Welcome to comp-biol-3000788

This is the repository for the learning materials from the 3000788: Introduction to Computational Molecular Biology course taught by our group at the Faculty of Medicine, Chulalongkorn University in Bangkok, Thailand.

Announcements

Current students can check their assignment score in the score table!

*NEW* Follow this year's lecture recordings on the 2025 YouTube Playlist.

*NEW* Lectures will be held in Room 1216, 12th Floor, Bhusiri Mangkhalanusorn Building

Current year (Fall 2025)'s schedule and assignments

Schedule and location

Monday and Wednesday at 10:00-11:30am in Room 1216, 12th Floor, Bhusiri Mangkhalanusorn Building.

Assignment

Assignment 1 Due: 3 Sep 2025, 11:59pm

Assignment 2 Due: 17 Sep 2025, 11:59pm

Assignment 3 Due: 6 Oct 2025, 11:59pm

E-mail to sira.sr [at] chula.ac.th

Past years' materials

English video contents recorded in Fall 2022 can be found here.

Thai video contents recorded in Fall 2023 can be found here.

Here is a guide of the keywords and key concepts to look out for while watching past videos.

Fall 2024's slides are not provided because they are shortern versions of Fall 2023's contents.

Past course structure

Module 0: Statistics and Computational Thinking

Introduction to the computational biology, review of probability and statistics, and computational thinking

Module 1: DNA Sequencing Analyses and Applications

DNA sequencing technology, basic sequencing data processing, sequence alignment, BLAST, phylogenetics and molecular evolution, and metagenomics

Module 2: Transcriptomics

Oligonucleotide microarray, Nanostring, RNA-sequencing, functional enrichment, and single-cell analysis

Check out the microarray and Nanostring analysis solutions and discussion for key Excel commands.

Check out the discussion on basic computer knowledge and kallisto commands and sleuth R commands.

Check out the discussion on functional enrichment analysis web tools.

Module 3: Other Omics and Advanced Topics

Proteomics, mass spectrometry, biological networks, Cytoscape, chromatin organization, gene expression dynamics simulation for systems biology, and useful online databases and tools

Module 4: Programming and Machine Learning with Python

Introduction to Python programming, Python data structure (pandas), visualization (matplotlib and seaborn), statistical analyses (scipy), principles of unsupervised and supervised machine learning, and a touch of deep learning in life sciences