Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data
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- 作者：Richard J Orton ; Caroline F Wright ; Marco J Morelli ; David J King…
- 关键词：Quasispecies ; Viral population ; Viral evolution ; High ; throughput sequencing ; Next generation sequencing ; Variant Calling ; Sequencing error correction ; RT ; PCR Errors ; Rare mutations
- 刊名：BMC Genomics
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：16
- 期：1
- 全文大小：1,546 KB
- 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
- 出版者：BioMed Central
- ISSN：1471-2164
文摘
Background RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.