Welcome to DH 603 - Computational Multi-omics of Ageing! Through the class, we will explore how omics technologies, particularly transcriptomics and epigenomics have revolutionized our understanding of ageing. We will discuss key computational approaches to analyse gene expression and epigenetic modifications, uncovering mechanisms of ageing and longevity. Through real-world case studies and hands-on data analysis, students will learn how to leverage omics data to study ageing at a molecular level.
How to perform exploratory data analysis and visualize genomics/epigenomics data
“Getting your hands dirty” by analyzing genomics data to draw actionable insights for improving human health
First principles based approach to critically evaluate and interpret results from omics studies in ageing
The course will be evaluated based on assignments. Best (n-1) out of n assignments will be considered for grading. The problems will be a mix of theoretical and practical problems.
There is no end-semester exam for this course.
For assignment problems, you should work on your own. If you get stuck, you are welcome to discuss it with other students (in-person, or online on Piazza). However, the solutions must be your work. If you discussed with someone, please mention their name and what you received help with in your submission.
Units: 3
Lecture: Wednesdays and Fridays, 09:30am – 10:55am.
Location: LT 003, Ground Floor Lecture Hall Complex, L1 Building, Opp. KReSIT Bldg., Between Physics & MEMS Dept. GMaps coordinates
Instructor: Saket Choudhary | Homepage | Blog
Office: G-22, KCDH, KReSIT Basement
Office Hours: Wednesdays, 4:00 - 5:00pm or by appointment
For appointments outside office hours: https://cal.com/saketkc/
Contact: saketc@iitb.ac.in | Ext: 3785 (+91 22 2159 3785)
Saket is an Assistant Professor at the Koita Centre for Digital Health at IIT Bombay. His lab focuses on developing statistical models for analyzing multi-omics data. Saket obtained his B.Tech+M.Tech in Chemical Engineering at IIT Bombay in 2014. He pursued his Ph.D in Computational Biology and Bioinformatics at the University of Southern California developing computational methods for understanding how proteins are synthesized in the body. Until very recently, he was a postdoc at the New York Genome Center developing statistical methods for analyzing single-cell genomics data.