PhD Curriculum - Genomics and Algorithms Track
GCB students in the Genomics and Algorithms track are required to take 7 core courses or an approved alternative depending on the student's background. These requirements will help achieve a more uniform core knowledge among GCB students. The remaining courses will consist of electives of the student's choice for a total of 10 courses. Courses include:
- GCB 5340: Experimental Genome Science
- 1 CU of Fundamentals in Statistics
- 1 CU of Fundamentals in Computation
- 1 CU of Core Machine Learning
- 1 CU of Core Biology
- 1 CU from the list of Approach electives
- 1 CU from the list of Biological Specialty electives
- 3 CUs of additional electives
Example Schedule
| Fall | Spring | Summer | |
|---|---|---|---|
| Year 1 | GCB 5330 GCB 5340 GCB 5360 Lab Rotation 1 |
BIOM 5550 Approach Elective Elective Lab Rotations 2 and 3 |
Pre-dissertation Research |
| Year 2 | CIS 5200 Biological Elective Pre-dissertation Research |
Elective Elective Pre-dissertation Research Candidacy Exam |
Dissertation |
| Year 3 & beyond | Dissertation | Dissertation | Dissertation |
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). Additional example schedules may be found here.
Fundamentals in Statistics
All GCB students are required to take a fundamental course in Statistics. Typically, this will be a probability theory class we developed for our students (GCB5330), but some students enter our program with the background to take more advanced Statistical training. Courses in this pool that satisfy this requirement are:
- GCB 5330: Statistics for Genomics and Biomedical Informatics
- STAT 5100: Probability Theory
- BSTA 6200: Probability I
Fundamentals in Computation
All GCB students are required to take a fundamental in course in Computational Biology, Algorithms, or programming. The selection of this course requirement is tailored to the specifics of the student, as matriculating students tend to emerge from diverse backgrounds where they may have had very little to substantial levels of algorithmic experiences. Courses in this pool that satisfy this requirement are:
- GCB 5360: Fundamentals of Computational Biology
- BIOL 5535: Introduction to Computational Biology & Biological Modeling
- CIS 5450: Big Data Analytics
- BIOL 5860: Math Modeling in Biology
- CIS 6770: Advanced Topics in Algorithms and Complexity
- CIS 5520: Advanced Programing
Machine learning and approaches that utilize Artificial Intelligence are increasingly being utilized to facilitate biological and clinical inferences on data sets of increasingly large sizes. Thus, we believe GCB students need to carry forward a fundamental understanding of this approach and its use in these contexts. Students may enter into the program substantial expertise from previous coursework; in this case, more advanced coursework in specific topic areas or advanced techniques can be taken instead. Courses in this pool that satisfy this requirement are:
- BMIN 5210: Advanced Methods and Health Applications in Machine Learning
- CIS 5190: Applied Machine Learning
- CIS 5200: Machine Learning
- CIS 5210: Artificial Intelligence
- CIS 5220: Deep Learning for Data Science
- CIS 6200: Advanced Topics in Deep Learning
- ESE 5460: Principles of Deep Learning
Additional alternatives may also satisfy this requirement, subject to Chair approval.
Foundational knowledge in either genetics, genomics, physiology, cell and molecular biology, or gene regulation is essential for the thesis work of GCB students. This foundational knowledge is critical to understand and discover new genetic and genomic mechanisms and create new avenues to potentially improve patient care, but also to interact with the community of molecular biologist and scientists on the strengths and weaknesses of computational experimental design decisions. Courses that satisfy this requirement include:
- BIOM 5550: Regulation of the Genome or CAMB 5500: Genetic Principles
- BIOM 6000: Cell Biology
- CAMB 5320: Human Physiology
- BIOM 5020: Molecular Basis of Disease
- BIOL 5240: Genetic Analysis
- BIOL 5210: Molecular Biology and Genetics
Additional alternatives may also satisfy this requirement, subject to Chair approval.
Lab Rotations (GCB 6990)
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 8990)
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.
Qualifying Approach and Biological Specialty Courses
Approach Courses
- Any Statistics Fundamentals or Core Machine Learning course
- BMIN 5020: Database and Data Integration in Biomedical Research
- BMIN 5200: Foundations of Artificial Intelligence in Health (Spring)
- BMIN 5210: Advanced Methods and Health Applications in Machine Learning
- BMIN 5220: Natural Language Processing for Health
- BSTA 7870: Methods for Statistical Genetics and Genomics in Complex Human Disease (Fall)
- CIS 5450: Big Data Analytics (Fall)
- GCB 5370: Advanced Computational Biology
- STAT 4310: Statistical Inference (Fall/Spring)
- STAT 5000: Applied Regression and Analysis of Variance (Fall)
- STAT 9270: Bayesian Statistics (Spring)
Biological Specialty Courses
- Any Core Biology course
- BBCB 5850: Wistar Cancer Biology: Signaling Pathways in Cancer (Fall)
- BMIN 5010: Introduction to Biomedical and Health Informatics
- BIOL 5220: Human Evolutionary Genomics (Spring, even years)
- CAMB 4830: Epigenetics (Fall)
- CAMB 4850: The RNA World: A Functional and Computational Analysis (not offered every year)
- CAMB 5100: Immunology for CAMB
- GCB 5770: Advanced Epigenetics Technology (Spring; not offered every year)
- GCB 7520: 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
GCB 5330 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 will survey methods and questions in experimental genomics, including next generation sequencing methods, genomic sequencing in humans and model organisms, functional genomics, proteomics, and applications of genomics methods.
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 5770 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