Genomic Courses

UW-Madison offers a diverse collection of courses that incorporate genomic and genome-enabled perspectives.  Below is a list of courses offered on campus and taught by CGSI faculty or related to genomic topics.  For more information about these courses and availability, go to the Course Guide through your MyUW account.  Visitors may access the Course Guide.

Biochem 72, Section 8 – Responsible Conduct of Research

The topics for the course will be introduced largely through the use of case studies, relevant to the genomic sciences.

Offered every Fall, 1 credit. Lead instructor: David C. Schwartz

Pre-requisite: GSTP trainees and other graduate students, as space permits.

AS/DS362 – Veterinary Genetics

In this course, students learn the molecular basis of diseases in animals and integrate knowledge acquired from different disciplines to learn how environmental factors affect gene activity and hence affect phenotypes. The focus is on the role of epigenetics in livestock production, reproduction, and animal health.

Offered every Fall, 2 credits. Instructor: Hasan Khatib.

Pre-requisites: AS/DS361

ANS 366 – Concepts in Genomics

Basic overview of the latest concepts in genomics, including genome organization, the importance of genome annotation, the use of genomic testing in plant and animal breeding, the potential of genomic prediction on human medicine, and the latest advances in omics integration.

Offered every Fall, 3 credits. Instructor: Francisco Peñagaricano.

Pre-requisites: none.

Zoology 415 – Genetics of Human History 

Covers a range of topics related to human genetics and evolution. Explores questions about what genetic differences between humans tell us about our species’ evolutionary and demographic history, and conversely, how our history has shaped the genetic diversity of people living today. At a time of rapidly increasing ability to sequence huge numbers of genomes such questions play a central role in understanding how genetics impacts individuals’ disease risks, how to interpret reported ancestry and family history from direct-to-consumer genotyping kits, and how genetics can (or importantly, cannot) inform descriptions of human diversity and concepts of race. Includes topics of diversity and inclusion in genomics studies, with a focus on the application and limits of results obtained from one group to our understanding of other groups. 

Spring, 3 credits. Instructor: Aaron Ragsdale.

Pre-requisites: (BIOLOGY/ZOOLOGY 101 and 102), BIOLOGY/BOTANY/ZOOLOGY 152, or BIOCORE 381.

PHARMACY 434 – Pharmaceutical Genetics and Immunology

Facilitates the understanding and application of the principles of pharmaceutical genetics, immunology, and biotechnology.

Offered every Spring, 2 credits. Instructors: Jason Peters, Mary Hayney, and Quanyin Hu.

Pre-requisite: Declared in Doctor of Pharmacy program.

Pharmsci 493/Chemistry 622 – Organic Analysis by Mass Spectrometry

Mass spectrometry has emerged as a powerful analytical tool for
biomedical research. With the explosion in genomic information, increasing attention is
focused on the studies of functional roles and interactions of expressed proteins. Mass
spectrometry is playing an increasingly important role in this rapidly expanding field.
This course provides the basics in instrumentation and applications of mass
spectrometry in life sciences with special emphasis on proteomics, neuroscience,
disease diagnosis, and drug discovery. The goals of this course are to provide students
with necessary background to effectively design mass spectrometric experiments; more
efficiently utilize the available mass spectrometric resources on campus, and effectively
interpret and use mass spectrometric data to help their own research. Graduate
students and senior undergraduates who want to learn how the tools of mass
spectrometry can facilitate their research goals are encouraged to register for the class.
Students will present an original research proposal on a topic related to the course
content at the end of the semester in the context of an informal group meeting. This
course is offered every other Fall semester, i.e., fall semester in odd year.

Offered every other Fall (odd years), 2 credits. Instructor: Lingjun Li.

Genetics 548 – The Genomic Revolution 

Profound advances are now possible thanks to genomic data and analysis. Introduces the structure, function, and evolution of genomes. It also outlines the realized and prospective benefits of genomic technology for human health, agriculture, and conservation. 

Spring, 3 credits. Instructor: Nicole Perna; Bret Payseur.

Pre-requisites: GENETICS 466, 468, or BIOCORE 587.

