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

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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

2. Python Getting Started

Contents:

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

References:

3. Elements of Programming

Contents

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

References:

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:

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.

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