GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists
Eran Eden,Roy Navon,Roy Navon,Israel Steinfeld,Israel Steinfeld,Doron Lipson,Zohar Yakhini,Zohar Yakhini +7 more
TL;DR: 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, and its unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation.
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Abstract: 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 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 (eg 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 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-gorillacstechnionacil
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Figure 3 certainly support this hypothesis. These results suggest that 14 bits of information in a 33% GC-rich genome ought to be enough to provide a search efficiency roughly similar to that of CRP in E. coli. Nevertheless, H. influenzae CRP displays 17.83 bits, indicating that additional information is being used to provide it with an adequate operating range. 
Figure 2 and Figure 3 show the ROC curves for information theory-based methods attempting to locate, respectively, CRP and Fur binding sites against equiprobable, 66% GC- and 66% AT-skewed randomly generated backgrounds, with their RE profile plots shown as insets in the bottom-right corner. The curves show the mean and standard deviation of three independent experiments and thus reveal that the differences between the observed methods are statistically significant. As it can be seen, all methods substantially improve their results on equiprobable random backgrounds when compared to those obtained on the E. coli genome. Even though the E. coli genome is nearly equiprobable, this is to be expected, since naive random sequences are not very good approximations of genome sequences, in which certain word fre- 
Table 1: Summary of relative method performances.
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References
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Gene expression profiling predicts clinical outcome of breast cancer
Laura J. van't Veer,Hongyue Dai,Marc J. van de Vijver,Yudong D. He,Augustinus A. M. Hart,Mao Mao,Hans Peterse,Karin van der Kooy,Matthew J. Marton,Anke T. Witteveen,George J. Schreiber,Ron M. Kerkhoven,Christopher J. Roberts,Peter S. Linsley,René Bernards,Stephen H. Friend +15 more
TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Glynn Dennis,Brad T. Sherman,Douglas A. Hosack,Jun Jun Yang,Wei Gao,H. Clifford Lane,Richard A. Lempicki +6 more
TL;DR: DAMID is a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries that assists in the interpretation of genome-scale datasets by facilitating the transition from data collection to biological meaning.
BiNGO : a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks
TL;DR: The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine whichGene Ontology terms are significantly overrepresented in a set of genes.
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