Genetics 564 – Genomics and Proteomics 

The basic principles of genomics, proteomics and bioinformatics will be taught through a semester-long project of the students choosing. Creative problem solving in science skills will be learned through a variety of active-learning techniques that include: reading of primary literature, group presentations, peer review, bioinformatic lab exercises, science communication skills (writing & visualization), and creating a website. Emphasis will be placed upon how to effectively communicate science (written, oral and written). Topics include: genomic sequencing, phylogeny, domain analysis, transcriptomics, CRISPR screens, chemical genomics, quantitative proteomics and protein networks. Capstone course. 

Spring, 3 credits. Instructor: Ahna Skop.

Pre-requisite: GENETICS 466, 468, or BIOCORE 587. Not open to graduate students. 

Genetics 565 – Human Genetics

Principles, problems, and methods of modern human genetics.  Focuses on how researchers discover the genetics of diseases and how those discoveries are used to improve clinical practice.  Surveys aspects of (i) the molecular function of the human genome, (ii) the basic principles of human genetics including statistical genetics, quantitative genetics, and genomic variation in human populations, (iii) the genetics of rare disorders and common diseases, and genomic analysis approaches, including genome-wide association studies and sequencing, and (iv) how genetics are used in medicine and discussions covering ethical considerations of human genomic data.

Offered every Fall, 3 credits. Instructors: Donna Werling and Steven Schrodi.

Pre-requisites: Genetics 466 or Genetics 468

BMI/COMP SCI 576 – Introduction to Bioinformatics

Algorithms for computational problems in molecular biology. Studies algorithms for problems such as: genome sequencing and mapping, pairwise and multiple sequence alignment, modeling sequence classes and features, phylogenetic tree construction, and gene-expression data analysis.

Offered every Fall, 3 credits. Instructor: Colin Dewey.

Pre-requisites: (COMP SCI 400 or COMP SCI 320) and MATH 222

Genetics 626 / CHEM 826– Genomic Science

This special topics course is designed to bring cutting-edge topics in the genomic sciences into the reach of traditionally “pure” chemistry, biology, engineering, computer science, & statistics students. It is also designed for enabling biologically-oriented students to deal with the advances in analytical science so that they may incorporate new genomic science concepts into their own scientific repertoires.

Offered every Spring, 2 credits. Instructor: David C. Schwartz

Pre-requisite: Graduate student in the sciences, or engineering–advanced undergraduates require instructor permission

Genetics 633 – Population Genetics

This is graduate-level (and upper-level undergraduate) course in population genetics, aimed at preparing students to initiate research in this field.  We will explore how genetic variation is influenced by mutation and recombination, population size changes and migration, and natural selection for or against new mutations.  A major focus is on how to analyze modern population genomic data sets.

Offered every other Spring, 3 credits. Instructor: John Pool.

Pre-requisites: A basic genetics course (Genetics 466, Genetics 468, or Biocore 381), or graduate student standing, or consent of instructor.

MICRO657 – Bioinformatics for Microbiologists

This course will provide a practical and fundamental introduction to sequence-based analysis focused on microbial systems. No prior knowledge of computational biology is required. Emphasis on gaining a basic understanding of the principles of both classical and newer algorithms useful for bioinformatics analysis. Students will spend a significant portion of the class on the computer learning a number of skills including, but not limited to, Unix command line, installation of bioinformatics programs (e.g. BLAST), databases, and introductory programming in Python. Evaluation will include a midterm exam and a project geared towards solving a bioinformatics problem. Topics to be covered include: BLAST; RNA-seq analysis; transcriptional binding prediction; genome sequence assembly, analysis and annotation; and comparative genomics. Other topics may be covered, as time permits. This course requires that each student have access to a laptop that runs a Linux/Unix Operating System such as a Mac or a ChromeBook. PCs running Windows 10 or a VM are also acceptable.

Offered every Spring, 3 credits. Instructor: Garret Suen.

Pre-requisites: This course is open to both graduate and undergraduate students with consent of the instructor. All undergraduates are expected to have taken one of MICROBIO 303, BIOCHEM 501, GENETICS 466, or GENETICS 467.

