Analysis of Quasispecies of Avain Leukosis Virus Subgroup J Using Sanger and High-throughput Sequencing. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Meng, Fanfeng; Dong, Xuan; Hu, Tao; Liu, Yingnan; Zhao, Yingjie; Lv, Yanyan; Chang, Shuang; Zhao, Peng; Cui, Zhizhong
• Is Part Of: Virology journal. Volume 13:Issue 1(2016)
• Page Start: 1
• Page End: 7
• Publication Date: 2016-12
• Subjects: Quasispecies-Sanger sequencing-deep sequencing-simulation; Virology -- Periodicals; 579.2
• DOI: 10.1186/s12985-016-0559-6
• Languages: English
• ISSNs: 1743-422X

Abstract

Abstract Background Avian leukosis viruses subgroup J (ALV-J) exists as a complex mixture of different, but closely related genomes named quasispecies subjected to continuous change according to the Principles of Darwinian evolution. Method The present study seeks to compare conventional Sanger sequencing with deep sequencing using MiSeq platform to study quasispecies dynamics of ALV-J. Results The accuracy and reproducibility of MiSeq sequencing was determined better than Sanger sequencing by running each experiment in duplicate. According to the mutational rate of single position and the ability to distinguish dominant quasispecies with two sequencing methods, conventional Sanger sequencing technique displayed high randomness due to few sequencing samples, while deep sequencing could reflect the composition of the quasispecies more accurately. In the mean time, the research of quasispecies via Sanger sequencing was simulated and analyzed with the aid of re-sampling strategy with replacement for 1000 times repeat from high-throughput sequencing data, which indicated that the higher antibody titer, the higher sequence entropy, the harder analyzing with the conventional Sanger sequencing, resulted in lower ratios of dominant variants. Conclusions In sum, deep sequencing is better suited for detecting rare variants comprehensively. The simulation of Sanger sequencing that we propose here will also help to standardize quasispecies researching under different selection pressure based on next-generation sequencing data.