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Updated: Jan 15, 2026

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

ViralBottleneck: an R package for estimating viral transmission bottlenecks from deep sequencing data using multiple

Bowen Zheng¹, Paul C D Johnson², Joseph Hughes¹

¹MRC-University of Glasgow Centre for Virus Research, Sir Michael Stoker Building, 464 Bearsden Road, Glasgow G61 1QH, United Kingdom.

Virus Evolution

|October 15, 2025

Summary

This summary is machine-generated.

Understanding viral transmission bottlenecks is key to controlling infectious diseases. This study introduces ViralBottleneck, an R package to estimate bottleneck size, revealing significant variations across different estimation methods.

Keywords:

R package bottleneck deep sequencing intra-host viral diversity transmission

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Last Updated: Jan 15, 2026

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

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

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Published on: June 16, 2011

Area of Science:

Virology
Computational Biology
Epidemiology

Background:

Acute viral infections present major public health concerns.
Viral evolution, immune escape, and disease severity are influenced by host-to-host transmission dynamics.
Transmission bottlenecks, which reduce viral genetic diversity during spread, are critical for understanding disease emergence and evolution.

Purpose of the Study:

To introduce ViralBottleneck, an R package for estimating viral transmission bottleneck size.
To integrate six established methods for bottleneck size estimation within a single package.
To provide a user-friendly tool for researchers studying viral transmission.

Main Methods:

The ViralBottleneck R package integrates six methods: presence-absence, Kullback-Leibler (KL), binomial, two beta-binomial versions, and Wright-Fisher.
Simulated datasets generated using SANTA-Sim were employed to test package functionality under various scenarios with known bottleneck sizes.
The performance and output of each method were evaluated using these simulated datasets.

Main Results:

The study demonstrated the functionality of the ViralBottleneck package using simulated data.
Significant variations in transmission bottleneck size estimates were observed across the six integrated methods.
The choice of estimation method demonstrably impacts the resulting bottleneck size estimations.

Conclusions:

The ViralBottleneck package offers a comprehensive suite of tools for transmission bottleneck analysis.
Methodological choices significantly influence the estimation of viral transmission bottleneck size.
Accurate estimation of bottleneck size is crucial for predicting disease dynamics and informing public health interventions.