Overview
Welcome to DH 607 - Introduction to computational multi-omics! The course focuses on diving deep into technologies that have been developed in the last couple of decades to understand what goes inside a cell.
Course information
Units: 6
Lecture: Mondays, Tuesdays and Thursdays, 5:05pm – 6:00pm.
Location: LT-005
Instructor: Saket Choudhary | Homepage | Lab | Blog
Office: B-22, KCDH, KReSIT Basement
Office Hours: Wednesdays, 5:00 - 6:00pm or by appointment
For appointments outside office hours: https://calendar.app.google/qMMkAKHS1WN67MNm7
Contact: [email protected] | Ext: 3785 (+91 22 2159 3785)
Schedule
Classes are on Monday, Tuesday and Thursday, 5:05pm – 6:00pm. Topics below are tentative and will firm up as the term progresses. Days marked NO CLASS do not meet. On a narrow screen the table scrolls sideways.
| Week | Date | Topic | Notes |
|---|---|---|---|
| 1 | Mon, Jul 27 | NO CLASS | Saket travelling |
| 1 | Tue, Jul 28 | Lecture 01: Introduction to the course (Video) | Extended class till 6:30pm |
| 1 | Thu, Jul 30 | Lecture 02A: A short introduction to molecular biology (Video) | Extended class till 6:30pm |
| 2 | Mon, Aug 03 | Lecture 02B: Sequences: Probabilities and Statistics (Video) | |
| 2 | Tue, Aug 04 | Lecture 02C: Sequences: Probabilities and Statistics (Video) | |
| 2 | Thu, Aug 06 | Hands on | Please bring your laptops. R/RStudio installation instructions. R basics: download .qmd | Colab notebook |
| 3 | Mon, Aug 10 | Lecture 03A: Sequence statistics and alignment (Video) | |
| 3 | Tue, Aug 11 | Lecture 03B: Sequence statistics and alignment (Video) | |
| 3 | Thu, Aug 13 | Lecture 03C: Sequences: Global Alignment (Video) + Hands on | Colab notebook | Clustal Omega | alignmyseq | ALB_Sequence.fna. Readings: Needleman and Wunsch (1970) | Smith and Waterman (1981) |
| 4 | Sun, Aug 16 | Assignment 1 released | Via Gradescope |
| 4 | Mon, Aug 17 | Lecture 04A: Sequences: Local Alignment and Database Search (Video) | Readings: Karlin-Altschul statistics |
| 4 | Tue, Aug 18 | Sequencing 3.0 | |
| 4 | Thu, Aug 20 | Sequencing 3.0 + Hands on | FASTQ file | Commands |
| 4 | Sun, Aug 23 | Assignment 1 due, 11:59pm | Via Gradescope |
| 5 | Mon, Aug 24 | Third Gen Sequencing | |
| 5 | Tue, Aug 25 | DNA mapping algorithms I | |
| 5 | Thu, Aug 27 | DNA-seq Hands On | Colab notebook |
| 6 | Mon, Aug 31 | DNA mapping algorithms II | |
| 6 | Tue, Sep 01 | Introduction to RNA sequencing | |
| 6 | Thu, Sep 03 | RNA-seq: Quantification Hands on | Drive folder | Kallisto/BowTie2 alignment |
| 7 | Mon, Sep 07 | RNA-seq: Quantification | |
| 7 | Tue, Sep 08 | Lecture 07B: RNA-Seq EM | |
| 7 | Thu, Sep 10 | Lecture 07C: RNA-seq EM II and MidSem review | |
| 8 | Mon, Sep 14 | NO CLASS | Institute holiday; mid-sem week |
| 8 | Tue, Sep 15 | NO CLASS | Mid-sem week |
| 8 | Thu, Sep 17 | NO CLASS | Mid-sem week |
| 8 | Sat, Sep 19 | MID-SEM EXAM, 2:00pm – 4:00pm | Venue: LT-002. Closed book and offline (no collaboration) |
| 9 | Mon, Sep 21 | NO class | |
| 9 | Tue, Sep 22 | RNA-seq: Normalisation I | |
| 9 | Thu, Sep 24 | RNA-seq: Normalisation II | |
| 10 | Mon, Sep 28 | Lecture 10A: RNA-seq: Dimensionality reduction | |
| 10 | Tue, Sep 29 | NO CLASS | Mid-sem |
| 10 | Thu, Oct 01 | RNA-seq: Hypothesis testing | |
| 11 | Mon, Oct 05 | Single-cell RNA-sequencing I | |
| 11 | Tue, Oct 06 | Single-cell RNA-sequencing II | |
| 11 | Thu, Oct 08 | RNA-seq Hands on | |
| 12 | Mon, Oct 12 | Sequencing the epigenome I | |
| 12 | Tue, Oct 13 | Sequencing the epigenome II | |
| 12 | Thu, Oct 15 | scRNA-seq Hands on | |
| 13 | Mon, Oct 19 | Calling variants and peaks | |
| 13 | Tue, Oct 20 | NO CLASS | Institute holiday |
| 13 | Thu, Oct 22 | Finding genes | |
| 14 | Mon, Oct 26 | Spatial transcriptomics I | |
| 14 | Tue, Oct 27 | Spatial transcriptomics II | |
| 14 | Thu, Oct 29 | Hands on | |
| 15 | Mon, Nov 02 | Perturbation assays | |
| 15 | Tue, Nov 03 | Genome wide association studies | |
| 15 | Thu, Nov 05 | Course review |
Evaluation
The course will be evaluated based on the following components:
- Assignments: 20% (Best n-1 out of n)
- Due 1 week after posting via Gradescope
- Weightage: 20/(n-1)% each
- Late submission policy: 10% penalty per day upto a maximum of 3 days
