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Dr. Debarka Sengupta, Instructor - Introduction to Genome Mapping and Understanding with Data Science

Dr. Debarka Sengupta

Debarka earned his Ph.D. in Computer Science and Engineering, with specialization in machine learning and computational biology. He carried out his doctoral research in the Machine Intelligence Unit of Indian Statistical Institute, Kolkata. He completed his postdoc research at the prestigious, Genome Institute of Singapore. Debarka has advised a number of analytics companies on various machine learning and artificial intelligence related projects. His research articles have been published by some of the renowned publishers, including Nature, Science, IEEE, Oxford and Royal Society of Chemistry. Currently, Debarka is an Assistant Professor of Computer Science and Computational Biology at Indraprastha Institute

Instructor: Dr. Debarka Sengupta

An introduction to genomics and how it is combining biology, mathematics, and computer science.

  • Understand cell and molecular biology primer.
  • You will gain an introduction to dynamic programming.
  • Instructor has a Ph.D. in Computer Science and Engineering, with specialization in Machine Learning and Computational Biology.

Duration: 4h 20m

Course Description

Genomics is a highly interdisciplinary field that cuts across biology, mathematics and computer science. Anyone, wanting to be introduced to the field of genomics would benefit from this course. The course discusses the foundation of molecular biology and the basic computational challenges involved in dealing with genome-scale sequencing data.

What am I going to get from this course?

  • Cell and molecular biology primer
  • Genome assembly
  • Introduction to dynamic programming
  • Sequence alignment
  • Utility of genomic data

Prerequisites and Target Audience

What will students need to know or do before starting this course?

The course does not have any significant pre-requisites. However, one, having acquaintance with computational programming would find the course easy to connect.

Who should take this course? Who should not?

Anyone interested in the subject and having acquaintance with computational programming.


Module 1: Cell & Molecular Biology Primer

Lecture 1 Introduction
Lecture 2 Size of Genome
Lecture 3 Structure of a Gene
Lecture 4 Protein
Lecture 5 Epigenomics
Lecture 6 DNA as Lego
Lecture 7 Sequencing
Lecture 8 Sequence of Photos

Module 2: Genome Assembly

Lecture 9 Jigsaw Puzzle

Module 3: Introduction to Dynamic Programming

Lecture 10 Fibonacci
Lecture 11 Shortest Path in Multi Stage Graph
Lecture 12 0/1 Knapsack
Lecture 13 An Instance
Lecture 14 Tracking the Solution
Lecture 15 Edit Distance
Lecture 16 Recurrence General
Lecture 17 Recursive Function
Lecture 18 Instance
Lecture 19 Instance -2
Lecture 20 Global Alignment
Lecture 21 Edit Distance
Lecture 22 Check
Lecture 23 Penalty Matrix - Increased Trivial Complexity
Lecture 24 You Can Pose It as a Maximization Problem
Lecture 25 Recursion

Module 4: Foundation of Modern Alignment

Lecture 26 Rational Behind Local Alignment
Lecture 27 Refresh Your NW Memory With the Following Example
Lecture 28 Maximum Contiguous Subsequence Sum
Lecture 29 Smith Waterman: A Change Insanely Simple
Lecture 30 Burrows Wheeler Transformation
Lecture 31 Burrows Wheeler Transformation - 2
Lecture 32 Example From The Original Article
Lecture 33 Another Example
Lecture 34 Reverse

Module 5: Utility of Genomic Data

Lecture 35 Two Prevalent Data Types
Lecture 36 Variant Calling
Lecture 37 Genome Wide Association Studies
Lecture 38 Expression Data Analysis
Lecture 39 Count Data & Phenotype
Lecture 40 Quantile Normalisation