Ludt, A. | Search bioRxiv

bioRxiv2022

Magnetique: An interactive web application to explore transcriptome signatures of heart failure

Despite a recent increase in the number of RNA-seq datasets investigating heart failure (HF), accessibility and usability remain critical issues for medical researchers. We present Magnetique (https://shiny.dieterichlab.org/app/magnetique), an interactive web application to explore the transcriptional signatures of heart failure. We reanalyzed the Myocardial Applied Genomics Network RNA-seq dataset, one of the largest publicly available datasets of left ventricular RNA-seq samples from patients with dilated (DCM) or hypertrophic (HCM) cardiomyopathy, as well as unmatched non-failing hearts from organ donors and patient characteristics that allowed us to model confounding factors. Focusing on the DCM versus HCM contrast, we identified 201 differentially expressed genes and associated pathway signatures. Moreover, we predict underlying signaling networks based on inferred transcription factor activities. To the best of our knowledge, Magnetique is the first online application to provide an interactive view of the HF transcriptome by analyzing differential transcript isoform usage. Finally, another graphical view on statistically predicted RNA-binding protein to target transcript interactions complements the Magnetique web application. The source code for both the analyses (https://github.com/dieterich-lab/magnetiqueCode2022) and the web application (https://github.com/AnnekathrinSilvia/magnetique) is available to the public. We hope that our application will help users to uncover the molecular basis of heart failure.

bioinformatics↗

bioRxiv2021

GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data

BackgroundThe interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats - normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. ResultsWe developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. ConclusionGeneTonic is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both birds-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

bioinformatics↗