Abstract

The main objective of ViSEAGO package 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. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. 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. ViSEAGO is publicly available on https://bioconductor.org/packages/ViSEAGO .

Highlights

  • We developed ViSEAGO to carry out a data mining of biological functions supported by Gene Ontology (GO) terms and establish links between terms and genes involved in the scientific study

  • ViSEAGO package has been applied to three biological cases to illustrate its functionalities

  • ViSEAGO R package is a generic tool for functional analysis based on Gene Ontology that meets this challenge

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Summary

Introduction

While bioinformatical and biostatistical data analyses are quite robust, functional analysis remains a critical step of these high-throughput studies. One essential resource for such analysis is Gene Ontology (GO) [1, 2], that provides an unified vocabulary to describe gene functions (GO terms) and relations between them in three categories: biological processes (BP), molecular functions (MF) and cellular components (CC). GO annotation represents the association between a gene and a GO term. GO is structured in a graph, where each GO term is a node and edges are relations between GO terms. GO term annotations including GO acyclic graph and GO terms association tables are currently maintained and improved in major databases. Depending on the database being used, there are important differences between supported species and corresponding

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