GONOME: measuring correlations between GO terms and genomic positions

BMC Bioinformatics
Stefan M StanleyJohn S Mattick

Abstract

Current methods to find significantly under- and over-represented gene ontology (GO) terms in a set of genes consider the genes as equally probable "balls in a bag", as may be appropriate for transcripts in micro-array data. However, due to the varying length of genes and intergenic regions, that approach is inappropriate for deciding if any GO terms are correlated with a set of genomic positions. We present an algorithm--GONOME--that can determine which GO terms are significantly associated with a set of genomic positions given a genome annotated with (at least) the starts and ends of genes. We show that certain GO terms may appear to be significantly associated with a set of randomly chosen positions in the human genome if gene lengths are not considered, and that these same terms have been reported as significantly over-represented in a number of recent papers. This apparent over-representation disappears when gene lengths are considered, as GONOME does. For example, we show that, when gene length is taken into account, the term "development" is not significantly enriched in genes associated with human CpG islands, in contradiction to a previous report. We further demonstrate the efficacy of GONOME by showing that occurrence...Continue Reading

References

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Citations

Dec 20, 2011·Nature Protocols·Anaïs F BardetAlexander Stark
Feb 8, 2008·BMC Bioinformatics·Frédéric Chalmel, Michael Primig
Sep 11, 2012·Genetics·Julie M GrankaMarcus W Feldman
Jul 6, 2014·Gene·Timo TiirikkaMauno Vihinen
Jan 5, 2010·Developmental Biology·Julia Zeitlinger, Alexander Stark

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Software Mentioned

MEME
BIOPERL
BLAST
MathType
GONOME
GO TermFinder
GOStat
SGD

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