Dataset Information

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

ABSTRACT: Gene 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.Herein, 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.We 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.

SUBMITTER: Waardenberg AJ

PROVIDER: S-EPMC4557902 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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

Waardenberg Ashley J AJ Basset Samuel D SD Bouveret Romaric R Harvey Richard P RP

BMC bioinformatics 20150902

<h4>Background</h4>Gene 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.<h4>Results</h4>Herein, 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 ...[more]

PMID: 26329719

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Project description:BackgroundThe construction of the Disease Ontology (DO) has helped promote the investigation of diseases and disease risk factors. DO enables researchers to analyse disease similarity by adopting semantic similarity measures, and has expanded our understanding of the relationships between different diseases and to classify them. Simultaneously, similarities between genes can also be analysed by their associations with similar diseases. As a result, disease heterogeneity is better understood and insights into the molecular pathogenesis of similar diseases have been gained. However, bioinformatics tools that provide easy and straight forward ways to use DO to study disease and gene similarity simultaneously are required.ResultsWe have developed an R-based software package (DOSim) to compute the similarity between diseases and to measure the similarity between human genes in terms of diseases. DOSim incorporates a DO-based enrichment analysis function that can be used to explore the disease feature of an independent gene set. A multilayered enrichment analysis (GO and KEGG annotation) annotation function that helps users explore the biological meaning implied in a newly detected gene module is also part of the DOSim package. We used the disease similarity application to demonstrate the relationship between 128 different DO cancer terms. The hierarchical clustering of these 128 different cancers showed modular characteristics. In another case study, we used the gene similarity application on 361 obesity-related genes. The results revealed the complex pathogenesis of obesity. In addition, the gene module detection and gene module multilayered annotation functions in DOSim when applied on these 361 obesity-related genes helped extend our understanding of the complex pathogenesis of obesity risk phenotypes and the heterogeneity of obesity-related diseases.ConclusionsDOSim can be used to detect disease-driven gene modules, and to annotate the modules for functions and pathways. The DOSim package can also be used to visualise DO structure. DOSim can reflect the modular characteristic of disease related genes and promote our understanding of the complex pathogenesis of diseases. DOSim is available on the Comprehensive R Archive Network (CRAN) or http://bioinfo.hrbmu.edu.cn/dosim.

Dataset Information

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

Publications

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

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