PanBGC-DB

PanBGC-DB is a publicly available database and analysis platform designed to explore the genetic diversity and evolutionary dynamics of biosynthetic gene cluster (BGC) families using a pangenome-inspired framework. The platform integrates large-scale BGC datasets from antiSMASH-DB and MIBiG, clusters them into gene cluster families (GCFs), and applies orthologous grouping and openness metrics to characterize core, accessory, and unique genes within each family.

Features

• Interactive visualization of BGC families and gene presence/absence matrices
• Gene classification into core, accessory, and unique types
• Openness metric calculation using Heaps’ Law
• Comparative views of gene architecture, domain organization, and phylogenetic relationships
• Query tool for uploading and comparing custom BGCs

Access

The web platform is available at:
👉 https://panbgc-db.cs.uni-tuebingen.de

Source Code

This repository contains the source code for:

• The PanBGC-DB web interface
• The backend clustering, orthologous grouping, and openness analysis scripts
• Data processing and visualization modules

How to Create the Visualization

Prerequisites

These instructions work for Linux and macOS. If you are on Windows, the Windows Subsystem for Linux (WSL) is recommended.

1. Install Miniconda
   Download and install Miniconda for your operating system.

2. Download Required Tools

   • Download and extract the folder Astral-Pro3 from GitHub.
   • Download the pipeline scripts from this repository file or directly from the website unuder the Visualisation tab.

Installation Commands

For Debian/Ubuntu and macOS with Intel chip:

conda create --name PanBGC_vis -c conda-forge -c bioconda zol

For macOS with Apple Silicon chip:

CONDA_SUBDIR=osx-64 conda create -n PanBGC_vis -c conda-forge -c bioconda zol

For all systems (after creating the environment):

conda activate PanBGC_vis
pip install openpyxl tqdm

Note: If you want to use all annotation libraries, you can remove the -m flag from the following command (increases download and run time):

setup_annotation_dbs.py -m

Running the Pipeline

1. Extract the Scripts
   Unzip the downloaded Scripts.zip to a location of your choice.

2. Navigate to the Scripts Directory

cd /path/to/extracted/Scripts

3. Run the Pipeline

If visualizing a single Gene Cluster Family (GCF), the input folder can contain GenBank files directly.
For multiple GCFs, place subfolders (each containing GenBank files) inside the input folder. No further changes are required.

python PanBGC.py -i /path/to/input_folder \
                 -o /path/to/result_folder \
                 -log /path/to/result_folder/log \
                 -c number_of_threads \
                 -al /path/to/astral-pro3

Note: The astral-pro3 executable is found in the extracted Astral-Pro3 folder under:
ASTER-Linux/bin/astral-pro3

Parameters Explained:

• -i: Path to the input folder with GenBank files
• -o: Path to the output directory
• -log: Log file location
• -c: Number of CPU threads to use
• -al: Path to astral-pro3 executable

Upload Results

After successful execution, your output folder will contain:

/path/to/result_folder/single_family/Final_Results/Visualisation.json

This file can be uploaded directly using the form on the left side of the PanBGC-DB interface.

Example command:

python PanBGC.py -i ~/data/genomes \
                 -o ~/results/analysis \
                 -log ~/results/analysis/pipeline.log \
                 -c 4 \
                 -al ~/tools/ASTER-Linux/bin/astral-pro3