ViSEAGO {ViSEAGO} | R Documentation |
Easier data mining of biological functions organized into clusters using Gene Ontology and semantic.
The main objective of ViSEAGO workflow is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest.
It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge.
The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology
(pkgdiagram
).
It provides access to the last current GO annotations (annotate
), which are retrieved from one of
NCBI EntrezGene (Bioconductor2GO
, EntrezGene2GO
),
Ensembl (Ensembl2GO
) or Uniprot (Uniprot2GO
) databases
for available species (available_organisms
).
ViSEAGO extends classical functional GO analysis (create_topGOdata
) to focus on functional coherence
by aggregating closely related biological themes while studying multiple datasets at once (merge_enrich_terms
).
It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure
(MDSplot
, GOterms_heatmap
, GOclusters_heatmap
), and ensuring functional
coherence supplied by semantic similarity (build_GO_SS
, compute_SS_distances
).
ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.