The availability of functional genomics data has increased dramatically over the last decade, mostly due to the development of high-throughput microarray-based technologies such as expression profiling. Automatic mining of these data for meaningful biological signals requires systematic annotation of genomic elements at different levels. The Gene Ontology (GO) project [1
] is a collaborative effort aimed at providing a controlled vocabulary to describe gene product attributes in all organisms. GO consists of three hierarchically structured vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions. The building blocks of GO are terms, the relationship between which can be described by a directed acyclic graph (DAG), a hierarchy in which each gene product may be annotated to one or more terms in each ontology.
Since its inception, many tools have been developed to explore, filter and search the GO database. A comprehensive list of available tools is provided at the Gene Ontology web site http://www.geneontology.org
. One of the most common applications of the GO vocabulary is enrichment analysis
– the identification of GO terms that are significantly overrepresented in a given set of genes [2
]. Enrichment may suggest possible functional characteristics of the given set. For example, enriched GO terms in a set of genes that are significantly over-expressed in a specific condition may suggest possible mechanisms of regulation that are put into play, or functional pathways that are activated in that condition.
A large repertoire of tools for enrichment analysis has been developed in recent years, including GoMiner [3
], FatiGO [4
], BiNGO [5
], GOAT [6
], DAVID [7
] and others. In general, these tools accept as input a target set of genes that is compared to a given background set of genes, or to a default "complete" background set. Some subset of GO terms from one or more of the three ontologies is scanned for enrichment in the target set relative to the background set, and terms for which significant enrichment is discovered are reported. The statistical test used for enrichment analysis is typically based on a hypergeometric or binomial model.
The most common form of output is a list of enriched terms. This simple approach allows the user to identify terms that are most significantly enriched but may lose substantial information regarding the relations between these terms. A more informative approach is to present the enrichment results in the context of the DAG structure of the respective ontology. In a typical case, the list of significantly enriched GO terms may include several related terms at varying significance levels. Identifying the clusters of enriched terms in the GO hierarchy becomes much simpler if the DAG structure is made available. A few tools visualize the results of enrichment analysis in the DAG structure, including the downloadable version of GoMiner [3
], the CytoScape plug-in BiNGO [5
], GOLEM [8
], GOEAST [9
] and GOTM [10
]. A particularly friendly and useful GO enrichment analysis tool is GO::TermFinder which is provided at the Saccharomyces Genome Database (SGD, [11
]). This tool provides a color-coded map of the enriched GO terms. It is, however, limited only to analysis of S. cerevisiae
genes and requires specifying an explicit target set.
In many practical cases, functional genomic information used as the input for the GO enrichment analysis may be naturally represented as a ranked list. For most applications, the requirement for an input target gene set forces the user to set some arbitrary threshold and define the target set as all genes with ranks above (or below) the threshold. For example, genes may be naturally scored and ranked according to their differential expression between two conditions. However, defining the specific set of genes that are differentially expressed requires setting an arbitrary threshold. Unfortunately, the results of the enrichment analysis may often depend on the specific threshold that is set. Tools that use the simple hypergeometric distribution require setting such a fixed threshold.
A few tools have been developed that use a threshold free approach including GSEA [12
], FatiScan [13
], GO-stat [14
], GeneTrail [15
] and iGA [16
]. The widely used GSEA tool uses a statistic that is similar to Kolmogorov-Smirnov but assigns different weights to the occurrences of genes in different ranks in the list. The tool is not specifically aimed at GO enrichment, and therefore does not offer visualization in terms of the GO DAG structure. In addition GSEA does not provide an exact p-value and estimates the p-value using permutations. The p-values assigned by GSEA are therefore limited by the number of permutations performed. FatiScan is another threshold free tool by the creators of FatiGO. It tests a number of thresholds determined by the user (the default is 30 thresholds) and then corrects for multiple testing using FDR. Again, this tool does not provide an exact p-value. The iGA method uses an iterative approach that circumvents the need for a fixed cutoff by computing the hypergeometric score at all possible cutoffs. iGA does not produce an exact p-value as well. In [17
] the authors study the advantages of sample re-sampling. The sample permutation approach is applicable for the analysis of differential gene expression data but not in other applications of gene set enrichment where ranking is inferred otherwise.
As a matter of practicality it is also important for GO enrichment tools to perform in an interactive manner. Many of the existing tools, in particular those based on flexible thresholds that require time consuming simulations, fall short on this desired property.
In this note we describe a web-server interactive software tool, called GOrilla
, that enables GO enrichment analysis in ranked lists of genes. It is based on previous work [18
], in which we describe a statistical framework, called mHG, for enrichment analysis in ranked lists. The method identifies, independently for each GO term, the threshold at which the most significant enrichment is obtained. The significance score is accurately and tightly corrected for threshold multiple testing without the need for time consuming simulations. Consequentially GOrilla
performs the enrichment analysis on thousands of genes and thousands of GO terms in a few seconds. In the Results section we demonstrate how GOrilla
can capture relevant biological processes and visualize the results with an easy to use graphical representation of the GO hierarchy, emphasizing on the enriched nodes.