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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.
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...

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Related Experiment Video

Updated: Jun 6, 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: visualizing pathogen dispersal in a high-performance browser application.

Yimin Li^1,2, Nena Bollen^1,2, Samuel L Hong¹

¹Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven 3000, Belgium.

Bioinformatics (Oxford, England)

|December 3, 2024

Summary

This summary is machine-generated.

We developed Spread.gl, an open-source application for visualizing pathogen dispersal history. This tool aids researchers in understanding spatio-temporal spread patterns and exploring environmental factors influencing disease transmission.

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Last Updated: Jun 6, 2025

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Area of Science:

Epidemiology
Computational Biology
Bioinformatics

Background:

Bayesian phylogeographic analyses are crucial for understanding pathogen dispersal history.
Interpreting complex phylogeographic data requires advanced visualization tools.

Purpose of the Study:

To develop an intuitive and feature-rich visualization tool for phylogeographic reconstructions.
To enable the exploration of environmental factors influencing pathogen spread.

Main Methods:

Development of Spread.gl, an open-source, browser-based application.
Visualization of discrete and continuous phylogeographic inferences.
Animation of pathogen geographic dispersal through time.
Integration of phylogenetic data with environmental data layers.

Main Results:

Spread.gl provides smooth and intuitive visualization of phylogeographic data.
The application supports the animation of pathogen dispersal over time.
Demonstrated utility with a large-scale SARS-CoV-2 genomic dataset (>17,000 sequences).

Conclusions:

Spread.gl enhances the interpretation of complex phylogeographic analyses.
Facilitates the exploration of environmental drivers of pathogen dispersal.
Offers a valuable resource for epidemiological research and public health.