After the main genetics and genomics lectures, the Genetics & Genomics class has 6 sessions dedicated to hands-on bioinformatics analyses in Python. They will be split as follows:
- Two sessions (4h each) dedicated to an Introduction to Python and data analysis. And some hands-on exercises with a famous genetics example.
- Two sessions (4h each) dedicated to a Genetics project (graded)
- Two sessions (4h each) dedicated to a Genomics project (graded)
Python and R are the two main languages used for bioinformatics analyses. We chose Python over R in 2022, since other courses are also using Python (e.g., Machine Learning, ...). This makes it easier for the students to grasp the concepts of bioinformatics, while removing the burden of having to learn R and its syntax.
As described in the first lesson 01., in this course, we will use Python in Jupyter notebooks (a shorthand for Julia, Python and R). To get and use Jupyter notebooks, we recommend Anaconda distribution, which is available for the most common operational systems. To install Anaconda, check out this link. After installing, you can either open the Jupyter via the app, or execute the jupyter notebook command in the terminal to start a Jupyter server.
Then, you will need to install some packages for Python. Please check out lesson 03. to know how to proceed.
The supports are made with Jupyter Notebook, and are also listed on the main web page:
- 01.introduction_to_jupyter.ipynb -- contains short installation instructions for Anaconda and a short overview of the Jupyter Notebook interface.
- 02.a.extended_introduction_to_python.ipynb -- contains an extensive introduction into basic Python functionality. Is designed for students with no prior experience with Python.
- 02.b.quick_python_overview.ipynb -- contains an overview of basic Python functionality. Is useful for students who want to refresh their knowledge of Python syntax and functionality.
- 03.useful_python_packages_and_examples.ipynb -- contains an overview of the most frequently used packaged for data analysis with Iris dataset-based examples.