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

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TLDR
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.
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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Common patterns and disease-related signatures in tuberculosis and sarcoidosis

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DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes

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The air-liquid interface and use of primary cell cultures are important to recapitulate the transcriptional profile of in vivo airway epithelia

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References
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Journal ArticleDOI

Gene Ontology: tool for the unification of biology

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.
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Gene expression profiling predicts clinical outcome of breast cancer

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.
Journal ArticleDOI

DAVID: Database for Annotation, Visualization, and Integrated Discovery

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.
Journal ArticleDOI

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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