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RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing

BACKGROUND: Rare single nucleotide variants play an important role in genetic diversity and heterogeneity of specific human disease. For example, an individual clinical sample can harbor rare mutations at minor frequencies. Genetic diversity within an individual clinical sample is oftentimes reflect...

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Detalles Bibliográficos
Autores principales: Cushing, Anna, Flaherty, Patrick, Hopmans, Erik, Bell, John M, Ji, Hanlee P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3695852/
https://www.ncbi.nlm.nih.gov/pubmed/23701658
http://dx.doi.org/10.1186/1756-0500-6-206
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author Cushing, Anna
Flaherty, Patrick
Hopmans, Erik
Bell, John M
Ji, Hanlee P
author_facet Cushing, Anna
Flaherty, Patrick
Hopmans, Erik
Bell, John M
Ji, Hanlee P
author_sort Cushing, Anna
collection PubMed
description BACKGROUND: Rare single nucleotide variants play an important role in genetic diversity and heterogeneity of specific human disease. For example, an individual clinical sample can harbor rare mutations at minor frequencies. Genetic diversity within an individual clinical sample is oftentimes reflected in rare mutations. Therefore, detecting rare variants prior to treatment may prove to be a useful predictor for therapeutic response. Current rare variant detection algorithms using next generation DNA sequencing are limited by inherent sequencing error rate and platform availability. FINDINGS: Here we describe an optimized implementation of a rare variant detection algorithm called RVD for use in targeted gene resequencing. RVD is available both as a command-line program and for use in MATLAB and estimates context-specific error using a beta-binomial model to call variants with minor allele frequency (MAF) as low as 0.1%. We show that RVD accepts standard BAM formatted sequence files. We tested RVD analysis on multiple Illumina sequencing platforms, among the most widely used DNA sequencing platforms. CONCLUSIONS: RVD meets a growing need for highly sensitive and specific tools for variant detection. To demonstrate the usefulness of RVD, we carried out a thorough analysis of the software’s performance on synthetic and clinical virus samples sequenced on both an Illumina GAIIx and a MiSeq. We expect RVD can improve understanding the genetics and treatment of common viral diseases including influenza. RVD is available at the following URL:http://dna-discovery.stanford.edu/software/rvd/.
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spelling pubmed-36958522013-06-29 RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing Cushing, Anna Flaherty, Patrick Hopmans, Erik Bell, John M Ji, Hanlee P BMC Res Notes Technical Note BACKGROUND: Rare single nucleotide variants play an important role in genetic diversity and heterogeneity of specific human disease. For example, an individual clinical sample can harbor rare mutations at minor frequencies. Genetic diversity within an individual clinical sample is oftentimes reflected in rare mutations. Therefore, detecting rare variants prior to treatment may prove to be a useful predictor for therapeutic response. Current rare variant detection algorithms using next generation DNA sequencing are limited by inherent sequencing error rate and platform availability. FINDINGS: Here we describe an optimized implementation of a rare variant detection algorithm called RVD for use in targeted gene resequencing. RVD is available both as a command-line program and for use in MATLAB and estimates context-specific error using a beta-binomial model to call variants with minor allele frequency (MAF) as low as 0.1%. We show that RVD accepts standard BAM formatted sequence files. We tested RVD analysis on multiple Illumina sequencing platforms, among the most widely used DNA sequencing platforms. CONCLUSIONS: RVD meets a growing need for highly sensitive and specific tools for variant detection. To demonstrate the usefulness of RVD, we carried out a thorough analysis of the software’s performance on synthetic and clinical virus samples sequenced on both an Illumina GAIIx and a MiSeq. We expect RVD can improve understanding the genetics and treatment of common viral diseases including influenza. RVD is available at the following URL:http://dna-discovery.stanford.edu/software/rvd/. BioMed Central 2013-05-23 /pmc/articles/PMC3695852/ /pubmed/23701658 http://dx.doi.org/10.1186/1756-0500-6-206 Text en Copyright © 2013 Cushing et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Cushing, Anna
Flaherty, Patrick
Hopmans, Erik
Bell, John M
Ji, Hanlee P
RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing
title RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing
title_full RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing
title_fullStr RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing
title_full_unstemmed RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing
title_short RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing
title_sort rvd: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation dna resequencing
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3695852/
https://www.ncbi.nlm.nih.gov/pubmed/23701658
http://dx.doi.org/10.1186/1756-0500-6-206
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