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Updated: Jun 19, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
Inferring relative proportions of DNA variants from sequencing electropherograms
I M Carr1, J I Robinson, R Dimitriou
1Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, University of Leeds, St James's University Hospital, Beckett Street, Leeds LS9 7TF, UK. msjimc@leeds.ac.uk
QSVanalyzer software accurately quantifies single-nucleotide variants (SNVs) proportions from DNA sequences. This accessible tool aids in determining relative copy number for SNVs, even in complex genomic regions with paralogs and copy number variation (CNV).
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Determining relative copy number of single-nucleotide variants (SNVs) is crucial in genetics.
- Existing methods like hybridization and pyrosequencing have limitations in cost and accessibility.
- Standard dye-terminator electropherograms offer an unexplored, cost-effective avenue for copy number analysis.
Purpose of the Study:
- To develop a user-friendly software for high-throughput quantification of SNV proportions.
- To assess the software's accuracy in estimating relative SNV proportions.
- To demonstrate the software's capability in analyzing complex genetic scenarios including paralogs and copy number variation (CNV).
Main Methods:
- Development of a desktop application named QSVanalyzer.
- Utilizing standard dye-terminator electropherogram data.
- Reconstruction experiments and analysis of a large panel of genomic DNA samples.
Main Results:
- QSVanalyzer accurately estimates the relative proportions of SNVs in reconstruction experiments.
- The software successfully analyzes common biallelic SNVs.
- QSVanalyzer handles SNVs in loci with gene conversion among paralogs and those with copy number variation (CNV).
Conclusions:
- QSVanalyzer provides a robust and accessible solution for SNV relative copy number determination.
- The software is effective for high-throughput analysis of SNVs in diverse genomic contexts.
- This tool facilitates research in areas affected by SNVs, paralogs, and CNV.
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