Syllabus

IITB’s academic calendar is here and holiday calendar is here.

A playlist of in-class recordings is here.

Online Interactive Tools

There are a couple of interactive web tools to help understand key concepts covered in this course.

  1. BWT Visualisation:

  1. Sequence alignment:

Week Day Topic Slides Hands-on Readings Video
1 Jul 28, Mon
  1. Introduction to the course
πŸ”– πŸ“š πŸ“š πŸ“š
1 Jul 31, Thu
  1. A short introduction to molecular biology
πŸ”– πŸ’» πŸ“šπŸ“šπŸ“šπŸ“š
2 Aug 04, Mon
  1. Sequences: Probability & Statistics
πŸ”–
2 Aug 07, Thu
  1. Sequences: Statistics & Alignment
πŸ”–
3 Aug 11, Mon
  1. Sequences: Global Alignment
πŸ”– πŸ“š πŸŽ₯
3 Aug 14, Thu
  1. Sequences: Local Alignment and Database Search
πŸ”– πŸ“šπŸ“š πŸŽ₯
4 Aug 18, Mon Guest Lecture
4 Aug 21, Thu
  1. Alignment Review & Sequencing by Synthesis
πŸ”– πŸ’» πŸ“š πŸŽ₯
5 Aug 25, Mon
  1. Sequencing 3.0
πŸ”– πŸ“šπŸ“š πŸŽ₯
5 Aug 28, Thu
  1. DNA mapping algorithms
πŸ”– πŸŽ₯
6 Sep 01, Mon
  1. Hands on DNA-seq
πŸ”–

πŸ’»

πŸ’»

πŸŽ₯
6 Sep 04, Thu
  1. DNA mapping algorithms II
πŸ”–
7 Sep 08, Mon
  1. RNA-sequencing
πŸ”– πŸ“š πŸŽ₯
7 Sep 11, Thu
  1. RNA-seq: HandsOn
πŸ”– πŸ’»
8 Sep 15, Mon

MidSem Exam (Closed book/notes)

6:30 PM - 8:30PM

LC101

Venue map

8 Sep 18, Thu Midsem Week πŸ”–
9 Sep 22, Mon
  1. RNA-seq: Quantification
πŸ”– πŸŽ₯
9 Sep 25, Thu
  1. RNA-seq: Normalisation
πŸ”– πŸ’» πŸŽ₯
10 Sep 29, Mon MidSem Discussion
10 Oct 02, Thu Institute Holiday
11 Oct 06, Mon
  1. RNA-seq: Hands ON
πŸ”– πŸ’» πŸŽ₯
11 Oct 09, Thu
  1. Dimensionality Reduction
πŸ”– πŸŽ₯
12 Oct 13, Mon
  1. Hypothesis testing
πŸ”– πŸ’» πŸŽ₯
12 Oct 16, Thu
  1. Single-cell RNA-sequencing
πŸ”– πŸŽ₯
13 Oct 20, Mon Institute Holiday
13 Oct 23, Thu
  1. scRNA-seq II
πŸ”– πŸ’»
14 Oct 27, Mon
  1. scRNA-seq HandsOn
πŸ’» πŸŽ₯
14 Oct 30, Thu
  1. Genome wide association studies
πŸ”–
15 Nov 03, Mon
  1. Sequencing the epigenome
πŸ”–
15 Nov 05, Wed
  1. Course Review
πŸ”– πŸŽ₯

Suggested readings

  1. Molecular Biology for Computer Scientists by Lawerence Hunter
  2. Genomes by TA Brown
  3. Computational Genome Analysis by Richard C. Deonier , Michael S. Waterman , and Simon TavarΓ©
  4. Bioinformatics Algorithms by Phillip Compeau and Pavel A. Pevzner
  5. Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber
  6. Data Analysis for the Life Sciences by Rafael A Irizarry and Michael I Love