Cited by 3,356Bioinformatics and Genomic NetworksGenomics and Phylogenetic StudiesBiomedical Text Mining and Ontologies

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GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

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Eden, Eran, Navon, Roy, Steinfeld, Israel, Lipson, Doron, Yakhini, Zohar·2009·BMC Bioinformatics

Abstract

BACKGROUND: Since 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. RESULTS: GOrilla 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. CONCLUSION: GOrilla 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

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Figure 1 How to use the GOrilla web user interface. To use the GOrilla web interface, the user is required to perform the following four simple steps: (i) choose an organism; (ii) choose a running mode (either flexible threshold or fixed threshold mode) (iii) copy and paste a list (or upload a file) of genes in the case of a flexible threshold or two lists of genes – a target and a background – in the case of a fixed cutoff; (iv) choose an ontology.
Figure 1 How to use the GOrilla web user interface. To use the GOrilla web interface, the user is required to perform the following four simple steps: (i) choose an organism; (ii) choose a running mode (either flexible threshold or fixed threshold mode) (iii) copy and paste a list (or upload a file) of genes in the case of a flexible threshold or two lists of genes – a target and a background – in the case of a fixed cutoff; (iv) choose an ontology.CC BY · PubMed Central
Figure 2 An example of the GOrilla analysis output. 14,565 genes from the van't Veer dataset were ranked according to their differential expression and given as input to GOrilla. The resulting enriched GO terms are visualized using a DAG graphical representation with color coding reflecting their degree of enrichment. Nodes in the graph are clickable and give additional information on the GO terms and genes attributing to the enrichment. N is the total number of genes; B is the total number of genes associated with a specific GO term; n is the flexible cutoff, i.e. the automatically determined number of genes in the 'target set' and b is the number of genes in the 'target set' that are associated with a specific GO term. Enrichment is defined as (b/n)/(B/N).
Figure 2 An example of the GOrilla analysis output. 14,565 genes from the van't Veer dataset were ranked according to their differential expression and given as input to GOrilla. The resulting enriched GO terms are visualized using a DAG graphical representation with color coding reflecting their degree of enrichment. Nodes in the graph are clickable and give additional information on the GO terms and genes attributing to the enrichment. N is the total number of genes; B is the total number of genes associated with a specific GO term; n is the flexible cutoff, i.e. the automatically determined number of genes in the 'target set' and b is the number of genes in the 'target set' that are associated with a specific GO term. Enrichment is defined as (b/n)/(B/N).CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Eden E: Discovering Motifs in Ranked Lists of DNA Sequences. Master's thesis [ http://bioinfo.cs.technion.ac.il/people/zohar/thesis/eran.pdf ]

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Metadata

Authors
Eden, E., Navon, R., Steinfeld, I., Lipson, D., & Yakhini, Z.
Affiliations
  • Weizmann Institute of Science (IL)
  • Tel Aviv University (IL)
  • Technion – Israel Institute of Technology (IL)
Corresponding author
  • Eden, Eran
  • Navon, Roy
Venue
BMC Bioinformatics
Published
2009-02-03
Volume
10
Issue
1
Pages
48-48
ISSN
1471-2105 (ISSN-L)
OpenAlex
W2038963685
Cited by
3,356
References
20
Citations by year
2026: 64 · 2025: 135 · 2024: 169 · 2023: 213 · 2022: 274 · 2021: 409 · 2020: 305 · 2019: 319 · 2018: 300 · 2017: 251 · 2016: 242 · 2015: 217 · 2014: 168 · 2013: 138 · 2012: 67
FWCI
20.15
Type
journal article
Language
en
Open access
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
European Commission
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
Keywords
gorillas, gene ontology enrichment, exact p-values, web-based bioinformatics tool, differential gene expression
MeSH terms
Algorithms, Animals, Data Interpretation, Statistical, Genes, Humans, Models, Genetic, Terminology as Topic, Software, User-Computer Interface, Yeasts, Models, Statistical, Information Storage and Retrieval, Arabidopsis, Computational Biology, Internet, Gene Expression Profiling, Genomics, Databases, Genetic

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