PanGBank tutorials

This repository contains runnable tutorials for PanGBank, a web-based platform for exploring, analyzing, and downloading pangenomes built with PPanGGOLiN.

These notebooks walk through real analyses, from AMR gene distribution to genome projection, so you can see PanGBank in action and adapt it to your own data.

The best place to start is the paper use case: the complete, end-to-end workflow used in the PanGBank publication, from a pre-computed pangenome to AMR gene analysis and genome projection.

Google Colab:

Opens the notebook directly in Google Colaboratory — no installation required.

A self-contained docker image is available to run the tutorials:

docker run -p 8888:8888 ghcr.io/labgem/pangbank-tutorial:latest pangbank-tuto article_use_case

A jupyterlab instance will then be available at http://localhost:8888.

Install the conda environment

git clone https://github.com/labgem/PanGBank-tutorial.git
cd PanGBank-tutorial
conda env create -f environment.yaml
conda activate pangbank-tutorial
pip install pangbank-cli pangbank-api[sdk]==0.5.0 plotly>=6.1.1 kaleido fa2

Then run jupyter-notebook in the tutorial directory:

cd tutorials/article_use_case/ && jupyter-notebook notebook.ipynb

Shorter, standalone notebooks covering focused analyses — several of these were used to build the paper use case above.

Antimicrobial resistance analyses: find Regions of Genome Plasticity (RGPs) and Spots with AMR-related annotations.

name	description	notebook
Acinetobacter_baumannii	Downstream analysis based on pre-computed PPanGGOLiN outputs.
Acinetobacter_nosocomialis	End-to-end tutorial using a small pangenome.

Genome Projection

name	description	notebook
Projection	Augment a pangenome with AMR annotations, then project a new genome onto the annotated pangenome.

See the tutorials/ directory for the full list of notebooks and examples.