GOstat: find statistically overrepresented Gene Ontologies within a group of genes.

By T. Beissbarth, Terence P. Speed. Bioinformatics, 2004

1,315 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9c07c51fffda2a237fd9f7c4c0b2bb3f63f9b255

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In Molecular Biology

Citations in the corpus

GeneTrail—advanced gene set enrichment analysis (2007, methodology): “Some of the developed tools focus on the analysis of only one type of functional categories for example various Gene Ontology (GO) (1) based tools, among them FatiGO (2), BiNGO (3), and GOstat ( 4 ).”
Bias in plant gene content following different sorts of duplication: tandem, whole-genome, segmental, or by transposition. (2009, methodology): “In 2006 (32) and again in 2008 (30), I attempted to use The Arabidopsis Information Resource (TAIR) GO categories and the online statistical application GOStat (6) to test for significant underand overrepresentation of data in Arabidopsis; the overlapping structure of GO terms makes statistical eval”
Pathways to the analysis of microarray data. (2005, methodology): “GOStat Fisher Exact test, multiple testing correction GO Web http://gostat.wehi.edu.au/ [ 46 ]”
BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks (2005, neutral): “Several tools exist that analyze GO term enrichment in a given gene set (Berriz et al., 2003; Hosack et al., 2003; Zeeberg et al., 2003; Al-Shahrour et al., 2004; Beißbarth and Speed, 2004; Boyle et al., 2004; Masseroli et al., * To whom correspondence should be addressed.”
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges (2012, methodology): “ORA uses one or more variations of the following strategy [4–11]”
Ontological analysis of gene expression data: current tools, limitations, and open problems (2005, methodology): “Several tools such as GOToolBox, GOstat, GoMiner, FatiGO, and GOTM1 use the total set of genes in a genome as the reference (Beissbarth and Speed, 2004; Martin et al., 2004; Zeeberg et al., 2003; Zhang et al., 2004) or the set of genes with GO annotations (Al-Shahrour et al.”
Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. (2008, methodology): “01, X-axis) as identified by GoStat (Beissbarth and Speed 2004).”
Discovering Sequence Motifs with Arbitrary Insertions and Deletions (2008, methodology): “We examined the fifty top-scoring genes using GOStat”
ARED 3.0: the large and diverse AU-rich transcriptome (2005, methodology): “The selection criteria used do not take into account either AREs loosely related to the 13 bp ARE search pattern, such as c-myc, nor AREs lacking the AUUUA motif, such as c-jun, which lack specific recognizable pattern derived from computational biology studies (6,11,15,16).”