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Updated: Apr 14, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data.

Richard J Orton^1,2, Caroline F Wright³, Marco J Morelli⁴

¹Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom. Richard.Orton@glasgow.ac.uk.

|April 18, 2015

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This study developed a computational model to differentiate true viral mutations from sequencing errors. This method is crucial for accurately identifying low-frequency variants in RNA virus populations.

Area of Science:

Virology
Genomics
Bioinformatics

Background:

RNA viruses exhibit high mutation rates and population heterogeneity.
Next-generation sequencing (NGS) enables deep sequencing of viral genomes.
Distinguishing low-frequency viral variants from sequencing errors is a significant challenge.

Purpose of the Study:

To develop a method for accurately identifying true viral variants amidst sequencing and sample processing errors.
To establish reliable frequency thresholds for variant detection in heterogeneous viral populations.

Main Methods:

Creation of laboratory control samples with controlled viral diversity.
Assessment of reverse transcription (RT) and PCR amplification errors through varying amplification cycles.

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Last Updated: Apr 14, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

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

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Development of a genome-scale computational model to simulate and analyze processing errors.

Main Results:

Quantified errors introduced by RT and PCR amplification processes.
Identified genomic sites prone to higher error rates.
Developed a model to predict expected errors based on site coverage and quality scores.

Conclusions:

The developed datasets and computational models effectively distinguish true viral mutations from processing and sequencing errors.
This approach enhances the accuracy of viral variant identification, particularly for low-frequency mutations.