Introduction to Programming for Bioinformatics

Introduction to Programming for Bioinformatics using Python and R.

• Lecturer: Gang Chen (chengangcs@gmail.com)
• 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.