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Related Experiment Video

Updated: Nov 9, 2025

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

Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach.

Jeffrey N Dudley¹, Celine S Hong², Marwan A Hawari¹

¹National Human Genome Research Institute, National Institutes of Health, 50 South Drive Room 5140, Bethesda, MD, 20892, USA.

BMC Bioinformatics

|April 9, 2021

Summary

This summary is machine-generated.

Detecting low-level mosaic variants is challenging. Position-Based Variant Identification (PBVI) improves detection of these variants using next-generation sequencing data for research and diagnostics.

Keywords:

Mosaic variants Prediction of mosaic variants Somatic overgrowth disorder

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Last Updated: Nov 9, 2025

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Next-generation sequencing (NGS) reveals the importance of somatic mosaicism in disease.
Detecting low-frequency mosaic variants in NGS data is a significant challenge.

Purpose of the Study:

To introduce a novel method, Position-Based Variant Identification (PBVI), for enhanced detection of low-level mosaic variants.
To evaluate PBVI's performance in identifying single nucleotide mosaic variants.

Main Methods:

PBVI utilizes empirically-derived nucleotide distributions from control data.
The method was modeled on 11 segmental overgrowth genes.
Performance was assessed at sequencing depths of 600× and 1200×.

Main Results:

PBVI improves detection of single nucleotide mosaic variants (0.01-0.05 variant allele fraction) compared to existing callers.
Sensitivity reached >85% at 600× and >95% at 1200× depth.
In 26 individuals with somatic overgrowth disorders, PBVI identified pathogenic variants in 17, showing improved signal-to-noise ratio.

Conclusions:

PBVI enhances the identification of low-level mosaic variants.
This method increases the utility of NGS data for both research and clinical diagnostics.