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Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: May 22, 2026

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods.

Alexandre P Francisco¹, Cátia Vaz, Pedro T Monteiro

¹KDBIO, INESC-ID, , R, Alves Redol 9, 1000-029 Lisboa, Portugal.

BMC Bioinformatics

|May 10, 2012

Summary

This summary is machine-generated.

PHYLOViZ software integrates sequence-based typing and epidemiological data for microbial population studies. This user-friendly tool enhances bacterial surveillance by visualizing evolutionary relationships and analyzing diverse datasets.

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Area of Science:

Microbiology
Bioinformatics
Epidemiology

Background:

Decreasing DNA sequencing costs are driving the adoption of sequence-based typing for epidemiological surveillance.
Existing online databases for bacterial population data are underutilized due to a lack of user-friendly analysis tools.
Reproducible and comparable results are essential for both global bacterial population analysis and local epidemiological surveys.

Purpose of the Study:

To develop a user-friendly software for integrated analysis of sequence-based typing and epidemiological data.
To facilitate the exploration and utilization of existing microbial population datasets.
To support microbial epidemiological and population studies through combined data analysis.

Main Methods:

PHYLOViZ software utilizes Java for platform independence, integrating sequence-based typing data (including SNP data) with epidemiological information.
The goeBURST algorithm and its Minimum Spanning Tree expansion are employed for visualizing evolutionary relationships among bacterial isolates.
Results are presented as annotated graphs, allowing overlay of query results with other available epidemiological data.

Main Results:

PHYLOViZ enables integrated analysis of diverse data sources for microbial studies.
The software visualizes potential evolutionary links between isolates using goeBURST and MST.
Annotated graphs facilitate the overlay and interpretation of various epidemiological datasets.

Conclusions:

PHYLOViZ is a user-friendly software solution for combined analysis of multiple data sources in microbial epidemiology and population studies.
The software enhances the utility of existing bacterial surveillance data.
PHYLOViZ is freely accessible, promoting wider adoption in research and public health.