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
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
3 Aug 14, Thu
  1. Sequences: Local Alignment and Database Search
πŸ”– πŸ“šπŸ“š
4 Aug 18, Mon Guest Lecture
4 Aug 21, Thu
  1. Alignment Review & Sequencing by Synthesis
πŸ”– πŸ’» πŸ“š
5 Aug 25, Mon
  1. Sequencing 3.0
πŸ”– πŸ“šπŸ“š
5 Aug 28, Thu
  1. DNA mapping algorithms
πŸ”–
6 Sep 01, Mon
  1. Hands on DNA-seq
πŸ”–

πŸ’»

πŸ’»

6 Sep 04, Thu
  1. DNA mapping algorithms II
πŸ”–
7 Sep 08, Mon
  1. RNA-sequencing
πŸ”– πŸ“š
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”–
9 Sep 25, Thu
  1. RNA-seq: Normalisation
πŸ”– πŸ’»
10 Sep 29, Mon MidSem Discussion
10 Oct 02, Thu Institute Holiday
11 Oct 06, Mon
  1. RNA-seq: Hands ON
πŸ”– πŸ’»
11 Oct 09, Thu
  1. Dimensionality Reduction
πŸ”–
12 Oct 13, Mon
  1. Hypothesis testing
πŸ”–
12 Oct 16, Thu
  1. scRNA-seq &HandsOn
13 Oct 20, Mon Institute Holiday
13 Oct 23, Thu
  1. Genome wide association studies
14 Oct 27, Mon
  1. scRNA-seq HandsOn II
14 Oct 30, Thu
  1. Spatial transcriptomics
15 Nov 03, Mon
  1. Spatial transcriptomics: Hands-On
15 Nov 06, Thu
  1. β€˜CRISPRing’ the genome
16 Nov 10, Mon
  1. Course Review

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