GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists
By Eran Eden, R. Navon, Israel Steinfeld, Doron Lipson, Zohar Yakhini. BMC Bioinformatics, 2009
635 citations recorded by the source corpus.
BackgroundSince the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results.ResultsGOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (e.g. by level of expression or of differential expression). GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list. Building on a complete theoretical characterization of the underlying distribution, called mHG, GOrilla computes an exact p-value for the observed enrichment, taking threshold multiple testing into account without the need for simulations. This enables rigorous statistical analysis of thousand of genes and thousands of GO terms in order of seconds. The output of the enrichment analysis is visualized as a hierarchical structure, providing a clear view of the relations between enriched GO terms.ConclusionGOrilla is an efficient GO analysis tool with unique features that make a useful addition to the existing repertoire of GO enrichment tools. GOrilla's unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation. GOrilla is publicly available at: http://cbl-gorilla.cs.technion.ac.il
Subfields: Information Systems, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/0b6a82a9112227222493068f009ebab68c0e0a03
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Citations in the corpus
- MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (2015, positive): “Testing for enriched GO terms based on a list of genes was performed with the GOrilla online tool by comparing an unranked target list against a background list [35].”
- Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR (2015, positive): “Since knocking out ribosomal genes lead to a strong negative selection phenotype [1, 2], the significance of negative selection on ribosomal genes can be evaluated in MAGeCK-VISPR by Gene Ontology (GO) enrichment analysis using GOrilla [15].”
- High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. (2015, positive): “We then analyzed each set of genes for enrichment in Gene Ontology biological process or cellular component annotations, using the GOrilla web service (Eden et al., 2009).”
- RNA-methylation-dependent RNA processing controls the speed of the circadian clock. (2013, positive): “GO enrichment analysis was carried out with single gene IDs using GOrilla (Eden et al., 2007; Eden et al., 2009), either using a single ranked list of genes or using two unranked lists (target and background).”
- g:Profiler—a web server for functional interpretation of gene lists (2016 update) (2016, neutral): “Tools such as Panther (14), FuncAssociate (15) and GOrilla (16) aim to support the analysis of many species, while others such as WebGestalt (17) focus on the convenient mapping of diverse gene identifiers.”