Python for Bioinformatics
This course introduces computational analysis of macromolecular data using Python, covering sequence alignment, genome annotation, database retrieval, phylogenetics, and molecular evolution with an emphasis on experiential learning.
Summary
What you’ll be able to do
Select appropriate computational strategies for the analysis of genomic, transcriptomic, proteomic, and metabolomic data.
Implement analyses of large datasets and model relationships among elements of very complex systems.
Use python programming strategies to solve problems in bioinformatics.
Demonstrate the ability to integrate python programming strategies with complementary resources, especially UNIX, GitHub, and libraries.
Source: UW Guide
Grade history
↗No recorded grade history.
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Prerequisites
Course mapDeclared in MS Applied Biotechnology or Capstone Certificate in Applied Bioinformatics
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Declared in MS Applied Biotechnology or Capstone Certificate in Applied Bioinformatics
Professors
Spring 2027No instructors recorded for this selection.
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Student experience
the class
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.
difficulty & workload
No workload feedback recorded.
Topics
- Sequence alignment, genome annotation, database retrieval, phylogenetic analysis, and molecular evolution.
- Analysis of genomic, transcriptomic, proteomic, and metabolomic data.
- Integration of Python with UNIX, GitHub, and libraries.
Skills
- Computational analysis of macromolecular data using Python, including sequence alignment, genome annotation, database retrieval, phylogenetic analysis, and molecular evolution.
- Implementation of analyses on large datasets and modeling relationships in complex systems.
- Integration of Python programming with UNIX, GitHub, and libraries.
Grades
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Catalog & offerings
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
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Model & dataset provenance
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"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "064da73641e75cf3d4ee2ea8ceac5767b6f7ca49f2236b2301d33a7c43ea42fd",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}