Abstract
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
Highlights
Gene ontology (GO) enrichment is commonly used for inferring biological meaning from systems biology experiments
We present an open-source implementation of a comparative GO approach, CompGO, which is readily adaptable to existing analysis pipelines for performing these functions and implement a log odds ratio [10, 11] normally applied to epidemiological studies for comparing GO enrichment directly
To determine the utility of the methods proposed in CompGO we downloaded DNA targeted regions for a number of wild-type (WT) and mutated cardiac transcription factors (TFs) identified by Bouveret et al [12] using the DamID method, and compare the outcomes using a simple overlap approach
Summary
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
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