Panorama: A robust pangenome-based method for predicting and comparing biological systems across species

About the project

PANORAMA is an innovative pangenomic tool designed to exploit pangenome graphs and enable them to be annotated and compared in order to explore the genomic diversity of several species. Based on the PPanGGOLiN software suite¹ (https://github.com/labgem/PPanGGOLiN), PANORAMA integrates advanced methods for rule-based prediction of macromolecular systems and comparative analysis of conserved features between different pangenomes, such as hotspots of insertion. We illustrate the use of PANORAMA on a Pseudomonas aeruginosa dataset, evaluating its performance against reference defense system prediction tools such as PADLOC² and DefenseFinder³. The analysis was then extended to a larger set including four species of Enterobacteriaceae (>6,000 genomes), demonstrating PANORAMA's ability to annotate, compare and explore the diversity and distribution of biological systems across multiple species. This work provides new methods for the large-scale comparative study of microbial genomes and underlines the relevance of pangenome approaches in deciphering their evolutionary dynamics. PANORAMA is freely available and accessible through: https://github.com/labgem/PANORAMA

Authors

• Jérôme Arnoux, LABGeM, Genomics Metabolics, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS
• Jean Mainguy, LABGeM, Genomics Metabolics, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS
• Laura Bry, LABGeM, Genomics Metabolics, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS
• Quentin Fernandez De Grado, LABGeM, Genomics Metabolics, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS
• Yazid Hoblos, LABGeM, Genomics Metabolics, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS
• David Vallenet, LABGeM, Genomics Metabolics, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS
• Alexandra Calteau, LABGeM, Genomics Metabolics, CEA, Genoscope, Institut François Jacob, Université d’Évry, Université Paris-Saclay, CNRS

Dependencies

All the dependencies has been listed in the conda-env.yml file.

Dataset

The dataset is available on zenodo: (lien vers Data)[]. It contains all data and materials necessary to reproduce the results presented in the article:

• Input data: Pangenome files used as input for the analyses
• Output data: Complete results generated by PANORAMA as presented in the manuscript
• Genome list: Catalog of genomes used to construct the pangenomes

All data and results can be used to fully reproduce the analyses and figures presented in the publication.

Contents:

• pangenomes.tar.xz/ - Pangenome input files
• DATA.tar.xz/ - Results generated by PANORAMA & source script to generate figures (script from this repository)
• Genomes.tar.xz - List of genomes used for pangenomes construction

Running the project

Google Colab

The notebook is available on Google Colab here, or by opening from GitHub the main.py file and click on Colab button.

Then follow the Google Colab instruction instruction.

Local execution

To begin, note that you need to install Conda, follow the installation guide here

To execute the notebook, you will need to first install some packages.

These are listed in the following conda environment file conda-env.yml, that you could use to easily install them.

You can follow the under code to install all dependencies and run the notebook:

git clone https://github.com/labgem/PANORAMA_article
cd PANORAMA_article
conda update -n base -c defaults conda -y
conda env create --file conda-env.yml
conda init bash
conda activate panorama_notebook
python -m ipykernel install --user --name=panorama_notebook
jupyter notebook --notebook-dir=.

N.B: If you encounter any difficulty to install with conda try to use mamba or write an issue

Then skip the first cells that are specific for Google Colab and start from "Results - Define constant and load data" right after "Introduction"

References

1. Gautreau, Guillaume, Adelme Bazin, Mathieu Gachet, Rémi Planel, Laura Burlot, Mathieu Dubois, Amandine Perrin, et al. « PPanGGOLiN: Depicting Microbial Diversity via a Partitioned Pangenome Graph ». PLOS Computational Biology 16, nᵒ 3 (19 mars 2020): e1007732. https://doi.org/10.1371/journal.pcbi.1007732.
2. Payne, Leighton J, Thomas C Todeschini, Yi Wu, Benjamin J Perry, Clive W Ronson, Peter C Fineran, Franklin L Nobrega, et Simon A Jackson. « Identification and classification of antiviral defence systems in bacteria and archaea with PADLOC reveals new system types ». Nucleic Acids Research 49, nᵒ 19 (8 novembre 2021): 10868‑78. https://doi.org/10.1093/nar/gkab883.
3. Doron, Shany, Sarah Melamed, Gal Ofir, Azita Leavitt, Anna Lopatina, Mai Keren, Gil Amitai, et Rotem Sorek. « Systematic discovery of antiphage defense systems in the microbial pangenome ». Science 359, nᵒ 6379 (2 mars 2018): eaar4120. https://doi.org/10.1126/science.aar4120.