Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.
BioMediTech Institute, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.
BMC Bioinformatics. 2019 Feb 15;20(1):79. doi: 10.1186/s12859-019-2639-2.
Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high.
We developed an approach that exploits the intrinsic hierarchical structure of several functional annotations to summarize the results obtained through enrichment analyses to higher levels of interpretation and to map gene related information at each summarized level. We built a user-friendly graphical interface that allows to visualize the functional annotations of one or multiple experiments at once. The tool is implemented as a R-Shiny application called FunMappOne and is available at https://github.com/grecolab/FunMappOne .
FunMappOne is a R-shiny graphical tool that takes in input multiple lists of human or mouse genes, optionally along with their related modification magnitudes, computes the enriched annotations from Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, or Reactome databases, and reports interactive maps of functional terms and pathways organized in rational groups. FunMappOne allows a fast and convenient comparison of multiple experiments and an easy way to interpret results.
功能注释是组学数据分析的重要步骤。目前有多个数据库和方法可用于将基因的功能总结为更高层次的表示形式，如本体论和分子途径。将组学实验的结果注释到功能类别中不仅对于理解潜在的调控动态至关重要，而且对于在更高的抽象层次上比较多个实验条件也至关重要。社区已经有几种工具可用于表示和比较组学实验的功能谱。然而，当实验数量和/或丰富的功能术语数量较高时，即使以图形方式表示，也很难解释结果。因此，目前需要交互和用户友好的工具来以图形方式导航和进一步总结注释，以便在维度较高时也能促进结果的解释。
我们开发了一种方法，利用几种功能注释的内在层次结构，将通过富集分析获得的结果总结到更高的解释水平，并在每个总结的层次上映射基因相关信息。我们构建了一个用户友好的图形界面，可一次可视化一个或多个实验的功能注释。该工具实现为一个名为 FunMappOne 的 R-Shiny 应用程序，并可在 https://github.com/grecolab/FunMappOne 上获得。
FunMappOne 是一个 R-shiny 图形工具，它可以输入多个人类或小鼠基因列表，还可以选择输入与其相关的修饰幅度，从基因本体论、京都基因与基因组百科全书或反应数据库中计算丰富的注释，并报告以合理组组织的功能术语和途径的交互式映射。FunMappOne 允许快速方便地比较多个实验，并轻松解释结果。
