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APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools
Vicent Giménez-Alventosa1, José Damián Segrelles1, Germán Moltó1
1Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC-Universitat Politècnica de València, Valencia, Spain.
Methods of Information in Medicine
|August 11, 2020
Summary
This study introduces APRICOT, an open-source tool simplifying cloud infrastructure deployment for reproducible life science research. APRICOT enhances scientific reproducibility and reduces wasted resources in computational experiments.
Area of Science:
- Life Sciences
- Computational Biology
- Bioinformatics
Background:
- Irreproducible computational experiments limit scientific research quality and waste resources, particularly in life sciences where over 50% of preclinical research is irreproducible.
- Scientific reproducibility is crucial for Open Science, yet complex computational experiments often require specialized, multi-cloud virtual infrastructures.
- Deploying these infrastructures demands advanced cloud computing knowledge, posing a barrier for many researchers.
Purpose of the Study:
- To enhance reproducibility in life sciences research through simplified infrastructure deployment and usage.
- To enable more cost-effective and reliable scientific discoveries by addressing computational experiment reproducibility challenges.
Main Methods:
- Introduction of Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools (APRICOT), an open-source Jupyter extension.
- APRICOT facilitates the deployment of deterministic virtual infrastructures across multiple clouds for reproducible scientific experiments.
- Demonstration of APRICOT's utility with two life science examples, ensuring all reproduction requirements are disclosed within the platform.
Main Results:
- APRICOT was used to deploy a Message Passing Interface cluster for processing a prostate cancer magnetic resonance image.
- The platform demonstrated infrastructure scaling capabilities with a batch cluster for positron emission tomography image reconstruction.
- Both examples showcased APRICOT's effectiveness in managing complex computational requirements for reproducible research.
Conclusions:
- APRICOT integrates infrastructure deployment, management, and usage for Open Science, making computationally intensive experiments reproducible.
- All experimental details, from infrastructure specifications to termination, are documented within a single Jupyter notebook.
- Sharing the experimentation notebook and data is sufficient for users to reproduce the scientific experiments.
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