Pre-requisites
Students must have previously completed Genetic Analysis I (01:447:384) or Genetics (01:447:380), and Statistics I and II (01:960:211-212).
Course Restrictions
This course is limited to Genetics majors (and graduate students in a number of departments). Other students can be added by special permission number pending computer space availability.
Course Description
The focus of this course is the application of R programming (with independent verification by other methods) to the analysis of genetic data, particularly “big data” sets with multiple measurements. The primary data sets will contain multiple Single-Nucleotide-Polymorphism genotypes, DNA methylation, conservation, eQTLs, RNA-sequence and/or other data for multiple/all genes in a set of individuals.
Computational analysis will be performed on such data, with the goal of determining whether certain genes are causal for given phenotypes.
This course is for junior or senior students who are thinking of careers relating to life sciences, statistics, and/or computer science. Specifically, this course is for students who major in Genetics. The course fulfills the laboratory requirement for the Genetics major.
Students will learn how to acquire such data, format it for R, plot the data, and perform computational analyses. In addition, students will learn how to simulate data under different hypotheses, and how to perform power and sample size calculations for different statistical methods applied to real or simulated data.
Each class consists of a mixture of lecture and computer-based demos and/or exercises. There will be time for students to work on assignments. Guest investigators will make short presentations (in person or virtually) to provide illustrations of how programming and informatics is critical for their research. The course provides the introductory skills needed to conduct basic computational research in the life sciences, including many aspects of computer programming and data analysis.
Course Goals
The goals of Honors Computational Genetics reflect the learning goals of the Department of Genetics, and include:
- Knowledge specific goals: know the terms, concepts, and theories in genetics.
- Integrate the material from multiple courses and research.
- Participate in Discussion Forums with other students. These forums are based on current events in genetics. Students will write scientific opinions and respond to other students’ opinions.
Specific itemized goals include:
- Learning R programming, specifically methods for acquisition and analysis of big data from genomics repositories.
- Discovering online repositories for genomic data sets.
- Learning the fundamentals of statistical analysis for such data sets.
- Performing empirical type I and power evaluations for different statistics applied to expression data by writing R programs that can simulate data with mathematical models; and,
- Determining the fundamentals of experimental design for expression-data statistics.
Core Curriculum Learning Goals Met by this Course: Info Tech & Research [ITR]
- Goal Y: Employ current technologies to access information, to conduct research, and to communicate findings.
- Goal Z: Analyze and critically assess information from traditional and emergent technologies.
Course Materials
The computer lab has Windows computers. Class materials and files may be copied after each class to a portable USB flash drive (Windows formatted) to continue working at home. No textbook is required as most of the needed material is made available during class. However, there will be journal articles that will be provided for discussion forums.
A useful resource to have on hand if you prefer to have a printed book is:
R Cookbook, 2nd Edition, Authors: James Long and Paul Teetor
https://rc2e.com/
Exams, Assignments, and Grading Policy
Attendance is expected at all classes; in-class demos and exercises are an integral part of this class and it is difficult to make-up work when class is missed. If a student must miss a class, please use the University absence reporting website to indicate the date and reason for your absence. An email is automatically sent to the instructors. Completion of all assignments is required, including any that may have been missed due to absence in class.
Students will be assigned weekly projects based on current material. The final grade is based on the grades received on these projects, quizzes, and a final exam.
Course Closed?
If this course is closed, please use the following link to add your name to the wait list: Wait List Sign Up . If you have any questions, please contact the Department of Genetics Undergraduate Education Office in Nelson Biological Laboratories Room B416 or call 848-445-1146.
Faculty:
Dr. Derek Gordon
Office: B-414, Nelson Biological Labs, 604 Allison Road, Busch Campus
** All information is subject to change at the discretion of the course coordinator.
