Abstract: RNA-seq transcriptome analysis workflows often generate the essential information (data and results) distributed 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. The efficient interpretation of the results can be hampered due to this fragmentation, and the same can happen even when providing static analysis reports. 
  We developed the GeneTonic package (https://bioconductor.org/packages/GeneTonic/), containing a Shiny application which provides an efficient and interactive solution to combine the results of RNA-seq analysis. GeneTonic assists users in 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 and communication of results. 
  With GeneTonic, researchers 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, streamlining analysis, interpretation, and knowledge extraction of transcriptome data for a broad spectrum of collaborating scientists. 
  (https://doi.org/10.1101/2021.05.19.444862)
Speaker: Annekathrin Ludt
Title: GeneTonic: enjoying the interpretation of your RNA-seq data analysis
Abstract_Sanitzed: RNA-seq transcriptome analysis workflows often generate the essential information (data and results) distributed 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. The efficient interpretation of the results can be hampered due to this fragmentation, and the same can happen even when providing static analysis reports.  We developed the GeneTonic package (https//bioconductor.org/packages/GeneTonic/), containing a Shiny application which provides an efficient and interactive solution to combine the results of RNA-seq analysis. GeneTonic assists users in 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 and communication of results.  With GeneTonic, researchers 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, streamlining analysis, interpretation, and knowledge extraction of transcriptome data for a broad spectrum of collaborating scientists.  (https//doi.org/10.1101/2021.05.19.444862)