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Updated: May 11, 2026
11:11
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted
Anna Cushing1, Patrick Flaherty, Erik Hopmans
1Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. genomics_ji@stanford.edu.
BMC Research Notes
|May 25, 2013
Summary
A new algorithm, RVD, enhances rare variant detection in DNA sequencing, identifying mutations at frequencies as low as 0.1%. This tool improves genetic insights for diseases, aiding therapeutic response prediction.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Rare single nucleotide variants contribute to genetic diversity and disease heterogeneity.
- Detecting rare mutations in clinical samples is crucial for predicting therapeutic responses.
- Existing rare variant detection methods are limited by sequencing errors and platform constraints.
Purpose of the Study:
- To introduce an optimized rare variant detection algorithm (RVD) for targeted gene resequencing.
- To provide a sensitive and specific tool for identifying low-frequency genetic variants.
Main Methods:
- Developed RVD, a command-line and MATLAB program, utilizing a beta-binomial model for context-specific error estimation.
- RVD processes standard BAM formatted sequence files.
- Algorithm tested on multiple Illumina sequencing platforms.
Main Results:
- RVD accurately calls variants with minor allele frequencies (MAF) as low as 0.1%.
- The algorithm demonstrated robust performance on synthetic and clinical virus samples.
- Tested on Illumina GAIIx and MiSeq platforms.
Conclusions:
- RVD addresses the need for sensitive and specific rare variant detection tools.
- The algorithm can enhance the understanding of viral disease genetics and treatment, including influenza.
- RVD is publicly available for research use.
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