Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data.
Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data.
复制标题
DOI：
10.3389/fmicb.2012.00329
复制
发表时间：
2012
影响因子：
5.2
通讯作者：
Metzner KJ
中科院分区：
文献类型：
--
作者：
Beerenwinkel N;Günthard HF;Roth V;Metzner KJ
Many viruses, including the clinically relevant RNA viruses HIV (human immunodeficiency virus) and HCV (hepatitis C virus), exist in large populations and display high genetic heterogeneity within and between infected hosts. Assessing intra-patient viral genetic diversity is essential for understanding the evolutionary dynamics of viruses, for designing effective vaccines, and for the success of antiviral therapy. Next-generation sequencing (NGS) technologies allow the rapid and cost-effective acquisition of thousands to millions of short DNA sequences from a single sample. However, this approach entails several challenges in experimental design and computational data analysis. Here, we review the entire process of inferring viral diversity from sample collection to computing measures of genetic diversity. We discuss sample preparation, including reverse transcription and amplification, and the effect of experimental conditions on diversity estimates due to in vitro base substitutions, insertions, deletions, and recombination. The use of different NGS platforms and their sequencing error profiles are compared in the context of various applications of diversity estimation, ranging from the detection of single nucleotide variants (SNVs) to the reconstruction of whole-genome haplotypes. We describe the statistical and computational challenges arising from these technical artifacts, and we review existing approaches, including available software, for their solution. Finally, we discuss open problems, and highlight successful biomedical applications and potential future clinical use of NGS to estimate viral diversity.
登录
查看更多内容
影响因子：
6.7
作者：
Bunnik, Evelien M.;Swenson, Luke C.;van't Wout, Angelique B.
通讯作者：
van't Wout, Angelique B.
影响因子：
64.8
作者：
Burch, CL;Chao, L
通讯作者：
Chao, L
影响因子：
3.3
作者：
Althaus CF;Vongrad V;Niederöst B;Joos B;Di Giallonardo F;Rieder P;Pavlovic J;Trkola A;Günthard HF;Metzner KJ;Fischer M
通讯作者：
Fischer M
影响因子：
2.7
作者：
Bolcic, F.;Sede, M.;Quarleri, J.
通讯作者：
Quarleri, J.
影响因子：
14.9
作者：
Casbon JA;Osborne RJ;Brenner S;Lichtenstein CP
通讯作者：
Lichtenstein CP