进化病原体种群的深度测序:应用、错误和生物信息学解决方案。

Kerensa McElroy, Torsten Thomas, Fabio Luciani
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引用次数: 82

摘要

深度测序利用下一代测序技术的高通量特性来生成种群样本,将个体读取的信息视为有意义的。本文综述了深度测序技术在病原菌进化中的应用。讨论了病毒学文献中开创性的深度测序研究,例如快速突变病原体丙型肝炎病毒和艾滋病毒的全基因组罗氏454测序分析。然后讨论了深度测序方法对细菌种群的扩展,包括新兴测序技术的影响。虽然深度测序在评估病原体种群的遗传结构和进化历史方面具有前所未有的潜力,但生物信息学方面的挑战仍然存在。我们总结了目前克服这些挑战的方法,特别是在测序错误的背景下检测低频变异和从短读段重建单个单倍型的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions.

Deep sequencing harnesses the high throughput nature of next generation sequencing technologies to generate population samples, treating information contained in individual reads as meaningful. Here, we review applications of deep sequencing to pathogen evolution. Pioneering deep sequencing studies from the virology literature are discussed, such as whole genome Roche-454 sequencing analyses of the dynamics of the rapidly mutating pathogens hepatitis C virus and HIV. Extension of the deep sequencing approach to bacterial populations is then discussed, including the impacts of emerging sequencing technologies. While it is clear that deep sequencing has unprecedented potential for assessing the genetic structure and evolutionary history of pathogen populations, bioinformatic challenges remain. We summarise current approaches to overcoming these challenges, in particular methods for detecting low frequency variants in the context of sequencing error and reconstructing individual haplotypes from short reads.

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A systematic search for discriminating sites in the 16S ribosomal RNA gene. Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions. Sialic acid utilization by Cronobacter sakazakii. Beginner's guide to comparative bacterial genome analysis using next-generation sequence data. An efficient rRNA removal method for RNA sequencing in GC-rich bacteria.
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