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Infection

Infection

When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
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Related Experiment Video

Updated: Jun 23, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

spread.gl: visualising pathogen dispersal in a high-performance browser application.

Yimin Li¹, Nena Bollen¹, Samuel L Hong¹

¹Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.

Medrxiv : the Preprint Server for Health Sciences

|June 17, 2024

Summary

This summary is machine-generated.

spread.gl is a new tool for visualizing pathogen dispersal history using phylogeographic data. It animates geographic spread through time and integrates environmental data for deeper insights.

Keywords:

data visualisation environmental factors geospatial diffusion phylodynamics phylogeography

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Last Updated: Jun 23, 2025

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Area of Science:

* Evolutionary Biology
* Computational Biology
* Epidemiology

Background:

* Phylogeographic analyses utilize location data from molecular sequences to reconstruct pathogen dispersal history.
* Interpreting complex phylogeographic results often requires specialized visualization tools.
* Existing methods may lack the features for intuitive and comprehensive visualization of spatio-temporal data.

Purpose of the Study:

* To introduce spread.gl, an open-source, browser-based application for visualizing phylogeographic inference results.
* To enable intuitive and user-friendly visualization of both discrete and continuous phylogeographic data.
* To facilitate the exploration of environmental factors influencing pathogen dispersal patterns.

Main Methods:

* Developed spread.gl as a feature-rich, open-source visualization application.
* Enabled the combination of geographic, phylogenetic, and environmental data layers.
* Implemented smooth animation capabilities for visualizing pathogen geographic dispersal through time.
* Showcased functionality with diverse pathogen dispersal examples, including a large-scale SARS-CoV-2 analysis.

Main Results:

* spread.gl provides smooth, intuitive, and user-friendly visualization of phylogeographic inference.
* The application effectively animates pathogen geographic dispersal over time.
* Users can integrate and explore multiple data layers, including environmental data, alongside phylogenetic information.
* Demonstrated successful visualization of a large SARS-CoV-2 dataset with over 17,000 sequences.

Conclusions:

* spread.gl enhances the interpretability of complex phylogeographic analyses.
* Facilitates the exploration of environmental drivers of pathogen dispersal.
* Offers a powerful platform for hypothesis generation, guiding further statistical analysis.
* Represents a significant advancement in the visualization of pathogen evolution and spread.