BIOPHYS 170 - Evolutionary and Quantitative Genomics
Course Head: Leonid Mirny
Aims to develop deep quantitative understanding of basic forces of evolution, molecular evolution, genetic variations and their dynamics in populations, genetics of complex phenotypes, and genome-wide association studies. Application of these foundational concepts to cutting edge studies in epigenetics, gene regulation and chromatin; cancer genomics, and microbiomes. Modules consist of lectures, journal club discussions of high impact publications, and guest lectures that provide clinical correlates. Homework assignments and final projects aim to develop hands-on experience and understanding of genomic data from evolutionary principles.
Prerequisite Knowledge: N/A
Credits: 4
Syllabus or Course Site: https://sites.google.com/view/hst508/home?authuser=0
Meeting Times: Mondays, Wednesdays, and Fridays 11:00 am - 12:30 pm
BMIF 201 - Concepts in Genome Analysis
Course Head: Shamil Sunyaev
This course focuses on quantitative aspects of genetics and genomics, including computational and statistical methods of genomic analysis. We will introduce basic concepts and discuss recent progress in population and evolutionary genetics and cover principles of statistical genetics of Mendelian and complex traits. We will then introduce current genomic technologies and key algorithms in computational biology and bioinformatics. We will discuss applications of these algorithms to genome annotation and analysis of epigenomics, cancer genomics and metagenomics data. Proficiency in programming and basic knowledge of genetics and statistics will be assumed.
Prerequisite Knowledge: N/A
Credits: 4
Syllabus or Course Site: Available upon request
Meeting Times: Mondays and Wednesdays, 2:30 pm - 4:00 pm
BST 227 - Introduction to Statistical Genetics
Course Head: M. Aryee
Chan School of Public Health, Dept. of Biostatistics
This course introduces students to the diverse statistical methods used throughout the process of statistical genetics. Topics covered include the basic molecular biology underpinnings of genetics, principles from population genetics, family-based and population-based association testing, genome wide association studies, expression QTL (eQTL) analysis and epigenome-wide association studies. Instructors use ongoing research to illustrate basic principles. Weekly homeworks supplement reading, course lectures, discussion and section. Relevant concepts in genetics and molecular genetics will be reviewed in lectures and labs. The emphasis of the course is fundamental principles and concepts.
Prerequisite Knowledge: BST 210 - Applied Regression Analysis
Syllabus or Course Site:
Credits: 2 – Must take concurrently with BST 262. Special permission required from program.
Meeting Times: Mondays and Wednesdays, 3:45 pm - 5:15 pm Fall 2
BST 262 - Computing for Big Data
Course Head: C. Choirat
Big data is everywhere, from Omics and Health Policy to Environmental Health. Every single aspect of the Health Sciences is being transformed. However, it is hard to navigate and critically assess tools and techniques in such a fast-moving big data panorama. In this course, we are going to give a critical presentation of theoretical approaches and software implementations of tools to collect, store and process data at scale. The goal is not just to learn recipes to manipulate big data but learn how to reason in terms of big data, from software design and tool selection to implementation, optimization and maintenance.
Prerequisite Knowledge: N/A
Syllabus or Course Site: Available upon request
Credits: 2 – Must take concurrently with BST 227. Special permission required from program.
BST 283 - Cancer Genome Data Science
Course Head: G. Parmigiani
This course is an introduction to modern statistical computing techniques used to characterize and interpret cancer genome sequencing datasets. This Master's level course will begin with a basic introduction to DNA, genes, and genomes for students with no biology background. It will then introduce cancer as an evolutionary process and review landmarks in the history of cancer genetics, and discuss the basics of sequencing technology and modern Next Generation Sequencing. The course will cover the main steps involved in turning billions of short sequencing reads into a representation of the somatic genetic alterations characterizing an individual patient’s cancer, and will build on this foundation to study topics related to identifying mutations under positive selection from multiple tumors sampled in a population. By the end of the course, students will be able to apply state-of-the art analysis to cancer genome datasets and to critically evaluate papers employing cancer genome data.
Prerequisite Knowledge: N/A
Credits: 4
Syllabus or Course Site: Pending
Meeting Times: Tuesdays and Thursdays, 9:45 am - 11:15 am
COMPSCI 107 - Systems Development for Computational Science
Course Head: D. Sondak
This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. Most important, students will learn to fit tools and approaches to the problem being solved.
Prerequisite Knowledge: Students are expected to have basic programming experience
(Computer Science 50).
Credits: 4
Syllabus or Course Site:
Meeting Times: Tuesdays and Thursdays, 12:00 pm - 1:15 pm