Zúñiga-León Eduardo, Carrasco-Navarro Ulises, Fierro Francisco
Departamento de Biotecnología, Universidad Autónoma Metropolitana-Unidad Iztapalapa, Ciudad de Mexico 09340, Mexico.
Genes (Basel). 2018 Nov 23;9(12):569. 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/.
越来越多的组学研究需要生物信息学工具来辅助分析大量基因或蛋白质，以了解它们在细胞中的作用，并建立功能网络和通路。在过去十年中，过度表达或富集工具在对大量基因/蛋白质列表的功能分析中发挥了成功的作用，这从数千篇引用这些工具的出版物中得到了证明。然而，在大多数情况下，这些分析的结果是与蛋白质相关的一长串生物学术语，难以理解和解读。在这里，我们介绍NeVOmics，即基于网络的组学可视化工具，这是一种功能富集分析工具，可识别给定基因/蛋白质组中统计学上过度表达的生物学术语。该工具提供超几何分布测试以计算显著富集的生物学术语，并便于分析蛋白质与过程和通路的聚类分布及关系。NeVOmics适用于使用来自两个主要注释数据库（基因本体论和京都基因与基因组百科全书（KEGG））的更新信息。在识别生物学术语方面，NeVOmics在覆盖范围上优于其他基因本体论和富集工具。NeVOmics还可以根据富集结果构建不同的基于网络的图形表示，这使其成为一个综合工具，极大地便于解读通过组学方法获得的结果。可通过https://github.com/bioinfproject/bioinfo/免费访问NeVOmics。
