Advanced Python Course – Proteomics Pipeline

This repository hosts a five-day intensive course that teaches advanced Python package development practices through a simulated mass-spectrometry proteomics workflow.

Simulated proteomics pipeline

The course emulates a bottom-up proteomics experiment:

• Input: Multi-protein FASTA file: sample_proteins.fasta.
• Processing: Enzymatic digestion → LC retention-time prediction → MS1/MS2 simulation (mass, m/z, fragmentation).
• Output: TSV files summarizing peptide/fragment m/z values (stored under results/), diagnostic plots, and a reusable Python package.

Course overview

• Day 1: FASTA parsing and protein digestion fundamentals (tutorials/1_protein_digestion.ipynb)
• Day 2: Liquid chromatography simulation (tutorials/2_liquid_chromatography.ipynb)
• Day 3: MS1/MS2 spectra simulation (tutorials/3_mass_spectra_simulation.ipynb)
• Day 4: Build a reusable Python package from the functions (tutorials/4_build_your_own_package.md)
• Day 5: Publish the package, set up CI, and collaborate (tutorials/5_publish_your_package.md)

A final notebook (tutorials/ms_simulation_final.ipynb) ties the package together for an end-to-end experiment.

Getting started

1. Clone this repository (e.g. from terminal):

cd mytargetdir
git clone https://github.com/UKHD-NP/advanced-python_course.git
cd advanced-python_course

2. Install miniconda following the instructions on (if not already installed):
   https://www.anaconda.com/docs/getting-started/miniconda/install#macos-terminal-installer
3. Create conda environment:

conda create --name advanced-python_course python=3.10 -y
conda activate advanced-python_course

4. Install dependencies from requirements.txt:

pip install -r requirements.txt
conda deactivate advanced-python_course

Folder structure

.
├── data/                      # Input FASTA files
├── tutorials/                 # Day-by-day Jupyter notebooks
├── templates/                 # pyproject.toml and packaging scaffolds
├── results/                   # Output TSVs saved by tutorials
├── docs/                      # Reference materials
└── README.md

Recommendations

If you work in Google Colab, mount your Drive and switch into the repository folder so notebooks can read/write data. Pick the snippet that best matches your situation.

1. You already know where the repo lives

from google.colab import drive
drive.mount('/content/drive')

import os

repo_path = '/content/drive/MyDrive/path/to/advanced-python_course/tutorials'
os.chdir(repo_path)
print('Directory changed to:', os.getcwd())

2. You only know the notebook name

from google.colab import drive
drive.mount('/content/drive')

import os

target_notebook = '1_protein_digestion.ipynb'
notebook_dir = None

for root, _, files in os.walk('/content/drive/MyDrive'):
    if target_notebook in files:
        notebook_dir = os.path.dirname(os.path.join(root, target_notebook))
        break

if notebook_dir:
    os.chdir(notebook_dir)
    print('Directory changed to:', notebook_dir)
else:
    print(f\"Notebook '{target_notebook}' not found in Google Drive.\")

Improvements for next iteration of the course

Student feedback

• Advertise the course better to the students: "advanced" python in the sene that they learned package development skills, not that they learn how to write more complex algorithms and code. Also communicate the coding skill requirements well. Probably via HeiCo.
• Provide clear instructions on how to obtain access to Datacamp WELL in advance
• Testing course on Datacamp may have been overkill since we only used simple testing functions
• Conda/VScode/Gcollab/environment setup session prior to practical week to avoid overhead to actual tutorials. Maybe even with a script?
• Problem: cloning repo and working direclty inside hinders pulling of changes to the tutorials. Solution? Work on tutorials in a separate directory
• Great clarity, structure and objective setting, but for some more advanced students the first tutorials were too easy, too much was given. Solution? Hide proposed code under "hints" foldable sections. This way advanced students have a greater challenge while less advanced can still profit from templates.
• For bigger groups, ensure enough power outlets in the room.

Content improvement

• For exercise 5.12 use the E.coli fasta file and ask the students to compare different proteases as well as to plot the distribution of nr. of peptides per protein (fasta file already included in repo).