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Magnetique: an interactive web application to explore transcriptome signatures of heart failure
Thiago Britto-Borges1,2,3, Annekathrin Ludt4, Etienne Boileau1,2,3
1Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg, Germany.
This study introduces Magnetique, an interactive web tool for exploring heart failure (HF) transcriptomes. It reveals key gene expression and RNA isoform changes in dilated (DCM) and hypertrophic (HCM) cardiomyopathy, aiding HF research.
Area of Science:
- Cardiovascular Biology
- Genomics
- Bioinformatics
Background:
- Heart failure (HF) research is hindered by limited accessibility and usability of RNA-sequencing (RNA-seq) data.
- A need exists for intuitive tools to explore the complex transcriptional landscape of HF.
Purpose of the Study:
- To develop an interactive web application for exploring HF transcriptomes.
- To provide researchers with a user-friendly platform for analyzing RNA-seq data in HF.
Main Methods:
- Re-analysis of the Myocardial Applied Genomics Network RNA-seq dataset, including patients with dilated (DCM) and hypertrophic (HCM) cardiomyopathy, and non-failing donors (NFD).
- Differential gene expression analysis, pathway signature identification, and signaling network reconstruction using inferred transcription factor activities.
- Analysis of differential RNA transcript isoform usage (DTU) and prediction of RNA-binding protein (RBP) interactions.
Main Results:
- Identified 201 differentially expressed genes in the DCM vs. HCM comparison, with notable associations to ERK1/ERK2 signaling pathway alterations.
- Discovered 149 differently used RNA transcripts in DCM vs. HCM, including O-linked N-acetylglucosamine (GlcNAc) transferase (OGT).
- Reconstructed RBP-target interaction networks, highlighting CPEB1 as differentially expressed in DCM vs. HCM.
Conclusions:
- Magnetique is the first online application offering interactive exploration of the HF transcriptome at the RNA isoform level.
- The application integrates transcription factor signaling and RBP:RNA interaction networks for comprehensive analysis.
- Source code for analysis and the web application are publicly available to facilitate HF research.
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