MEDC0106: Python workshop

This repository contains material for the Bioinformatics in Applied Biomedical Science (MEDC0106) Python workshop.

Getting started

The workshop materials are hosted on GitHub. You can run them directly online using either Google Colab (requires a Google account but loads quickly) or Binder (no login needed, though it may take several minutes to start). Use the buttons above to launch your preferred option.

Workshop format

The workshop runs over three sessions combining short lectures and self-paced practicals. Each session builds on the previous one. Start with the introductory notebooks before attempting the exercises. We will review the previous session’s exercises at the start of each class.
By the end of the workshop, you should feel confident writing and running Python code, and understand its applications in bioinformatics and data analysis. All material remains available online if you wish to revisit it later.

• Session 1 (Introduction to Python)

  1. (01) Introduction to Python
  2. (02) Functions
  3. (03) Modules and packages
  4. (04) Exercises

• Session 2 (Bioinformatics with BioPython)

  1. (05) Handling sequences with Biopython
  2. (06) Exercises
  3. (07) Handling protein data with Biopython (supplementary material)
  4. (08) Supplementary exercises

• Session 3 (Introduction to Data Analysis)

  1. (09) Pandas
  2. (10) Exercises
  3. (11) NumPy (supplementary material)
  4. (12) Predictive modelling (supplementary material)
  5. (13) Supplementary exercises

Contact

For general queries: please post in the discussion forum so others can benefit.

For direct or more specific help:

• Mateusz Kaczyński – mateusz.kaczynski.21@ucl.ac.uk
• Florion Peni – f.peni@ucl.ac.uk

For bugs or incorrect information, please open an issue or pull request.

License

This work is licensed under the Attribution 4.0 International (CC BY 4.0). To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Acknowledgments

Python packages

• Bioinformatics:

  • biopython

• Data science:

  • pandas, scikit-learn, scipy, numpy

• Data visualisation:

  • matplotlib, seaborn

If we are using your resource and forgot to add it here, please contact us so that we can rectify this. Thank you!

Authors

• Oliver Scott
• Mateusz Kaczyński