CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments

BMC Bioinformatics

Software

Samuel D. Bassett¹  Ashley J. Waardenberg²  Romaric Bouveret³  Richard P. Harvey⁴ 

[1] Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia;Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia;Present Address: Children’s Medical Research Institute, 2145, Westmead, NSW, Australia;Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia;St. Vincent’s Clinical School, University of New South Wales, 2052, Kensington, Australia;Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia;St. Vincent’s Clinical School, University of New South Wales, 2052, Kensington, Australia;School of Biotechnology and Biomolecular Sciences, University of New South Wales Faculty of Science, 2052, New South Wales, Australia;Stem Cells Australia, Melbourne Brain Centre, University of Melbourne, 3010, Victoria, Australia;

关键词: Gene Ontology;    Serum Response Factor;    Jaccard Coefficient;    Enrich Gene Ontology;    Hard Thresholding;   

DOI  :  10.1186/s12859-015-0701-2

 received in 2015-03-14, accepted in 2015-08-13,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundGene ontology (GO) enrichment is commonly used for inferring biological meaning from systems biology experiments. However, determining differential GO and pathway enrichment between DNA-binding experiments or using the GO structure to classify experiments has received little attention.ResultsHerein, we present a bioinformatics tool, CompGO, for identifying Differentially Enriched Gene Ontologies, called DiEGOs, and pathways, through the use of a z-score derivation of log odds ratios, and visualizing these differences at GO and pathway level. Through public experimental data focused on the cardiac transcription factor NKX2-5, we illustrate the problems associated with comparing GO enrichments between experiments using a simple overlap approach.ConclusionsWe have developed an R/Bioconductor package, CompGO, which implements a new statistic normally used in epidemiological studies for performing comparative GO analyses and visualizing comparisons from .BED data containing genomic coordinates as well as gene lists as inputs. We justify the statistic through inclusion of experimental data and compare to the commonly used overlap method. CompGO is freely available as a R/Bioconductor package enabling easy integration into existing pipelines and is available at: http://www.bioconductor.org/packages/release/bioc/html/CompGO.html packages/release/bioc/html/CompGO.html

【 授权许可】

CC BY   
© Waardenberg et al. 2016