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A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology

The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs), requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s). Deep sequencing is able to provide an assessment of both th...

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Autores principales: Kravatsky, Yuri, Chechetkin, Vladimir, Fedoseeva, Daria, Gorbacheva, Maria, Kravatskaya, Galina, Kretova, Olga, Tchurikov, Nickolai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5744132/
https://www.ncbi.nlm.nih.gov/pubmed/29168754
http://dx.doi.org/10.3390/v9120357
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author Kravatsky, Yuri
Chechetkin, Vladimir
Fedoseeva, Daria
Gorbacheva, Maria
Kravatskaya, Galina
Kretova, Olga
Tchurikov, Nickolai
author_facet Kravatsky, Yuri
Chechetkin, Vladimir
Fedoseeva, Daria
Gorbacheva, Maria
Kravatskaya, Galina
Kretova, Olga
Tchurikov, Nickolai
author_sort Kravatsky, Yuri
collection PubMed
description The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs), requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s). Deep sequencing is able to provide an assessment of both the general target conservation and the frequency of particular mutations in the different target sites. The aim of this study was to develop a reliable bioinformatic pipeline for the analysis of millions of short, deep sequencing reads corresponding to selected highly variable viral sequences that are drug target(s). The suggested bioinformatic pipeline combines the available programs and the ad hoc scripts based on an original algorithm of the search for the conserved targets in the deep sequencing data. We also present the statistical criteria for the threshold of reliable mutation detection and for the assessment of variations between corresponding data sets. These criteria are robust against the possible sequencing errors in the reads. As an example, the bioinformatic pipeline is applied to the study of the conservation of RNA interference (RNAi) targets in human immunodeficiency virus 1 (HIV-1) subtype A. The developed pipeline is freely available to download at the website http://virmut.eimb.ru/. Brief comments and comparisons between VirMut and other pipelines are also presented.
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spelling pubmed-57441322017-12-31 A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology Kravatsky, Yuri Chechetkin, Vladimir Fedoseeva, Daria Gorbacheva, Maria Kravatskaya, Galina Kretova, Olga Tchurikov, Nickolai Viruses Article The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs), requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s). Deep sequencing is able to provide an assessment of both the general target conservation and the frequency of particular mutations in the different target sites. The aim of this study was to develop a reliable bioinformatic pipeline for the analysis of millions of short, deep sequencing reads corresponding to selected highly variable viral sequences that are drug target(s). The suggested bioinformatic pipeline combines the available programs and the ad hoc scripts based on an original algorithm of the search for the conserved targets in the deep sequencing data. We also present the statistical criteria for the threshold of reliable mutation detection and for the assessment of variations between corresponding data sets. These criteria are robust against the possible sequencing errors in the reads. As an example, the bioinformatic pipeline is applied to the study of the conservation of RNA interference (RNAi) targets in human immunodeficiency virus 1 (HIV-1) subtype A. The developed pipeline is freely available to download at the website http://virmut.eimb.ru/. Brief comments and comparisons between VirMut and other pipelines are also presented. MDPI 2017-11-23 /pmc/articles/PMC5744132/ /pubmed/29168754 http://dx.doi.org/10.3390/v9120357 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kravatsky, Yuri
Chechetkin, Vladimir
Fedoseeva, Daria
Gorbacheva, Maria
Kravatskaya, Galina
Kretova, Olga
Tchurikov, Nickolai
A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology
title A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology
title_full A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology
title_fullStr A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology
title_full_unstemmed A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology
title_short A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology
title_sort bioinformatic pipeline for monitoring of the mutational stability of viral drug targets with deep-sequencing technology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5744132/
https://www.ncbi.nlm.nih.gov/pubmed/29168754
http://dx.doi.org/10.3390/v9120357
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