David B. Blumenthal

Introduction to Python for Bioinformatics

In this course, targeted at PhD students in the biomedical sciences, participants will receive an introduction to the programming language Python 3. We will start with the basics (I/O, basic data structures, loops and conditions, functions, classes) and then introduce widely used packages for data manipulation, scientific computing, and visualization (NumPy, SciPy, pandas, seaborn). Finally, the course will provide a first glimpse at two widely used packages for the analysis of biological data (Biopython, GSEAPY).

Getting Started

All course materials are provided as Jupyter Notebooks. To run Jupyter Notebooks on your machine, install an Anaconda distribution before the start of the course.

Now download the course material into a directory called python-intro. For this, either click on Code → Download ZIP, or download the course material via git:

git clone https://github.com/dbblumenthal/python-intro

After downloading the course material, navigate to the python-intro directory and create a conda environment called python-intro with all dependencies:

cd python-intro
conda env create -f environment.yml

You can now activate the environment and connect it to your Jupyter Notebook:

conda activate python-intro
(python-intro) python -m ipykernel install --user --name=python-intro

Now you can open your first notebook as follows:

(python-intro) jupyter notebook 1_first_steps.ipynb

If the Notebook opens without any errors, you are ready for the course.

Writing Python Scripts

The entire course is based on Jupyter Notebooks, but sometimes it is useful to write Python scripts that can be executed from a terminal. As an example, you can have a look at the script log_transform.py (just open it in any text editor). You can execute the script as follows:

(python-intro) python log_transform.py input/P53.txt output/P53_log_transformed.tsv --drop DESCRIPTION

This will save a log-transformed version of the data saved in input/P53.txt in the file output/P53_log_transformed.tsv , where the column DESCRIPTION contained in the input file is discarded.

Acknowledgements

This course uses material from the following Python courses:

• Mark Bakker's Python for Exploratory Computing course.
• Chris Rands' biopython-coronavirus course.

License

This course is licensed under the Creative Commons Attribution 4.0 International License.