Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 2, 257-258

Fred Hutchison Cancer Research Center, Program Computational Biology,1100 Fairview Avenue North P. O. Box 19024, Seattle, WA 98109, USA.
Bioinformatics (Impact Factor: 4.98). 02/2007; 23(2):257-8. DOI: 10.1093/bioinformatics/btl567
Source: PubMed

ABSTRACT Functional analyses based on the association of Gene Ontology (GO) terms to genes in a selected gene list are useful bioinformatic tools and the GOstats package has been widely used to perform such computations. In this paper we report significant improvements and extensions such as support for conditional testing.
We discuss the capabilities of GOstats, a Bioconductor package written in R, that allows users to test GO terms for over or under-representation using either a classical hypergeometric test or a conditional hypergeometric that uses the relationships among GO terms to decorrelate the results.
GOstats is available as an R package from the Bioconductor project:

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    • "The GO enrichment analysis was conducted using the Bioconductor package GOstats (Falcon and Gentleman, 2007) as previously described (Liu et al. 2015). For the plots in Figure 4, all enriched goBP terms of five transcription factors were pooled, sorted by the term size, and filtered for enriched GO terms with sizes ranging from 100 to 1000, resulting in 120 terms total. "
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    • "The number of novel isoforms was determined from the final annotation based on transcripts flagged by Cufflinks with class_code ''j'' (indicating a novel, spliced transcript sharing at least one junction with an annotated transcript). Human GO terms were mapped to X. tropicalis genes and the hypergeometric test for enriched terms was performed using the GOstats Bioconductor package (Falcon and Gentleman, 2007). The aligned reads have been submitted to the NCBI Sequence Read Archive under BioProject ID PRJNA266550. "
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    Cell Reports 02/2015; 10(1):1-10. DOI:10.1016/j.celrep.2014.12.046 · 8.36 Impact Factor
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    • "Consequently, low-abundance and invariant genes will show lower connectivity. GO term enrichment analysis was performed using the R package GOstats (Falcon and Gentleman, 2007). We report GO terms as enriched when p < 0.05. "
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    ABSTRACT: Studies of the genetic basis and evolution of complex social behavior emphasize either conserved or novel genes. To begin to reconcile these perspectives, we studied how the evolutionary conservation of genes associated with social behavior depends on regulatory context, and whether genes associated with social behavior exist in distinct regulatory and evolutionary contexts. We identified modules of co-expressed genes associated with age-based division of labor between nurses and foragers in the ant Monomorium pharaonis, and we studied the relationship between molecular evolution, connectivity, and expression. Highly connected and expressed genes were more evolutionarily conserved, as expected. However, compared to the rest of the genome, forager-upregulated genes were much more highly connected and conserved, while nurse-upregulated genes were less connected and more evolutionarily labile. Our results indicate that the genetic architecture of social behavior includes both highly connected and conserved components as well as loosely connected and evolutionarily labile components.
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Seth Falcon