Identifying biological themes within lists of genes with EASE

By Douglas A. Hosack, Glynn Dennis, Brad T. Sherman, H. Clifford Lane, Richard A. Lempicki. Genome Biology, 2003

2,062 citations recorded by the source corpus.

EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE, and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from "genes to themes."

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/59f9ca707a911daf19755df817a0d2553524e526

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Citations in the corpus

Myc Stimulates Nuclearly Encoded Mitochondrial Genes and Mitochondrial Biogenesis (2005, background): “21E 45) (see Table S4 in the supplemental material) (21).”
Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks (2006, methodology): “*For each module in each dataset analyzed, an EASE analysis [ 25 ] was performed by using Fisher's exact test and the resulting p-values were placed into the table based on the prevalence of the GO (Gene Ontology) term being searched for.”
Analyzing gene expression data in terms of gene sets: methodological issues (2007, neutral): “Contact: j.j.goeman@lumc.nl”
DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways (2012, methodology): “0 can also calculate a more conservative adjustment of Fisher’s exact test (24).”
The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists (2007, methodology): “While the cytokine and chemokine categories were systematically highlighted by EASE (a GO enrichment analysis based on the Fisher Exact Test) [2], other annotation categories reported in the publication were discovered through semi-manual analysis by bioinformatics experts with an advanced level of ”
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters (2012, neutral): “TM4 MultiExperiment Viewer (MeV) (Saeed et al., 2003), which implements the Tree-EASE (TEASE) algorithm by combining hierarchical clustering with EASE (Hosack et al., 2003), can perform clustering analysis followed by GO enrichment calculation, but without tools designed for comparing and visualizin”
A gene expression signature associated with "K-Ras addiction" reveals regulators of EMT and tumor cell survival. (2009, methodology): “We also performed a gene functional annotation analysis using the EASE algorithm (Dennis et al., 2003; Hosack et al., 2003) for term enrichment using the K-Ras dependency signature genes (Figure S6).”
DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis (2007, methodology): “The work was originally conducted years ago to successfully serve as a comprehensive backend knowl-edgebase for various high throughput gene-annotation enrichment analytic tools in the DAVID and EASE packages [13,14].”
Evaluation of gene expression measurements from commercial microarray platforms. (2003, methodology): “multiple correction adjusted EASE P-value score was determined using a bootstrap method that calculates the probability of biological themes occurring by chance alone by permuting 100 randomly generated lists of genes of equal size to the original list of genes (13).”
BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks (2005, background): “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.”
LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data (2009, result): “Overall results were concordant between the simulation study and the experimental data analysis, and provide useful information to investigators seeking to choose the appropriate method.”
Genomic Analysis of Mouse Retinal Development (2004, methodology): “-values for GO categories that are overrepresented in individual clusters were calculated using EASE (”
PAGE: Parametric Analysis of Gene Set Enrichment (2005, neutral): “Since inception of the microarray technology, significant technological and analytical improvements have been introduced to meet these challenges, from experimental design [1], probe-level analysis of oligonucleotide chips [3,4], data normalization [5], statistical analysis [6], clustering technique”
KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases (2011, methodology): “A growing number of tools have been developed for pathway and disease identiﬁcation, including, but not limited to, MAPPFinder (13), EASE (14), DAVID (15,16), ArrayXPath (17), WebGestalt (18), FuncCluster (19), PageMan (20), GENECODIS (21,22), GeneTrail (23), g:Proﬁler (24), FunNet (25) and PaLS (26”
Aging effects on DNA methylation modules in human brain and blood tissue (2012, methodology): “Additional file 6 shows the results of a GO enrichment analysis using the DAVID software when ‘GO Cluster’ output is selected.”
Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining (2012, methodology): “The assessment is done using a statistical test, mostly a modified Fisher’s exact test [21] or rank-based enrichment testing [22].”