GCB students are required to take 7 “core” courses. These requirements will help achieve a more uniform core knowledge among GCB students. The courses are:
- GCB 533: Statistics for Genomics and Biomedical Informatics
- GCB 534: Experimental Genome Science
- GCB 536: Fundamentals of Computational Biology
- Machine Learning course: CIS 519: Applied Machine Learning or CIS 520: Machine Learning
- Core Biology course: BIOM 555: Regulation of the Genome or CAMB 550: Genetic Principles
- One course from the list of Approach electives
- One course from the list of Biological Specialty electives
The remaining courses will consist of electives of the student’s choice for a total of 10 courses (6 for combined degree students). In Year 1, students will be expected to take 3 courses per semester (plus rotations). In Year 2 that number is reduced, and students will be expected to take 2 courses per semester (plus pre-dissertation research).
Lab Rotations (GCB 699)
Because it is essential that candidates have a firm training in biology and experimental techniques, a crucial component of the GCB curriculum is research rotations in the laboratories of GCB-affiliated faculty. Students in this program are required to do three lab rotations as part of their training. The definition of a lab rotation is flexible and includes the possibility of rotations in a computer science lab (for example, the application of data mining techniques to biological information sources) or a course of directed reading and research in mathematics/statistics, but students should expect to spend at least 25 hours per week in their rotation lab. At least one rotation must be a wet-lab project, and one must be computational.
For PhD students, each rotation lasts 11 weeks, with the first rotation beginning towards the end of September, the second rotation beginning during the first week of January, and the third rotation beginning in late March and running until mid-June.
The dissertation laboratory is usually chosen from one (or more) of these rotation labs, although this is not required. To ensure breadth of the training experience, all laboratory assignments must be approved in advance by the GCB Chair or the Chair of the Advising Committee.
Pre-dissertation Research (GCB 899)
Once the student has identified a thesis lab, generally during their first summer and no later than the end of their third semester, they begin graded lab work in their chosen dissertation laboratory. These lab projects serve as a foundation to the more formal dissertation research that follows the Candidacy Exam.
|Year 1||GCB 533
Lab Rotation 1
Lab Rotations 2 and 3
|Year 2||CIS 520
|Year 3 & beyond||Dissertation||Dissertation||Dissertation|
- BMIN 520: Fundamentals of Artificial Intelligence (Spring)
- BSTA 787: Statistical Methods and Data Analysis (Fall)
- CIS 545: Big Data Analytics (Fall)
- STAT 431: Statistical Inference (Fall/Spring)
- STAT 500: Applied Regression and Analysis of Variance (Fall)
- STAT 927: Bayesian Statistics (Spring, even years)
Biological Specialty Courses
- BIOL 522: Human Evolutionary Genomics (Spring, even years)
- BIOL 483: Epigenetics (Fall)
- BIOL 485: The RNA World: A Functional and Computational Analysis (not offered every year)
- GCB 577: Advanced Epigenetics Technology (Spring)
- GCB 585: Wistar Cancer Biology: Signaling Pathways in Cancer (Fall)
- GCB 752: Seminar in Genomics (Spring)
This list is not exhaustive, and additional qualifying courses may be approved by the Advising Committee and GCB Chair. Students can visit the University Catalog to view available courses.
Descriptions of GCB Courses
GCB533 is an introductory course in probability theory and statistical inference for graduate students in Genomics and Computational Biology. The goal of the course is to provide a foundation of basic concepts and tools as well as hands-on practice in their application to problems in genomics.
Syllabus - Fall 2020
This course covers fundamentals of algorithms, statistics, and mathematics as applied to biological problems. In particular, emphasis will be given to biological problem modeling and understanding the algorithms and mathematical procedures at the "pencil and paper" level. That is, practical implementation of the algorithms is not taught but principles of the algorithms are covered using small sized examples.
Syllabus - Fall 2020
The goals of this course include to introduce the basic principles involved in sequencing genomes; familiarize students with new instrumentation, informative tools, and laboratory automation technologies related to genomics; teach students how to access the information and biological materials that are being developed in genomics; and examine how these tools and resources are being applied to basic and translational research. This course is not required for GCB students, but highly recommended.
Syllabus - Spring 2021
This course provides broad overview of bioinformatics and computational biology as applied to biomedical research. A primary objective of the course is to enable students to integrate modern bioinformatics tools into their research activities. This course is not taken by GCB students, but by students in other graduate programs who wish to be introduced to Python, R, and tools for reproducible research.
Syllabus - Spring 2021
GCB 577 intends to cover the latest advances in genome-wide epigenetic assays (e.g. single-cell epigenomics) from both experimental and computational perspectives.
This course is an elective option for GCB students and may be of interest to other students in BGS.
Syllabus from Spring 2020
*course not being offered in Spring 2021