NeVOmics: An Enrichment Tool for Gene Ontology and Functional Network Analysis and Visualization of Data from OMICs Technologies

• Published in: Genes Vol. 9; no. 12; p. 569
• Main Authors: Zúñiga-León, Eduardo, Carrasco-Navarro, Ulises, Fierro, Francisco
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.11.2018; MDPI
• Subjects: Annotations; enrichment analysis; Gene Ontology; Genes; Genomes; KEGG pathways; Ontology; Organisms; plot visualization; Programming languages; Proteins; proteomic analysis; Proteomics; Visualization; plot visualization; Gene Ontology; KEGG pathways; enrichment analysis; proteomic analysis
• ISSN: 2073-4425; 2073-4425
• DOI: 10.3390/genes9120569

The increasing number of OMICs studies demands bioinformatic tools that aid in the analysis of large sets of genes or proteins to understand their roles in the cell and establish functional networks and pathways. In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at https://github.com/bioinfproject/bioinfo/.