AspGD Help: GO Term Finder


Contents



Background and Description

The Gene Ontology (GO) project was established to provide a common language to describe aspects of a gene product's biology. A gene product's biology is represented by three independent structured, controlled vocabularies: molecular function, biological process and cellular component. For more information on GO, see SGD's GO Tutorial or GO Help pages, or see the GO Consortium home page.

To provide the most detailed information available, gene products are annotated to the most granular GO term(s) possible. For example, if a gene product is localized to the perinuclear space, it will be annotated to that specific term only and not the parent term nucleus. In this example the term perinuclear space is a child of nucleus. However, for many purposes, such as analyzing the results of microarray expression data, it is very useful to "calculate" on GO, moving up the GO tree from the specific terms used to annotate the genes in a list to find GO parent terms that the genes may have in common. The GO Term Finder tool allows you to do this.

The GO Term Finder is described in detail in Boyle et al (2004).

Query Page

The query page has several options as described below.

Results

The results page displays, in both graphic and table form, the significant shared GO terms (or parents of GO terms) used to describe the set of genes entered on the previous page. In addition, the results page displays all the criteria used to customize the Background set and Annotations in the background set.

Graphic Display

The graphic illustrates the relationships among the GO terms used to directly or indirectly describe the genes in your list. The color of each box indicates the p value score (see description of the method below). Genes associated with the GO terms are shown in gray boxes. Each GO term links to the AspGD GO term page, where you can view the GO structure around that term as well as other genes associated with it. Each gene name links to its AspGD Locus Summary page.

In some cases, the number of GO terms is too large to display on a web page. When this occurs, the most significant terms are shown. Regardless of the significant number of terms returned, an option to download the complete set of results is always available.

To generate the graphics, the program utilizes CPAN's GraphViz perl wrapper module that uses AT&T's graphviz tool.

Results Table

The table below the graph lists each significant GO term, the number of times the GO term is used to annotate genes in the list (or cluster) and the number of times that the term is used to annotate genes in the background set. The default for the background set is all the genes/features that have at least one GO annotation in the database. The choice of background set is configurable. Because the frequency of any given annotation within the background set is compared against the frequency of the annotation within the query set (input), the choice of background set affects the significance of the results that are returned by the tool. Please note that the specific background set of genes that was used in the absence of any user-defined set (the default background set) has changed over time. Prior to December 2007, the default background set included all genes that have GO annotations in one or more of the Biological Process, Molecular Function, or Cellular Component ontologies. Between December 2007 and March 2008, the default background set included only the genes that have a GO annotation in the specific ontology that the user chose to query. As of March 2008, the background set includes all of the genes in the genome, regardless of whether or not they have GO annotations. In other words, the stringency that is contributed by the background set has varied from moderately stringent (before December 2007) to very stringent (December 2007 to March 2008), and is now least stringent.

Additional columns list the p-value, the False Discovery Rate, and a list of all the genes annotated, either directly or indirectly, to the term. False Discovery Rate is an estimate of the percent chance that a particular GO term that is shown as significant might actually be a false positive. It represents the fraction of the nodes with p-values as good or better than the node with this FDR that would be expected to be false positives.

About p-value

To determine the statistical significance of the association of a particular GO term with a group of genes in the list, GO Term Finder calculates the p-value: the probability or chance of seeing at least x number of genes out of the total n genes in the list annotated to a particular GO term, given the proportion of genes in the whole genome that are annotated to that GO Term. That is, the GO terms shared by the genes in the user's list are compared to the background distribution of annotation. The closer the p-value is to zero, the more significant the particular GO term associated with the group of genes is (i.e. the less likely the observed annotation of the particular GO term to a group of genes occurs by chance).

Results with a p-value less than 0.05 are color-coded on the graphical display, and all results with a p-value of less than or equal to 0.1 are included in the results table. Please note that the same cutoff has not always been used in generation of the results displays; between December 2007 and March 2008 the cutoff for both displays was increased in stringency from a p-value of less than or equal to 0.1 to a p-value of less than or equal to 0.01, and subsequently relaxed. The change in cutoff means that more results may be returned from any given search, and (as always) the important task of judging which results are truly significant under the circumstances is left to the investigator.

A customizable web implementation of the GO Term Finder tool that allows the user to set a p-value cut-off (which uses the same algorithm as the AspGD's tool) is available at Princeton University.

Publishing GO Term Finder Results

Here are some important points to note when including results from this tool in a publication.

Method/Algorithm Description

Genes are directly associated with GO terms that are as granular as possible. Because the GO terms have hierarchical relationships with each other, genes are also considered to be indirectly associated with all the parents of the granular terms to which they are directly associated.

The tool looks for significant shared GO terms that are directly or indirectly associated with the genes in the list. To determine significance, the algorithm examines the group of genes to find GO terms to which a high proportion of the genes are associated as compared to the number of times that term is associated with other genes in the genome. For example, when searching the process ontology, if all of the genes in a group were associated with "DNA repair", this term would be significant. However, since all genes in the genome (with GO annotations) are indirectly associated with the top level term "biological_process", it would not be significant if all the genes in a group were associated with this very high level term.

Notes: This version of GO Term Finder uses a hypergeometric distribution with Multiple Hypothesis Correction (i.e., Bonferroni Correction) to calculate p-values. A stand-alone, generic version of GO Term Finder that uses a hypergeometric distribution, with Bonferroni Correction and False Discovery Rate, can be downloaded here.

Algorithm Details:

If G is the number of genes annotated to a term (either directly or indirectly) and N is the total number of genes in the genome with GO annotations (please see Results Table section above for details on this number), then p, the probability of a randomly selected gene being annotated to a particular GO term can be calculated as:
G
-
N
Given a list of n genes, in which x of them have been annotated to a given GO term (directly or indirectly), the probability of having x out of n annotations assigned to the same GO term by chance is defined as the product of the number of permutations by which the annotations can occur and the following equation:
px x (1-p)(n-x)

Within a list of n genes, there are multiple permutations by which x of them may have this annotation. The number of permutations can be calculated as:
   n! 
--------
x!(n-x)!
However, annotations to a particular term are low probability events (p is small). Because of this, any list of genes having a particular set of annotations is likely to have a low probability, but not necessarily a significant one. Thus, instead of calculating the probability of having x of n genes annotated to a term, a more conservative approach, often used by statisticians, is taken to calculate the probability of x or more of n genes being annotated to a particular term. Since GO annotations are still incomplete (i.e. there may be more than x genes annotated to a particular term), this is appropriate. This is calculated as:

Equation

Associated Glossary Terms

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