page, use the drop-down menu to choose “Ontology”. You don’t need to type anything on the ‘Quick search’ box. This action will send you to the ‘Information about Ontology search’ page. There, open the ‘Ontology source’ filter menu on the left. As of January 2019, the number of terms on per ontology were:

29,687 Biological process
11,110 Molecular Function
4,206 Celular component

If you need a reference for this information, refer to our citation policy and license.

How can I calculate the “level” of a GO term?

GO terms do not occupy strict fixed levels in the hierarchy. Because GO is structured as a graph, terms would appear at different ‘levels’ if different paths were followed through the graph. This is especially true if one mixes the different relations used to connect terms.

A more informative metric would be the information content of the node based on annotations. See, for example, the work of Alterovitz et al..

Can a term in one ontology have parents in one of the other two ontologies?

Yes - there are links between molecular function, biological process, and cellular component ontologies. Note that any one term will ONLY have is_a parentage up to one of the root terms.

Can a term that is listed in two places in an ontology file have children in one place but not in the other?

No - the term will always have the same children wherever, and however many times it appears.

How do I get the term names for my list of GO ids?

You can use the YeastMine Analyze tool available at SGD to retrieve the GO term names for each ID.

Go to the Analyze tool on YeastMine
In the Select Type pull down, select GO Term
Enter your GO ids or upload a list in the full format (GO:0016020, GO:0016301…)
Click on Create List. The tool offers several options to download the list.

Can I download the ontologies as an Excel spreadsheet?

It is not possible to download the ontologies as a tabulated spreadsheet. The complex graph structure of GO, where terms can have one or more parent terms, makes it impractical to be rendered as a spreadsheet. It would probably also be too big for software packages such as Excel to cope with.

Where can I find software to allow me to edit the GO terms?

Protege: Protege is a free, open-source ontology editor and framework for building intelligent systems. At this time, all ontology editors for GO use this program.

Where have the ‘unknown’ terms gone?

Good principles of ontological design state that terms should represent biological entities that actually exist, e.g., functional activities that are catalyzed by enzymes, biological processes that are carried out in cells, specific locations or complexes in cells, etc. To adhere to these principles the Gene Ontology Consortium has removed the terms ‘GO:0000004 biological process unknown’, ‘GO:0005554 molecular function unknown’ and ‘GO:0008372 cellular component unknown’ from the ontology. The “unknown” terms violated this principle of sound ontological design because they did not represent actual biological entities but instead represented annotation status. Annotations to “unknown” terms distinguished between genes that were curated when no information was available and genes that were not yet curated (i.e., not annotated).

Annotation status is now indicated by annotating to the root nodes, i.e. ‘GO:0008150 biological_process’, ‘GO:0003674 molecular_function’, or ‘GO:0005575 cellular_component’. These annotations continue to signify that a given gene product is expected to have a molecular function, biological process, or cellular component, but that no information was available as of the date of annotation. Adhering to principles of correct ontology design should allow GO users to take advantage of existing tools and reasoning methods developed by the ontological community.

AmiGO

How do I browse the GO?

The GO Consortium has developed AmiGO for searching and browsing the Gene Ontology and the gene products that member databases have annotated using GO terms. The quick search field autocompletes gene products and GO Terms. Choosing an auto-completed choice from the drop-down will return the summary page for that gene product or term. Alternatively, terms can be entered by free text and the user will be allowed to choose whether the search will return genes, terms or annotations. For more information on using AmiGO, see the AmiGO help documentation.

Learn more about Retrieving GO Data Using AmiGO, API, Files, and Tools from our chapter in the Gene Ontology Handbook.

Where can I find software to allow me to browse the GO terms and annotations?

You can browse GO terms and annotations using various tools. The GO Consortium supports both AmiGO and QuickGO.

AmiGO was developed for searching and browsing the Gene Ontology and the gene products that member databases have annotated using GO terms. Entering a search term into the quick search menu and choosing an auto-completed choice from the drop-down will return the summary page for that gene product or term. Alternatively terms can be entered by free text and the user will be allowed to choose whether the search will return genes, terms or annotations. For more information on using AmiGO, see the AmiGO help documentation.

Learn more about Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools from our chapter in the Gene Ontology Handbook.

What data does AmiGO use? Are there IEAs? If so, which ones?

AmiGO is reloaded approximately once a week. The files currently loaded into the public AmiGO instance can always be seen on the load details page.

AmiGO does currently load full Inferred from Electronic Annotations (IEAs) from UniProt, although this is in development. For a more full discussion of the data loaded into AmiGO, please see the FAQ regarding AmiGO and QuickGO data.

Why does AmiGO display annotations to term X but these annotations aren’t in the GAF file?

