Viruses 2017, 9(12), 357; https://doi.org/10.3390/v9120357
A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology
Engelhardt Institute of Molecular Biology of Russian Academy of Sciences, Vavilov str., 32, Moscow 119334, Russia
*
Author to whom correspondence should be addressed.
Received: 28 July 2017 / Revised: 16 November 2017 / Accepted: 21 November 2017 / Published: 23 November 2017
(This article belongs to the Special Issue Homage to Mark Wainberg)
Abstract
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 websiteKeywords:
viruses; drug targets; mutations; deep-sequencing; data processing; bioinformatic pipeline
▼
Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Kravatsky, Y.; Chechetkin, V.; Fedoseeva, D.; Gorbacheva, M.; Kravatskaya, G.; Kretova, O.; Tchurikov, N. A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology. Viruses 2017, 9, 357.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.