ONC675 – Mining genomics data

The availability of current publicly available genomics and bioinformatics tools expands opportunities for in silico approaches towards hypothesis testing and hypothesis-generating biological research. While reading background literature is still necessary for forming the right questions to ask, we are developing a course to introduce students to resources and analysis skill sets needed to mine publicly available datasets. With an appreciation of the individual programming and bioinformatics capabilities of each student, the instructors will assemble small groups of students to understand: 1) what are the questions that can be addressed based on the available data, 2) where/how to find the most appropriate publicly available data; and 3) what basic analyses are needed to mine the genomic data. This class targets graduate students who use or plan to use genomic data in their research. Participants will either come up with their own biological questions or be assigned them by the instructors. Students will need to work/meet weekly as a group to develop the analysis pipeline for their project under the supervision of the instructors. Finally, students will be evaluated by presenting their contribution to their group project and feedback on other presentations.

Offered every Fall, 3 credit. Instructor: Huy Dinh.

Pre-requisite: Graduate student standing; students with no or little coding experience will need to meet the instructors to plan on learning coding basis 2 weeks before the course, depending on their needs.

NUTR SCI 711 – Personalized Nutrition: Genetics, Genomics, and Metagenomics 

Genetic factors that modulate the relationships between diet, health, and disease risks, including the effects of differences in our genetic makeup (Nutrigenetics), the regulation of gene expression by nutrients and dietary patterns (Nutrigenomics), and the interactions between diet, gut microbiome, and human hosts (Metagenomics). 

Summer, 1 credit. Instructor: Nathan Johnson 

Pre-requisites: Declared in Clinical Nutrition MS or the Capstone Certificate in Clinical Nutrition 

CHEM 725 – Separations in Chemical Analysis

Basic principles of chemical and biochemical separations by chromatography and electrophoresis.

Offered every other Fall, 2-3 credits. Instructor: Lloyd Smith.

BMI775 – Computational Network Biology

This course aims to provide students an introduction to different computational problems that arise in the biological networks, key algorithms to solve these problems, and in-depth case studies showing practical applications of these concepts.

Offered in Fall, 3 credits. Instructors: Sushmita Roy and Anthony Gitter.

Pre-requisite: Some experience with programming, computer science algorithms, and probability. Students can email the instructors about questions and concerns about their background.

BME 780 – Methods in Quantitative Biology 

Focuses on understanding the key methods and principles of quantitative biology through a close reading of the primary literature. Topics covered will include deterministic and stochastic methods for modeling cellular systems, techniques in systems and synthetic biology, image processing tools and image analysis for biology, data-driven network models, genomic approaches, single-molecule approaches, and key computational biology tools. This course is intended for graduate students from a variety of backgrounds who are interested in pursuing quantitative biology during their graduate studies. 

1 credit. Instructors: Pam Kreeger; Megan McClean.

Pre-requisites: Graduate/professional standing.

BMI 826 – Statistics in Human Genetics

This course provides a comprehensive survey on the statistical and computational methods frequently used in human genetics and genomics research.

Offered every Spring, 3 credits. Instructor: Qiongshi Lu

BMI 826-012 – Animal Experiments and Alternatives

Non-human animal experiments are typically motivated by their ability to contribute substantially to our understanding of the biological mechanisms underlying human health and disease. Nevertheless, results from non-human animal experiments are often limited in their translational value due in part to fundamental differences in physiology, poor experimental design, and/or lack of attention to hierarchical outcomes.

This course will examine what can and cannot be gained from different classes of non-human animal experiments. Alternatives to animal testing will also be discussed in detail. The course will provide students with an understanding of the assumptions imposed by different model systems and experimental designs as well as their impact on scientific inference. Students will also become familiar with alternatives to animal experiments and methods for designing optimal experiments given a particular set of scientific objectives.

This class is designed for graduate students interested in experimental design, analysis, and/or inference in non-human animal experiments. Those with a general interest in the use of animals in science are also welcome and encouraged to attend.

1 credit. Instructor: Christina Kendziorski

STAT/BMI 877 – Statistical Methods for Molecular Biology

Statistical and computational methods in statistical genomics for human and experimental populations. Methods for quality control, experimental design, clustering, network analysis, and other downstream analysis of next-generation sequencing studies will be reviewed along with methods for genome wide association studies.