Simply put, AmiGO displays annotations made to subclasses by default, while the GAF only contains direct annotations. So an AmiGO search for GO:0004672 protein kinase activity will also list annotations to terms like cAMP-dependent protein kinase regulator activity and even positive regulation of epidermal growth factor-activated receptor activity.

More specifically, AmiGO doesn’t just display subclasses, it uses closure over multiple edge types- part_of, is_a, occurs_in and regulates - to group annotations. This is why you’ll see the Process term positive regulation of epidermal growth factor-activated receptor activity in your results after using AmiGO to look for annotations to the Function term GO:0004672 protein kinase activity.

In order to modify the results in an AmiGO search, use the “GO class (direct)” filter. This will limit the results to only what is annotated directly to your GO term.

How do I find manually annotated gene products only, i.e. how do I sort by evidence code?

Search results can be filtered using the filter menu on the left-hand side of the results page of an AmiGO search. Using the drop-down menu a variety of evidence codes or evidence code combinations can be added or removed to filter the set.

What are the differences between the data available in AmiGO and those on QuickGO?

These are some of the differences between EBI-GOA (QuickGO) and GO Central (AmiGO) when it comes to entities.

GO Central recommends that GAF annotations are made to genes, that is 1:1 equivalents. In GOA (and consequently in QuickGO) annotations are made to proteins, and there may be multiple proteins per gene, sometimes representing different isoforms. You will see this reflected in different numbers for mouse annotations for example.

This is a very important difference, one that users can see when comparing UIs, but more importantly, it is about the underlying datasets and whether a gene-centric or protein-centric worldview is chosen.

Additionally, GO Central omits the majority of the sequences and IEA [electronic] annotations from UniProtKB from the weekly database builds due to the large size of the data set. For those species with a dedicated authoritative database group, such as Drosophila, mouse or Saccharomyces, UniProtKB annotations are collected and submitted by the dedicated group, and hence the UniProtKB IEA annotations for these species do appear in the GO database. As an NHGRI funded resource, GO Central focuses on annotations that elucidate human genes or genes of relevance to human health in some way. GO Central also includes plants, as well as the 200 genomes of the Quest for Orthologs project. More datasets will be supported depending on available resources.

What is the best way to link into AmiGO?

Please refer to the AmiGO 2 wiki manual.

How do I install AmiGO locally?

Full documentation for downloading and installing AmiGO is available on the GO wiki.

GO-CAMs

What is a GO-CAM?

GO-CAM stands for Gene Ontology Causal Activity Model. GO-CAMs link multiple standard GO annotations into an integrated model of a biological system. More information can be found on the GO-CAM site.

How does the information in GO-CAMs compare to existing pathway databases?

GO-CAMs are causally connected GO annotations. Existing pathway databases are not explicitly geared for performing GO annotation, although some such as Reactome include GO terms.

GO-CAMs differ from databases such as BioModels because the information in a GO-CAM is qualitative, whereas the information in BioModels is quantitative.

GO-CAMs have a different model from many pathway databases, which is shown below:

GO-CAM Datamodel

The GO-CAM model is activity-centric, in that the molecular activity (i.e. GO molecular function) is the central unit of annotation.

The model allows for standard GO annotations (with no causal connection), or for causally connected annotations. This allows for the capture of partial information in an incremental fashion.

The GO-CAM model is simpler than that used by databases such as Reactome: in a GO-CAM we do not typically capture all the participants in a reaction, together with their stoichiometry. Instead this information is included in the GO term.

We are currently investigating translations between pathway formats such as BioPAX and OpenBEL to GO-CAM. See the Pathways2GO repository for more information.

Enrichment Analysis

How do I use GO’s Term Enrichment tool?

One of the main uses of the GO is to perform enrichment analysis on gene sets. For example, given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene set.

Users can perform enrichment analyses directly from the home page of the GOC website. Details about the tool, how to use it, and how to interpret the results are available from the GO Enrichment Analysis page.

How can I do term enrichment analysis for a species that is not present in the list from AmiGO?

PANTHER, which supports the backend of the GO enrichment, provides the list of the species found on the right side of GO website. Besides the 130+ genomes listed there, PANTHER supports another 770+ genomes from the Reference Proteome project for users to generate GO annotations.

If your organism is not one of the nearly 1000 genomes supported in PANTHER, there are two options:

The first option is to contact the Reference Proteome project and work with them to incorporate the genome in their project. Once that is done, you can use the regular process to generate the GO annotation file.
The second option is to score your genomes against the PANTHER HMM library. Read our Nature protocol paper, and find the details in Box 2 of the paper.

Does the Term Enrichment tool have a limit on the number genes in the input file?

