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Viral Recombination

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Updated: Jun 5, 2025

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Polyphonia: detecting inter-sample contamination in viral genomic sequencing data.

Lydia A Krasilnikova^1,2,3, Christopher H Tomkins-Tinch², Alton C Gayton⁴

¹Howard Hughes Medical Institute, Chevy Chase, MD 20815, United States.

Bioinformatics (Oxford, England)

|December 14, 2024

Summary

This summary is machine-generated.

Polyphonia detects inter-sample contamination in viral sequencing data using existing genomic information, eliminating the need for extra controls. This tool enhances the accuracy of viral genomic research and surveillance by identifying contamination at any stage.

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Last Updated: Jun 5, 2025

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Published on: May 26, 2013

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

Area of Science:

Genomics
Bioinformatics
Virology

Background:

Inter-sample contamination poses a significant challenge in viral genomic research, impacting variant detection, evolutionary analysis, and outbreak reconstruction.
Existing methods like sample barcoding have limitations, as they cannot detect contamination prior to their implementation in the experimental pipeline.

Purpose of the Study:

To introduce Polyphonia, a novel computational tool designed to detect inter-sample contamination directly from deep sequencing data.
To provide a method for identifying contamination without requiring additional experimental controls.

Main Methods:

Polyphonia analyzes intrahost variant frequencies within sequencing data to infer the presence of contamination.
The tool processes raw sequencing data, leveraging inherent genomic information to identify anomalies indicative of contamination.

Main Results:

Polyphonia was applied to 1102 SARS-CoV-2 samples, demonstrating its capability to detect inter-sample contamination.
The tool's performance was validated against samples previously tracked using molecular barcoding, confirming its accuracy.

Conclusions:

Polyphonia offers a robust and control-free approach for detecting inter-sample contamination in viral sequencing datasets.
The tool has the potential to improve the reliability of viral genomic research, surveillance, and outbreak investigations.