MBS 337: Research Computing in Biology
The objective of this course is to give students practical experience with essential computing skills used in modern biological research settings. The course teaches core principles of research software engineering, reproducibility, automation, scientific data management, development and operations (DevOps), cloud computing, and high-performance computing to address real-world applications in bioimaging, genomics, protein folding, and drug discovery. By the end of the course, students will be equipped to design, implement, and share robust computational pipelines for a variety of biological research problems.
Course Schedule:
- Unit 1: Onboarding and Essential Skills
- Unit 2: Working with Common Data Formats
- Unit 3: Working with Common Bioinformatics Data Formats
- Unit 4: Best Practices in Python
- Unit 5: Containerization and Automation
- Unit 6: Databases, Persistence, Redis, and APIs
- Unit 7: Jupyter and Visualization for Data Analysis
- Unit 8: Building Dashboards with Plotly Dash
- Unit 9: Continuous Integration / Continuous Deployment
- Unit 10: Machine Learning Operations
- Unit 11: High Performance Computing
- Unit 12: Workflow Managers
Homeworks:
Additional Resources:
Class Docs: https://mbs-337-sp26.readthedocs.io/
Canvas: https://utexas.instructure.com/