Our Term Enrichment tool on the homepage of the Gene Ontology Website cannot handle very large gene lists. The root of the problem is that at a certain number, the input (gene list) is too large for the form; however there is not an exact number at which it fails. One way to solve the problem, should you come across this situation, is to reduce the size of the input file by reducing the number of genes.

Alternatively, annotators can use the PANTHER Term Enrichment tool directly, without AmiGO as an intermediary; this would still be the exact same analysis with the GO data. To perform term enrichment analysis directly from the PANTHER website, visit http://pantherdb.org. Once you upload or paste your gene list, select the ‘Statistical overrepresentation test’ option (in Step3) to perform the term enrichment.

Slims

What is “GO slimming” ?

Mapping granular annotations of a set of genes to one or more high-level, broader parent terms is referred to as “GO slimming”. GO slimming is commonly used to report an overview of a genome or to a set of summarize experimental results. GO hosts a number of predefined slim sets, a generic GO slim, and a number of slims tailored to give good coverage for some well studied/annotated model species. GO slimming will only be successful if the organism of interest has a body of GO annotation in the GO database (either electronic or manual). If your organism of interest has no publicly available GO data refer to the FAQ on annotating a gene set.

How do I create a user defined “GO slim”?

GO hosts a number of predefined Slim sets, a generic GO slim, and a number of slims tailored to give good annotation coverage for some well studied/annotated model species. The available GO slimming tools also provide an option to upload your own term set. For most applications you usually need to adjust the terms in the slim to represent your results (i.e to reduce the number of terms, or to replace terms in regions of special interest with more specific children). The generic GO slims or organism specific slims are a good starting point to create your own GO slim. When creating a slim you should still ensure that it covers as many annotated genes in your set as possible. To enable interpretation of your results you should also report how many genes are annotated but not in your slim, and how many genes do not slim (i.e map only to the root node and are therefore ‘unknown’).

How do I map a set of annotations to high level GO terms (GO slim)?

One method is to use GO Term Mapper. Choose the aspect (Molecular Function, Biological Process, or Cellular Component) and indicate if you want to map to a generic slim or one curated for your organism (for example, the S. cerevisiae slim omits terms applicable only to plants or bacteria).
In order to map your annotations to a GO slim, use the Map2Slim option in OWLTools. Given a GO slim file, and a current ontology (in one or more files), the Map2Slim script will map a gene association file (containing annotations to the full GO) to the terms in the GO slim. This script is an option of OWLTools, and it can be used to either create a new gene association file, which contains the most pertinent GO slim accessions, or in count-mode, in which case it will give distinct gene product counts for each slim term.

Background information and details on how to download, install, and implement OWLTools, as well as instructions on how to run the Map2Slim script are available from the OWLTools Wiki.

Read more or download the GO slims here.

When reporting GO slim results, why shouldn’t I display my “GO slim” results as a pie chart ?

The numbers provided by a GO slim are an annotation count not a gene product count, and so gene products may be present in more than one bin. Therefore displaying GO slim totals as percentages is not meaningful. For your results to be interpreted fully, you should also report how many genes are annotated but not in your slim, and how many genes do not slim.

Mappings

What are mappings?

The files contain concepts from systems external to GO e.g. Enzyme Commission numbers, SWISS-PROT keywords and TIGR roles, indexed to equivalent GO terms. The mappings are typically made manually; details can be found in the file header. See Mappings to GO for available files.

How do I find the annotations (mappings) for Entrez, NCBI or other IDs?

To search the GO database, a list of Entrez IDs, NCBI IDs, etc. needs to be converted to UniProtKB or model organism database IDs.

UniProt and the Protein Information Resource (PIR) have similar ID mapping tools to help with the conversion:

GO annotations from QuickGO can be filtered for many parameters and provide mappings to several IDs, e.g NCBI or Ensembl gene IDs: http://www.ebi.ac.uk/QuickGO/GAnnotation

Why are Interpro2go mappings not updated with GOA releases?

GOA is updated in accordance with the latest data released by its core databases (SWISS-PROT, TrEMBL, InterPro, Ensembl) as well as mappings of SWISS-PROT Keywords, InterPro and Enzyme Commission (EC) terms to GO. Each of GOA’s core databases produces its own releases; for example, InterPro has dependencies on the member databases of InterPro. InterPro2GO is updated at regular intervals but not always in keeping with monthly schedule of GOA releases.

Keep in mind that the Gene Ontology Annotation (GOA) resource (http://www.ebi.ac.uk/GOA) provides evidence-based Gene Ontology (GO) annotations to proteins in the UniProt Knowledgebase (UniProtKB), and is not the same as GOC (the entire GO Consortium, including groups like GOA).

File Formats

What are the file formats used by the Gene Ontology?

