FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments

Abstract

Background 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. Results 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 . Conclusion 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. Electronic supplementary material The online version of this article (10.1186/s12859-019-2639-2) contains supplementary material, which is available to authorized users.

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

“…The results were visualized by using the FunMappOne graphical tool. [ 63 ] The activation z scores are shown (red: activation, green: deactivation). The cutoff for p ‐values was p < 0.001.…”

“…KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were analyzed and visualized by using the FunMappOne graphical tool. [63] Statistical Analysis: Experiments were performed in at least three biological replicates (for primary cells, three individual donors) and samples were analyzed using three technical triplicates. Data are average values ± S.D.…”

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data

“…Programmatic larger-scale analyses can be performed with the methods in the limma R package. R and Cytoscape-based workflows can also combine visualizations with pathway analyses in a very powerful manner [109,110]. Fisher's exact test based methods can be used if there are no other alternatives, especially for GO analyses, although packages such as the topGO are preferable in that case.…”

“…Since more and more toxicogenomics studies involve the comparison of the effect of different materials at the same time, we recently proposed a graphical tool implemented in R, called FunMappOne [110], that enables the users to graphically inspect, navigate, and compare functional annotations in multiple experiments at different levels of abstraction. This tool facilitates the analyses of multiple experimental conditions through a simple user interface and dynamic graphical representations of the relevant functional categories.…”

Manually curated and harmonised transcriptomics datasets of psoriasis and atopic dermatitis patients

“…Differential expression analysis can be carried for instance by the limma package 15 for the microarray data, and the edgeR 24 , DESeq 2 25 or NOISeq 21 packages for RNA-Seq data, respectively. Functional analysis of differentially expressed genes can be performed by using FunMappOne 26 , the R/Bioconductor package ReactomePA 27 or Ingenuity Pathway Analysis (Qiagen, http://www.ingenuity.com/products/ipa ). The inference and analysis of co-expression networks can be performed, for instance, by using the INfORM tool 28 .…”