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Viral Recombination

Viral Recombination

Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.

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Updated: Jul 7, 2025

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

Evaluation of recombination detection methods for viral sequencing.

Frederick R Jaya^1,2, Barbara P Brito^1,3, Aaron E Darling^1,4

¹Australian Institute for Microbiology & Infection, University of Technology Sydney, 15 Broadway, Ultimo, New South Wales 2007, Australia.

Virus Evolution

|December 22, 2023

Summary

Identifying viral recombination signals is crucial for evolutionary studies. This research evaluates eight recombination detection methods (RDMs) for large-scale sequencing data, highlighting trade-offs in accuracy and scalability.

Keywords:

bioinformatics recombination recombination detection methods

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Last Updated: Jul 7, 2025

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

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Published on: June 16, 2011

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

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Determining 3'-Termini and Sequences of Nascent Single-Stranded Viral DNA Molecules during HIV-1 Reverse Transcription in Infected Cells

Determining 3'-Termini and Sequences of Nascent Single-Stranded Viral DNA Molecules during HIV-1 Reverse Transcription in Infected Cells

Published on: January 30, 2019

Area of Science:

Virology
Computational Biology
Evolutionary Genetics

Background:

Viral recombination significantly influences viral evolution, shaping new populations and lineages.
Accurate detection of recombination is essential for reliable evolutionary analyses, yet existing methods struggle with large datasets.
The increasing volume of viral sequencing data necessitates scalable and accurate recombination detection methods.

Purpose of the Study:

To assess the suitability of eight recombination detection methods (RDMs) for analyzing bulk viral sequencing data.
To evaluate the performance and scalability of these RDMs using simulated and empirical viral sequencing data.
To provide guidelines for validating recombination detection results and inform future method development.

Main Methods:

Evaluated eight RDMs: PhiPack, 3SEQ, GENECONV, RDP, MaxChi, Chimaera, UCHIME, and gmos.
Utilized simulated viral sequencing data with varying sequence diversities, recombination frequencies, and sample sizes.
Analyzed empirical viral sequencing data to validate findings and demonstrate practical application.

Main Results:

Assessed RDMs showed significant trade-offs between scalability, analytical approach, resolution, and accuracy.
No single RDM was universally optimal; suitability depends on dataset properties (e.g., sequence diversity, recombination frequency).
Performance varied considerably across methods when analyzing simulated and empirical large-scale viral sequencing data.

Conclusions:

RDMs must be scalable and possess analytical capabilities appropriate for the research application.
Method selection requires careful consideration of dataset characteristics to ensure accurate recombination detection.
Guidelines for validation and insights into method limitations are provided for large-scale viral sequencing data analysis.