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.
GENETICS 335 – Genomes in a Modern World
The ability to sequence genomes rapidly has transformed our understanding of the evolution of species and human history, our approaches to improve health and medicine, as well as how we tackle global environmental challenges, and increasingly impacts our modern world. Explore the interdisciplinary connections between genetics- and genomics-based research and important current questions related to ethics, history, and public policy.
Fall & Spring, 3 credits.
Pre-requisites: GENETICS 234.
AN SCI 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.
GENETICS 375 – Quantitative Methods in Genetics
Specialized subject matter of current interest to undergraduate students.
Fall, 1-4 credits. Instructor: Steven Schrodi.
Pre-requisites: none.
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.
Fall, 3 credits. Instructors: Nicole Perna, Bret Payseur .
Pre-requisites: GENETICS 466, 468, or BIOCORE 587.
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, 468, BIOCORE 587, or graduate/professional standing.
STAT 620 – 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
BIOCHEM/GENETICS 631 – Plant Genetics and Development
Covers the basic concepts of genetics and genomics as applied to plants and their development, including discussions on breeding systems (modes of reproduction, sex determination, self incompatibility and crossing barriers), linkage analysis, genome structure and function (structure, function and evolution of nuclear and organellar chromosomes; haploidy and polyploidy; expression regulation and epigenetics), along with a description of current methodologies used in the analysis of these processes within the context of plant development. The objective is to instigate a broader knowledge and understanding of the principles and methodologies used in plant genetics and their applications in investigations of the molecular mechanisms that modulate plant development.
Fall, 3 credits. Instructors: Patrick Masson, Jake Brunkard.
Pre-requisites: GENETICS 466, 468, BIOCORE 587, or graduate/professional standing.
ONC 675 – 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.
BMI/COMP SCI 775 – 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: Megan McClean.
Pre-requisites: Graduate/professional standing.
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 (ntperna@wisc.edu) or Audrey Gasch (agasch@wisc.edu) to enroll.
MD GENET 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 – Genomic Sciences Program Seminar
Cross-disciplinary exposure to cutting edge research in genomic sciences. Seminars presented by trainees and other scientists who study genomics using approaches based in chemistry, computer science, biostatistics, engineering and biological and biomedical sciences. Research objectives, findings and future directions are discussed.
Offered every Spring and Fall, 1 credit. Instructor: Qiongshi Lu
Pre-requisites: Graduate/professional standing
ZOOLOGY 957 – Genome Architecture Evolution
Sections in various fields of zoological research.
Fall, 1 credit. Instructor: Carol Lee
Pre-requisites: Graduate/professional standing
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: Irene Ong.
Pre-requisite: MED SC-M 810, 811, 812 and 813; or Declared in Cellular and Molecular Pathology Graduate Program.