Welcome to the official DiPhyx Case Studies repository. This repository hosts real-world computational pipelines and example workflows designed to demonstrate how DiPhyx can power complex, compute-intensive scientific models. These examples span various disciplines, with a current focus on Bioinformatics, Genomics, and Biotech use cases.
DiPhyx is a unified cloud platform built to simplify and accelerate scientific computing. It enables researchers, engineers, and data scientists to design, run, monitor, and productionize scientific workflows effortlessly. Unlike general-purpose cloud tools, DiPhyx is purpose-built for scientific computing and includes built-in tools for job tracking, reproducibility, optimization, and visualization.
Whether you are a seasoned DiPhyx user or just exploring advanced scientific computing tools, this repository provides valuable examples you can adapt, extend, and learn from.
If you're a DiPhyx user:
- Launch your desired compute-unit via DiPhyx Dashboard
- Choose the
flowfrom theNew Projectlist. - Use the data and scripts provided in the case study to run your analysis.
While DiPhyx provides native support and enhanced features, most of the case studies can also be run locally or on other cloud environments with minimal setup.
Make sure you have the required dependencies installed. Each case study lists the needed packages or containers.
- dxflow: The DiPhyx-native CLI and orchestration engine
- JupyterLab: Integrated interface for notebooks
- REST API: For productionizing and automating scientific workflows
- Common tools: RDKit, GROMACS, Boltz1, PyMOL, FastQC, and more
We welcome contributions from the community! Whether you have a case study to share, want to fix an issue, or suggest improvements:
- Fork the repository
- Submit a pull request
- Or reach out to us with your suggestions
For questions, bug reports, or demo requests:
- Email: info@diphyx.com
- Visit: https://diphyx.com
Thank you for exploring the future of scientific computing with DiPhyx!