Offered every other Spring, 3 credits. Instructors: Christina Kendziorski; Sunduz Keles; Qiongshi Lu; Colin Dewey; Sushmita Roy; Michael Newton; Tony Gitter; Daifeng Wang; Karl Broman; Zhengzheng Tang; Huy Dinh

Pre-requisites: Professional/graduate standing

Genetics 885 – Advanced Genomic and Proteomic Analysis

With the availability of genome sequences and high-throughput techniques, organismal physiology can now be examined on a global scale by monitoring the behavior of all genes, proteins, metabolites, and molecules as well as how those components interact to form a functioning cell. This course will present modern techniques in genomics, proteomics, and systems biology with particular focus on analyzing the data generated by these techniques.  Course material will cover genomic sequencing, comparative sequence analysis, phylogenomics, single-cell and bulk RNA sequencing, sequence motif discovery, high- throughput screens, techniques in mass spectrometry, and network analysis. The course is designed for biologists who are motivated to understand underlying computational and statistical methods, with the goal of analyzing their own or published datasets.  In addition to lecture time, the course includes a weekly computer lab where students get hands-on experience analyzing genomic and proteomic datasets. An introduction to R and Python is included in the labs. Students also conduct a semester-long computational project of their choice that uses multiple computational methods discussed in class.

Offered every other Fall, 3 credits. Class enrollment is limited to 20 students due to computer-lab space and is restricted to graduate students. Prerequisites include: Genetics 466 or equivalent, basic statistics knowledge, & experience with Excel or R or other programming language.

Enrollment requires consent of instructor – please email Nicole Perna ( or Audrey Gasch ( to enroll.

Medical Genetics 911 – Modern Clinical Genetics: How to Approach a Rapidly Changing Field 

Genetics and genomics are rapidly evolving fields. In modern clinical care settings, clinicians will be exposed to genetic and genomic data, including that brought by patients, and knowing how to read genetic and genomic data is increasingly necessary in clinical practice. Genetics and genomics in a clinical setting spans a wide range of topics including diagnosis and treatment of genetic diseases. Familiarity with clinical genetic analysis, and the genetic approaches used in basic science, helps medical students better understand genetic disease background. Learn how to bridge basic concepts of human genetics and clinical genetics (actual diseases). Emphases will include research into human genetic diseases, including designing genetic testing, using model organisms and/or cell culture systems, and the development of genetic testing technologies. 

Spring, 2 credits. Instructor: Akihiro Ikeda.

Pre-requisites: MED SC-M 810, 811, 812, and 813.

CHEM 923 – Genomics Sciences Program Seminar

The Genomic Sciences Training Program (GSTP) seminar course beings together trainees in GSTP, faculty trainers, and other interested faculty and students for cross-disciplinary exposure to current research related to genomic sciences. The seminars will be given by predoctoral and postdoctoral trainees in GSTP. Other presentations will be by scientists conducting cutting-edge research probing genomics.

Offered every Spring and Fall, 1 credit. Instructor: David C. Schwartz

Pre-requisites: GSTP trainee, or interested graduate student in the sciences, or engineering

PATH 970 – Genomics, Proteomics, and Metabolomics: A Deep Dive into Omics Data Analysis 

Advances in medicine are increasingly being driven by “big data” analyses, including proteomics, genomics, and metabolomics. Basic knowledge of how to analyze these datasets can allow one to generate and test hypotheses that have the potential to transform a field. In this course, students will conduct individual data mining expeditions using a collection of large proteomics and metabolomics data sets. Formulate hypotheses about the interrelationships of molecules and their potential relationship to health, disease, and biological phenotypes. Basic background instruction on “omics” methodologies, heritability studies, and analytical methods will be provided. Provides the basic knowledge to carry out future ‘omics analyses; using scientific inquiry to potentially transform the practice of medicine. 

Spring, 2 credits. Instructor: Thomas Raife.

Pre-requisite: MED SC-M 810, 811, 812 and 813; or Declared in Cellular and Molecular Pathology Graduate Program.