Refer to the ontology downloads page for information on ontology files. For general introduction to the project’s annotation file formats, see the guides on GAF 2.1 and GPAD file format.

What is a GAF file?

A GAF file is a GO annotation file containing annotations made to the GO by a contributing resource such as FlyBase or Pombase. See more information on the GAF file format guide.

What is a GPAD file?

The GPAD - Gene Product Association File Format - is an alternative means of exchanging annotations from the Gene Association File (GAF). The GPAD format is designed to be more normalized than GAF, and is intended to work in conjunction with a separate format for exchanging gene product information. For details, see the GPAD specification page.

What is a GPI file?

A GPI - Gene Product Information file is used to submit gene and gene product information to the GO Consortium. The GPI specification is here. Please note that annotation information relationships between GO terms and annotations made to them uses GPAD; see details on the GPAD specification page.

What exchange format is used for GO-CAMs?

GO-CAMs include more information than standard GO annotations, so cannot be effectively exchanged using the simple tabular formats used by the GO.

The native representation for GO-CAMs is the Web Ontology Language (OWL). Standard RDF exchange formats such as RDF/XML and Turtle can be used for GO-CAMs.

Downloads can be found on the GO-CAM site.

Why won’t the RDF-XML file parse using RDF parsers?

The GO RDF-XML format was originally developed some time ago, before the advent of OWL. It has a few unusual features that render it more of a pseudo-rdf format. The actual RDF is embedded within a xml element - this should be stripped out before handing to RDF parsers. Note that the GO RDF-XML conforms to a DTD, something that is not normally a requirement of RDF. This is because most people parse the file using conventional XML parsers rather than XML tools. We are working on a more up to date RDF representation of GO.

What is an OWL file?

OWL is the acronym for Web Ontology Language, a standard produced by the W3C. GO in OWL is based on a translation from OBO to OWL and is available for download. OWL files can be opened in an editing tool such as Protege.

What is OBO file format?

The OBO file format is one of the formats that the Gene Ontology is made available in. The most recent version is OBO 1.4. The OBO format is designed to be more human readable than XML based formats. GO can be accessed in this format on the Downloads page.

Why are the ontologies initially produced in OBO flat file format instead of XML?

The ontologies are initially produced in the specially designed OBO flat file format. They are converted to XML once a month for the convenience of users who require this facility. Both formats and many others are available in the GO downloads section. We use the OBO flat file format because it is human-readable, and also because the file is much smaller without the XML tags. This means that it is much quicker and easier for the curators to handle the file on a day-to-basis.

How can I generate files in the old GO flat file format?

As of August 1, 2009, the original GO flat file format was deprecated and is no longer be provided by the GO Consortium.

The OBO-Edit project, which used to generate the flat file format, has been mothballed.

Database Access

How do I find terms, annotations, or gene products in the database?

We maintain a set of examples that cover, or can be used as a base to cover, most common queries. This is also the set used with GOOSE.

What are the recommended data access policies for your services?

AmiGO and the GO relational database servers are a shared resource and thus we require data mining to be performed in a manner that allows others to utilize this resource at the same time. Any activity that mines the GO database or uses AmiGO must be controlled so that only one request is made at a time. If this is not sufficient, you may download and install the database locally. You can also retrieve all the source files that define the data within the database. More details on the database, including downloads and installation, can be found in the GO database guide.

For more information please contact the GO helpdesk.

I want to use the database files but…

The *.txt files must be imported into a MySQL instance using mysqlimport. They are not intended to be loaded into excel or parsed using custom tools. Why are so many of the .txt files empty?

For some exports of the GO database, some tables will necessarily be empty. For example, the termdb dump by design omits any data on genes or gene associations, so the corresponding tables are mepty. MySQL requires a file be present for every table, hence there will be some tables with 0 rows in the .txt files.

What is the best way to obtain the GO annotations for a list of UniProt accession numbers in batch?

With UniProt accession numbers, you can obtain all GO annotations by parsing a GOA gene association file, which are provided in a simple tab-delimited format. These files are available from the GO directory.

The GOA project offers users a number of different files; for example:

all UniProtKB proteins with GO annotation
human proteins
if you were only interested in proteins from a particular species, we also provide non-redundant, species-specific files for human, mouse, rat, zebrafish, chicken, cow and Arabidopsis proteins (these files are created using the International Protein Index (IPI) - which provides a top level guide to the main databases that describe the proteomes of higher eukaryotic organisms)

Further information on the content and format of our gene association files can be found in the ReadMe.

Please contact GOA help for further assistance.

What is the status of the GO MySQL database?

While the GO MySQL database is currently considered to be in “legacy” mode, meaning that there will likely not be any new developments on it, it is a widely used and convenient resource for many types of queries. More information about it can be found in the GO MySQL Database Guide.