gangchen / i2p4b

Introduction to Programming for Bioinformatics, based on previous GNBF5010 Introduction to Programming course in Chinese University of Hong Kong

Introduction to Programming for Bioinformatics

Introduction to Programming for Bioinformatics using Python and R.

• Lecturer: Gang Chen ([email protected])
• Laptop is required for this courses
• Most softwares and packages in this courses are available on Windows, Linux and Mac OS. However, Linux and Mac OS are recommended for Bioinformatics.
• All slides of this courses are written in Markdown or LaTeX. You can check out source codes of these slides from this repository.

1. Computer Architecture and Programming

Pre-requests

Contents:

• Computer Architecture
• Computer Network

References

Assignments

References

• CS301: COMPUTER ARCHITECTURE

2. Python Getting Started

Contents:

• Overview of Python
• Setting Up Python Development Environment
• Hello Python
• Interactive and Script mode

References:

• A IPython Notebook for Introduction to Python also can be used as a reference for 3rd week.
• Python Courses on codecademy an interactive courses of Python for people who don't have any programming experiences.
• A byte of Python
• The Hitchhiker’s Guide to Python

3. Elements of Programming

Contents

• Data Type
• Variable
• List and map
• Flow Control: conditional statement and loop
• Function and module

References:

• Computational Complexity Theory from Stanford University
• Introduction to Algorithms, 2nd
• Algorithms, 4th

4. Object Oriented Programming

References:

See references of Lecture 2.

5. Data Structure and Algorithm

References:

See references of Lecture 2.

6. Web Programming and Database

Contents

• Web Development Overview
  • Overview
  • LAMP: Operation System, Web Server, Database, Programming language
  • LAMP: HTML, CSS, Javascript, Python, SQL
  • A static website
• CGI
• Connect CGI to Database
• Web Framework: Django
• A online sequence aligner

References:

• Django Documentations

7. Scientific Computing

Contents

• NumPy, SciPy, Pandas: Scientific computing
• Scikit-Learn, NLTK: Machine Learning and natural language processing
• Matplotlib: Data visualization in Python

References:

• Machine Learning Exercises In Python

Exercises in Python for Andrew Ng's Machine Learning courses.

• Building Machine Learning Systems with Python

8. Python in Bioinformatics

References:

• Bioinformatics Programming using Python

9. R Getting Started

References:

• R in Action, 2nd

10. R in Bioinformatics

Contents:

• BioConductor
• Workflow of RNA-Seq data analysis
• Visualization of Biological Data

11. Programming best practices I

References:

• The Code Complete 2nd

12. Programming best Practices II

13. Final Examination

References:

Review slides and assignments.