GO: : TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

By E. Boyle, S. Weng, J. Gollub, Heng Jin, David Botstein, J. Cherry, Gavin Sherlock. 2004

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In Molecular Biology

Citations in the corpus

GOATOOLS: A Python library for Gene Ontology analyses (2018, methodology): “The first column, ‘GO Name’ is the name of the GO term found in the GO DAG.”
Uncovering the overlapping community structure of complex networks in nature and society (2005, methodology): “2c and 3 can be associated with either protein complexes or certain functions, as can be looked up by using the GO-TermFinder package [28] and the online tools of the Saccharomyces Genome Database (SGD) [29].”
GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists (2007, methodology): “The p values can then be adjusted for multiple tests using a simulation-based correction approach [10,11] or the false discovery method proposed by Benjamini and Hochberg [12].”
Detecting cohesive and 2-mode communities indirected and undirected networks (2014, methodology): “To further analyze the role of these communities, we used gen e ontologies to identify relevant terms/functions of protei ns nA, B, C, andD using the GO Term Finder [7].”
Overlapping Communities Explain Core–Periphery Organization of Networks (2014, methodology): “A. Clique Percolation First, we analyze the CPM and show that it may not properly detect two overlapping communities from Fig.”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, methodology): “In this case the server automatically runs the GO-TermFinder program [37] for the retrieval of all the GO-term ancestors.”
Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe (2010, methodology): “GO enrichment analysis used the Princeton implementation of GO term finde”
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters (2012, neutral): “…for identifying predominant biological themes in a collection of genes were developed to enhance data interpretation, including GO::TermFinder (Boyle et al., 2004) and GOstats (Falcon and Gentleman, 2007) for GO enrichment analysis, and SubpathwayMiner (Li et al., 2009) for detecting enriched…”
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update (2016, methodology): “Early tools such as FatiGO (2), BiNGO (3) and TermFinder (4) first realized this concept.”
A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence (2008, background): “To determine whether the human genes containing a conserved let-7 target site are related in terms of biological function, we tested the 25% of genes with the most highly conserved let-7 target sites for enrichment in Gene Ontology categories (22).”
clusterProfiler 4.0: A universal enrichment tool for interpreting omics data (2021, methodology): “The outputs of ORA and GSEA are enrichResult and gseaResult objects, respectively, while the output of compareCluster is a compareClus-terResult object.”
Diverse RNA-Binding Proteins Interact with Functionally Related Sets of RNAs, Suggesting an Extensive Regulatory System (2008, methodology): “Enrichment of GO terms was performed with GO::TermFinder [158].”
ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs (2014, methodology): “To analyze whether there are any Gene Ontology categories whose genes are over-represented among the targets of a motif, we use the “GO::TermFinder” Perl module[26].”