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Updated: Apr 18, 2026

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

Viral diversity and clonal evolution from unphased genomic data.

Hossein Khiabanian, Zachary Carpenter, Jeffrey Kugelman

|January 10, 2015

Summary

This study introduces novel genomic diversity measures for tracking clonal population evolution without full genomes. The method accurately captures rapid viral diversification, aiding infectious disease outbreak analysis.

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Last Updated: Apr 18, 2026

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

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Characterizing Mutational Load and Clonal Composition of Human Blood

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Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

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Published on: March 22, 2016

Area of Science:

Genomics
Evolutionary Biology
Infectious Disease Dynamics

Background:

Clonal expansion, asexual reproduction leading to diversifying populations, is key in pathogen evolution and disease outbreaks.
Existing phylogenetic tools often require full pathogen genomes or phased genotypes, limiting analysis of rapidly evolving populations.
Unphased genomic data presents a challenge for traditional evolutionary studies.

Purpose of the Study:

To develop novel methods for analyzing clonal population evolution using unphased genomic data.
To enable the study of pathogen diversification without the need for full genome reconstruction.
To accurately estimate evolutionary rates and divergence times in rapidly evolving populations.

Main Methods:

Introduced two new measures of diversity for clonal populations.
Utilized a maximum likelihood approach with a relaxed molecular clock model.
Estimated evolutionary rates and time to the most recent common ancestor (TMRCA) independent of growth models.

Main Results:

Evaluated and compared the new methods in silico and against existing approaches using 2009 H1N1 influenza data.
Applied the method to high-throughput genomic data from marburgvirus-infected non-human primates.
Inferred infection times, intra-host evolutionary rates, and identified purifying selection in viral populations.

Conclusions:

The developed method can detect minor variants (<1% of population) and capture genomic diversification within days.
It is an ideal tool for studying acute RNA viral infection dynamics.
Provides insights into pathogen evolution during emergent infectious disease outbreaks.