About the FRDA Transcriptomic Atlas
The Friedreich's Ataxia (FRDA) Transcriptomic Atlas is an interactive Shiny application that provides open access to harmonised transcriptomic analyses across publicly available FRDA RNA-seq datasets. This resource was developed to enable researchers, clinicians, and students to explore and compare genes, differential expression, isoforms etc across a broad range of human FRDA models. The Atlas serves as a central reference for transcriptome-wide alterations in FRDA, supporting hypothesis generation, candidate gene prioritisation, and data reuse for downstream integrative analyses. All analyses were performed using a consistent bioinformatic framework to ensure comparability across studies. Details of the datasets, quality control, and analytical pipelines are available in the associated publication here. Users are encouraged to consult the paper for methodological details and to cite it in any work derived from or informed by this resource.
Datasets are retrieved from external servers and may take 20 seconds to 3 minutes to load, with longer delays possible during periods of high demand.
How to Use
All instructions on how to use the app and its features can be found here: Instructions
Citation and Use
If you use this Atlas or any underlying data/analyses in your work, please cite the associated publication:
Maddock, M. L. et al. (2026). Friedreich ataxia transcriptomic dysregulation and identification of cell type-specific biomarkers: A systematic review and meta-analysis. bioRxiv. https://doi.org/10.64898/2026.03.18.712785
Users must cite the publication in any derivative analyses, figures, or reports generated from this resource. Users must also cite the original studies from which the data were derived. Please refer to the 'Datasets' section within the app for details on each dataset and its original publication.
Future Updates and Contributions
If you are interested in contributing data to the FRDA Transcriptomic Atlas, please get in touch. For contribution guidelines and further information, see the contribution page
Contact and Bug Reports
- Contact: mlm715@uowmail.edu.au
- Project GitHub: https://github.com/MarnieMaddock/FRDATranscriptomicAtlas
- Report an issue or request features: https://github.com/MarnieMaddock/FRDATranscriptomicAtlas/issues
Authorship and Acknowledgements
Developed by: Marnie Maddock, Prof. Mirella Dottori, University of Wollongong, Australia
We thank all researchers who generated and made publicly available the original datasets used in this atlas.
Datasets included
* = FRDA = patient-derived; CTRL = healthy sibling
† = FRDA = patient-derived; CTRL = matched control
∴ = FRDA = patient-derived; CTRL = wild-type control
‡ = FRDA = patient-derived; CTRL = unaffected control
§ = FRDA = patient-derived; IC = isogenic control
¶ = FRDA = patient-derived; IC = isogenic control (GAA excision, or GAA expansion replaced by healthy allele (E35))
Sample size: N = biological replicates (different genetic backgrounds); n = technical replicates.
Dataset Sequencing Metrics and Isoform Confidence Scores
Isoform confidence was classified as low, medium, or high using a composite score based on mean sequencing depth, mean read length, and library layout (paired-end vs single-end).
Mean sequencing depth was scored as 0 (<10 million reads), 1 (10-30 million reads), or 2 (>30 million reads); mean read length was scored as 0 (<75 bp), 1 (75-99 bp), or 2 (>=100 bp); and paired-end libraries were assigned +1 point relative to single-end libraries. Scores were summed, with higher scores indicating greater expected isoform resolution. Scores of 0-2 were classified as low confidence, scores of 3-4 as medium confidence, and scores of 5 as high confidence.