Computational biology combines the power of modern computing and mathematical modeling with biological research to study how systems behave, evolve and adapt. Both UW and Fred Hutch are home to some of the country’s top computational biology labs, pushing the boundaries of traditional benchwork in genetics, virology, microbiology and beyond.
Area Directors
Area Directors help advise students about classes and rotations in their interest area. They also provide a listing of suggested courses for those interested in Computational Biology.
Faculty Area Directors
- Erick Matsen, Fred Hutch, Public Health Sciences Division (matsen@fredhutch.org)
- Manu Setty, Fred Hutch, Basic Sciences Division, Public Health Sciences Division (msetty@fredhutch.org)
Student Area Directors
- Sarah Huang, Setty Lab (huangy57@uw.edu)
- Cailin Jordan, Setty Lab (cailinj@uw.edu)
Suggested Curriculum
The suggested curriculum outlined below is meant to guide you in choosing classes. We highly encourage you to take the Foundational courses, while the Electives are more specialized and often cross between Areas of Interest. Remember to review the UW Time Schedule for the most accurate and up-to-date information regarding whether a course is currently being offered.
MCB students must take a total of 18 graded credits before taking their General Exam. They must be 500-level courses listed in the Area of Interest offerings. Students may petition the MCB co-directors (mcb@uw.edu) to receive credit for courses not listed, or lower-level courses.
2025-2026 Suggested Curriculum (document download)
Foundational Courses
Introduction to Computational Molecular Biology: Molecular Evolution
Computational methods for studying molecular evolution. Students must be able to write computer programs in Python/R for data analysis.
Prerequisite: Prior coursework in biology and probability.
Quarter and frequency offered: Spring (SPR), every year
Weeks: 1-10
Credits: 4.0
Last offering: SPR 2026
Next offering: SPR 2027
Introduction to Statistical and Computational Genomics
Emphasis on basic probability and statistics, and in introduction to computer programming. This course is intended to introduce students with non-computer science background to the major concepts of programming and statistics. After taking this course, students will be able to describe and perform basic analysis tasks relating to biological sequence analysis, phylogenetics, pedigree analysis, genetic association studies, population genetics and microarray analysis. Students will be able to demonstrate an understanding of fundamental statistical concepts, such as p-values, t-tests, chi-squared tests and multiple testing correction. Finally, students will be able to write computer programs to perform statistical and bioinformatics analysis.
Instructions: Add code required. For those with very little or no programming experience, contact instructor to obtain permission to enroll.
Quarter and frequency offered: Winter (WIN), every year
Weeks: 1-10
Credits: 3.0
Last offering: WIN 2026
Next offering: WIN 2027
Introduction to Statistical and Computational Genomics
An introduction to fundamental concepts necessary for the analysis of genetic and genomic data including, basic elements of probability theory, parameter estimation, and hypothesis testing. Prerequisite: Permission of instructor.
Quarter and frequency offered: SPR, every year
Weeks: 1-10
Credits: 3.0
Last offering: SPR 2026
Next offering: SPR 2027
Tools for Computational Biology
Introduction to established best practices in computational biology. Learn to organize unstructured data into standard formats, transform data for statistical analyses, and visualize the transformed data. Learn workflows for reproducible research such as version control, project organization, and code documentation. Gain basic experience with Linux command line tools and the Python and R programming languages. Classes will involve hands-on learning through coding exercises, collaborative problem solving, and extensive use of online learning resources.
Quarter and frequency offered: Autumn (AUT), every year
Weeks: 1-10
Credits: 3.0
Last offering: AUT 2025
Next offering: AUT 2026
Elective Courses
Data Visualization
Covers techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics include data and image models; visual encoding; graphical perception; color; animation; interaction techniques; graph layout; and automated design. Lectures, reading, and project.
Quarter and frequency offered: SPR, every year
Weeks: 1-10
Credits: 4.0
Last offering: SPR 2026
Next offering: SPR 2027
Computational Biology
Introduces computational methods leveraging artificial intelligence (AI) and machine learning (ML) techniques to understand biological systems and enhance healthcare. Utilizes various AI/ML techniques, including explainable AI, interpretable ML, deep learning, probabilistic graphical models, and causal inference. Explores diverse problem areas such as genetics, epigenomics, transcriptomics, proteomics, imageomics, and electronic health records.
Instructions: Contact instructor for approval.
Quarter and frequency offered: WIN
Weeks: 1-10
Credits: 4.0
Last offering: WIN 2026
Next offering: Information not available
Software Development for Data Scientists
Provides students outside of CSE with a practical knowledge of software development that is sufficient to do graduate work in their discipline. Modules include Python basics, software version control, software design, and using Python for machine learning and visualization.
Quarter and frequency offered: AUT
Weeks: 1-10
Credits: 4.0
Last offering: AUT 2025
Next offering: Information not available
Computational Biology Seminar (Seminar C)
A taste of current research in Computational Biology (local and non-) + critical reading of literature + presentation skills. Students, with faculty advice, pick and present CompBio papers from recent journals/conferences. Students & faculty also present their own research (mostly in Spring, but may be sprinkled throughout, depending on schedules). Background knowledge of biology is not assumed; come learn!
Instructions: Contact ugrad-adviser@cs.washington.edu for add code.
Quarter and frequency offered: AUT/WIN/SPR, every year
Weeks: 1-10
Credits: 1.0-3.0
Last offering: SPR 2026
Next offering: AUT 2026, WIN 2027, SPR 2027
Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis
Algorithmic and probabilistic methods for analysis of DNA and protein analysis. Students must be able to write computer programs for data analysis. Prior coursework in biology and probability is highly desirable.
Quarter and frequency offered: WIN, every year
Weeks: 1-10
Credits: 4.0
Last offering: WIN 2026
Next offering: WIN 2027
Bioinformatics Workflows for High-Throughput Sequencing Experiments
Programming skills and software tools for building automated bioinformatics pipelines and computational biology analyses. Emphasis on UNIX tools and R libraries for distilling raw sequencing data into interpretable results. For students familiar with UNIX and with some programming experience in Python, R, or C/C++.
Quarter and frequency offered: SPR, every year
Weeks: 1-5
Credits: 1.5
Last offering: SPR 2026
Next offering: SPR 2027
Econometrics I: Introduction to Mathematical Statistics
Examines methods, tools, and theory of mathematical statistics. Covers, probability densities, transformations, moment generating functions, conditional expectation. Bayesian analysis with conjugate priors, hypothesis tests, the Neyman-Pearson Lemma. Likelihood ratio tests, confidence intervals, maximum likelihood estimation, Central limit theorem, Slutsky Theorems, and the delta-method. Offered jointly with CS&SS 509/ECON 580.
Prerequisite: STAT 311; MATH 126 or MATH 136; and MATH 208 or MATH 209.
Quarter and frequency offered: AUT
Weeks: 1-10
Credits: 4.0
Last offering: AUT 2025
Next offering: Information not available
Spotlight
MCB Data Science Option
The MCB Data Science Option will provide interested students within the program with specific training in the fundamentals of Data Science methods and applications. Having a working understanding of data science tools is an increasingly important aspect of scientific training in virtually all fields.