- One submission per student (Attribute if you discussed with someone or used LLMs)
- Mid-sem: 25%
- Closed book and offline (no collaboration)
- End-sem: 25%
- Closed book and offline (no collaboration)
- Course project: 20%
- Groups of maximum of 3 members
- Topics: Late August
- Surprise Quizzes: 10%
- Attendance/DX Policy: Each missed class results in -3 % from total score; 3 or more missed classes: DX
Collaboration policy
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.
Mid-semester and end-semester exams will be closed-book. No collaboration is allowed.
LLM Policy
You are allowed to use Large Language Models (LLMs) like ChatGPT, Claude, etc. as learning aids, but you must:
- Clearly document when and how you used an LLM in your submission
- Ensure you understand the solutions provided by the LLM
- Be prepared to explain your work during office hours or exams
- Not rely solely on LLM-generated code without understanding
For exams, LLMs will not be permitted.
Teaching Assistants
- Khyati Joshi
- Contact: [email protected]
- Saradha T
- Contact: [email protected]
- Ravikant Visvakarma
- Contact: [email protected]
About the course
We will take a first-principles approach to understanding biology and the technological advances that make it possible to understand how we can ‘measure’ biological processes quantitatively.
The course will build your foundational knowledge and skills necessary to explore and analyze complex (and real!) biological data. We will take multiple vignettes of biological questions and see how the fields of probability and statistics and computer science provide us with enlightening answers.
The course is neither a course in biology nor a course in statistics but somewhere in between.
What will you learn?
One of the key objectives of (modern) biology is to understand how different molecules shape the functionality of a cell/tissue/organ. The human body has 36 trillion (\(36 \times 10^{12}\)) cells in the human body. All the cells in our body typically have the same DNA but they have different functions.
If we had to draw a functional map of the human body, we would need to measure what goes inside each cell. Over the years, significant technological advancements have made it possible to ‘profile’ the various molecules in each cell (DNA/RNA/Protein). This data is high-dimensional. Scales can vary, but a typical modern-day dataset can have thousands of features (DNA sequence/gene expression/ proteins) measured over millions of rows (observations). To draw any biological insights, this high-dimensional data requires ‘processing’. The course will focus on mathematical and statistical methods that are required to analyze a modern day genomics/transcriptomics dataset.
A detailed set of topics will be available here. But overall, you will learn about:
- What is sequencing
- Algorithms for aligning DNA sequences and searching DNA databases
- Statistical methods to discover genome variation, and application to discovering etiology of disease
- Probability and statistics for sequence analysis
- How is gene expression quantified computationally
- Statistical models for analyzing gene expression data
- Linear and non-linear dimensionality reduction methods and their applications in multiomics
- Statistical models for identifying transcription factor binding
- Hidden markov models and applications in multiomics
- How are disease-causing mutations identified (genome wide association studies)
- Statistical modelling of single-cell multomics data
- Statistical models for modeling CRISPR screens
- Recent advancements in statistical methods and deep learning applications in multiomics for human diseases
Learning objectives
How to perform exploratory data analysis and visualize genomic data
Apply tailored statistical methods to answer questions using high dimensional biological data
“Getting your hands dirty” by analyzing genomics data to draw actionable insights for improving human health
Write production level, reproducible, reusable code and software packages
About the instructor
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. Saket Lab develops novel statistical and computational methods to answer fundamental questions in disease biology and public health.
Previous editions
Both archives include links to the in-class recordings.