Journal Article

Inferring relative proportions of DNA variants from sequencing electropherograms

I. M. Carr, J. I. Robinson, R. Dimitriou, A. F. Markham, A. W. Morgan and D. T. Bonthron

in Bioinformatics

Volume 25, issue 24, pages 3244-3250
Published in print December 2009 | ISSN: 1367-4803
Published online October 2009 | e-ISSN: 1460-2059 | DOI:
Inferring relative proportions of DNA variants from sequencing electropherograms

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Motivation: Determination of the relative copy number of single-nucleotide sequence variants (SNVs) within a DNA sample is a frequent experimental goal. Various methods can be applied to this problem, although hybridization-based approaches tend to suffer from high-setup cost and poor adaptability, while others (such as pyrosequencing) may not be accessible to all laboratories. The potential to extract relative copy number information from standard dye-terminator electropherograms has been little explored, yet this technology is cheap and widely accessible. Since several biologically important loci have paralogous copies that interfere with genotyping, and which may also display copy number variation (CNV), there are many situations in which determination of the relative copy number of SNVs is desirable.

Results: We have developed a desktop application, QSVanalyzer, which allows high-throughput quantification of the proportions of DNA sequences containing SNVs. In reconstruction experiments, QSVanalyzer accurately estimated the known relative proportions of SNVs. By analyzing a large panel of genomic DNA samples, we demonstrate the ability of the software to analyze not only common biallelic SNVs, but also SNVs within a locus at which gene conversion between four genomic paralogs operates, and within another that is subject to CNV.

Availability and Implementation: QSVanalyzer is freely available at It requires the Microsoft .NET framework version 2.0, which can be installed on all Microsoft operating systems from Windows 98 onwards.


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4699 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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