Abstract
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/.
Highlights
Omics technologies are revolutionizing biological research by enabling genome-scale analysis of complex biological systems and processes [1]
NeVOmics is an integral tool with two major features: it allows enrichment analysis from a given list with data from some ‘OMICs’ experiment, and it builds different graphical representations in network form from the enrichment results
The first dataset comes from a platelet proteome of patients with early-stage cancer [18], and the second comes from a transcriptome analysis of mutants in tail module subunits of Mediator in Arabidopsis thaliana, a model system for research in plant biology [19]
Summary
Omics technologies are revolutionizing biological research by enabling genome-scale analysis of complex biological systems and processes [1]. Encyclopedia of Genes and Genomes, KEGG [6]), using statistical testing to find biological terms, and functional annotations that are significantly enriched in a list of genes/proteins. The aim of the enrichment analysis is finding biological annotations that are over-represented in the query gene/protein list compared to what would be expected in a reference list (usually the whole proteome) [7]. For Gene Ontology analysis, NeVOmics builds background lists organized by category (Biological Process, Molecular Function, Cellular Component) of a specific organism for mapping the query protein list. For KEGG Pathways analysis NeVOmics builds a background file with all genes of a specific organism for mapping the query gene list
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