RNA-seq transcriptome analysis workflows often generate the essential information (data and results) among a variety of different tabular files and formats, e.g. raw and normalized expression values, results of differential gene expression analysis, or functional enrichment analysis. If this information is fragmented over single different files, the interpretation of the results can be hampered. We present the GeneTonic package (https://bioconductor.org/packages/Gen…), containing a Shiny application which provides an efficient and interactive possibility to combine the results of RNA-seq analysis. GeneTonic assists the identification of relevant functional patterns, as well as their contextualization in the data and results at hand, with interactivity (to make the analysis simple and accessible) and reproducibility (via RMarkdown reports) to simplify the integration of all components. With GeneTonic, users can generate a variety of visualizations, including bird’s eye perspective summaries (with interactive bipartite gene-geneset graphs or enrichment maps) as well as detailed information and visualizations of individual genes and gene-sets. These can be further inspected via drill-down actions that display additional content in specific elements of the user interface. In this workshop, we will provide an overview of the functionality of GeneTonic, and demonstrate its usage in classical RNA-seq workflows, showcasing how it is possible to integrate our package with the single components of typical differential expression scenarios, with the goal of streamlining the interpretation and in-depth exploration for this widely used application.
GeneTonic: enjoying the interpretation of your RNA seq data analysis
Marini F, Ludt A, Linke J, Strauch K. (2021) GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data. bioRXiv [online preprint]. [abstract]