Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich's ataxia, and other rare iron-related diseases
Gloria C Ferreira1, Jenna Oberstaller2, Renée Fonseca3
1Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, MDC 7, Tampa, FL, 33612, USA.
Insights
The "Iron Hack" event successfully utilized a modified hackathon model to foster collaboration between diverse experts, generating valuable bioinformatics tools for rare iron-related diseases and improving data science and clinical interaction. This approach aids rare disease research and diagnosis.
Area of Science:
- Bioinformatics
- Rare Diseases
- Computational Biology
Background:
- University of South Florida research integrates basic and clinical science for rare iron-related diseases.
- A modified National Center for Biotechnology Information (NCBI) Hackathon model, named "Iron Hack", was developed to leverage local expertise.
- A symposium on porphyrias and Friedreich's ataxia was held for general audiences to explain rare iron-related diseases.
Purpose of the Study:
- To demonstrate the utility of a hackathon model for generating generalizable rare disease tools.
- To train clinicians and data scientists in more effective interaction.
- To create prototypes for rare disease research and bioinformatics problems.
Main Methods:
- The hackathon featured daily presentations from experts in various fields including genetics, bioinformatics, and bioengineering.
- Guest speakers from the American Porphyria Foundation (APF) and Friedreich's Ataxia Research Alliance (FARA) shared the human impact of these diseases.
- A collaborative, problem-solving approach attracted participants from diverse backgrounds.
Main Results:
- Development of resources applicable to specific rare diseases and broader bioinformatics challenges.
- Successful creation of collaborative projects within two and a half days.
- Projects focused on data visualization, database construction, rare disease diagnosis improvement, and inheritance studies.
Conclusions:
- The hackathon model is effective in generating prototypes of generalizable tools for rare diseases.
- The "Iron Hack" initiative successfully trained clinicians and data scientists to interact more effectively.
- This approach can accelerate the development of solutions for rare diseases and bioinformatics issues.
Abstract:
Background: Basic and clinical scientific research at the University of South Florida (USF) have intersected to support a multi-faceted approach around a common focus on rare iron-related diseases. We proposed a modified version of the National Center for Biotechnology Information's (NCBI) Hackathon-model to take full advantage of local expertise in building "Iron Hack", a rare disease-focused hackathon. As the collaborative, problem-solving nature of hackathons tends to attract participants of highly-diverse backgrounds, organizers facilitated a symposium on rare iron-related diseases, specifically porphyrias and Friedreich's ataxia, pitched at general audiences. Methods: The hackathon was structured to begin each day with presentations by expert clinicians, genetic counselors, researchers focused on molecular and cellular biology, public health/global health, genetics/genomics, computational biology, bioinformatics, biomolecular science, bioengineering, and computer science, as well as guest speakers from the American Porphyria Foundation (APF) and Friedreich's Ataxia Research Alliance (FARA) to inform participants as to the human impact of these diseases. Results: As a result of this hackathon, we developed resources that are relevant not only to these specific disease-models, but also to other rare diseases and general bioinformatics problems. Within two and a half days, "Iron Hack" participants successfully built collaborative projects to visualize data, build databases, improve rare disease diagnosis, and study rare-disease inheritance. Conclusions: The purpose of this manuscript is to demonstrate the utility of a hackathon model to generate prototypes of generalizable tools for a given disease and train clinicians and data scientists to interact more effectively.
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