PYTHON FOR BIOINFORMATICS
Introduce diverse strategies for computational analysis of macromolecular data using Python including sequence alignment, genome annotation, data retrieval from databases, phylogenetic analysis, and molecular evolution. Experiential learning is emphasized; confidence in practical skills is developed through persistent application of course content to projects focused on current problems in bioinformatic research.
Prerequisites & course information
Requisites: Declared in MS Applied Biotechnology or Capstone Certificate in Applied Bioinformatics Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2025 Learning Outcomes: 1. Select appropriate computational strategies for the analysis of genomic, transcriptomic, proteomic, and metabolomic data. Audience: Graduate 2. Implement analyses of large datasets and model relationships among elements of very complex systems. Audience: Graduate 3. Use python programming strategies to solve problems in bioinformatics. Audience: Graduate 4. Demonstrate the ability to integrate python programming strategies with complementary resources, especially UNIX, GitHub, and libraries. Audience: Graduate Learning Outcomes: 1. Select appropriate computational strategies for the analysis of genomic, transcriptomic, proteomic, and metabolomic data. Audience: Graduate 2. Implement analyses of large datasets and model relationships among elements of very complex systems. Audience: Graduate 3. Use python programming strategies to solve problems in bioinformatics. Audience: Graduate 4. Demonstrate the ability to integrate python programming strategies with complementary resources, especially UNIX, GitHub, and libraries. Audience: Graduate
Sources
- Course catalogue ↗2026-2027