Towards population-scale long-read sequencing.

Towards population-scale long-read sequencing.
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DOI:
10.1038/s41576-021-00367-3
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发表时间:
2021-09
期刊:
Nature reviews. Genetics
影响因子:
--
通讯作者:
Sedlazeck FJ
Sedlazeck FJ
中科院分区:
其他
文献类型:
--
作者:
De Coster W;Weissensteiner MH;Sedlazeck FJ

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长读测序技术现在已经达到了精确度和产量的水平,使它们能够应用于数万到数千个样本的变异检测。随着新的计算工具的发展,在过去两年中出现了第一批涉及长阅读测序的人口规模研究,鉴于该领域的不断进步,可能会有更多的研究相继出现。在这篇综述中,我们综述了人口规模长阅读测序的最新发展,强调了扩大方法的潜在挑战,并提供了关于实验设计的指导。我们概述了当前的长读测序平台、变种调用方法和从头开始程序集的方法以及基于引用的映射方法。此外,我们总结了变异验证、基因分型和预测功能影响的策略,并强调了在种群规模实现长阅读测序仍然存在的挑战。人口规模的长读测序带来了具体的挑战,但正在变得越来越容易获得。在这篇综述中,Sedlazeck和他的同事们讨论了主要的平台和分析工具,项目设计中的考虑因素,以及将长阅读测序扩展到人群中的挑战。
Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale. Long-read sequencing at the population scale presents specific challenges but is becoming increasingly accessible. In this Review, Sedlazeck and colleagues discuss the major platforms and analytical tools, considerations in project design and challenges in scaling long-read sequencing to populations.
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