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Updated: Jul 13, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: March 5, 2022

G2GSnake: a Snakemake workflow for host-pathogen genomic association studies.

Zhi Ming Xu^1,2, Olivier Naret^1,2, Mariam Ait Oumelloul^1,2

¹School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.

Bioinformatics Advances

|October 16, 2023

Summary

Genome-to-genome (G2G) association studies reveal how host genetics influence pathogen evolution. This work introduces a Snakemake workflow and R Shiny app for reproducible G2G analyses, aiding host-pathogen interaction research.

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Last Updated: Jul 13, 2025

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Published on: March 5, 2022

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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Area of Science:

Genomics
Evolutionary Biology
Computational Biology

Background:

Understanding host-pathogen interactions is crucial for controlling infectious diseases.
Joint analysis of host and pathogen genomes offers insights into co-evolutionary dynamics.
Systematic approaches are needed to analyze complex genomic datasets from paired host and pathogen samples.

Purpose of the Study:

To develop a reproducible and scalable computational workflow for genome-to-genome (G2G) association studies.
To provide researchers with tools for identifying genetic associations between hosts and pathogens.
To facilitate the discovery of biological mechanisms underlying host-pathogen relationships.

Main Methods:

Implementation of a Snakemake workflow for conducting G2G association analyses.
Development of an R Shiny application for interactive visualization and summarization of G2G study results.
Testing associations between all host and pathogen genetic variants within paired samples.

Main Results:

The Snakemake workflow enables efficient and reproducible G2G association studies.
The R Shiny application allows for intuitive exploration and interpretation of complex G2G results.
Identified significant associations highlight potential genetic drivers of host-pathogen dynamics.

Conclusions:

The developed G2G analysis framework enhances the understanding of host-pathogen co-evolution.
This approach aids in pinpointing genetic factors influencing pathogen variation and host immune response.
The tools promote reproducible research and accelerate discoveries in infectious disease biology.