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有读书笔记Characterizing natural variation using next-generation sequencing technologies

热1 insidi 添加于 2010-4-9 13:05 | 2644 次阅读 | 0 个评论

  •  作 者

    Gilad Y, Pritchard JK, Thornton K

  •  摘 要

    Progress in evolutionary genomics is tightly coupled with the development of new technologies to collect high-throughput data. The availability of next-generation sequencing technologies has the potential to revolutionize genomic research and enable us to focus on a large number of outstanding questions that previously could not be addressed effectively. Indeed, we are now able to study genetic variation on a genome-wide scale, characterize gene regulatory processes at unprecedented resolution, and soon, we expect that individual laboratories might be able to rapidly sequence new genomes. However, at present, the analysis of next-generation sequencing data is challenging, in particular because most sequencing platforms provide short reads, which are difficult to align and assemble. In addition, only little is known about sources of variation that are associated with next-generation sequencing study designs. A better understanding of the sources of error and bias in sequencing data is essential, especially in the context of studies of variation at dynamic quantitative traits.

  •  详细资料

    • 关键词: Animals; Base Sequence; Evolution, Molecular; Gene Expression Profiling/methods; *Genetic Variation; Genome-Wide Association Study/*methods; Genomics/*methods; Humans; Sequence Analysis, RNA/*methods
    • 文献种类:期刊
    • 期刊名称: Trends in Genetics : TIG
    • 期刊缩写: Trends Genet
    • 期卷页: 2009年  第25卷 第10期 463-471页
    • 地址: Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. gilad@uchicago.edu
    • ISBN: 0168-9525
    • 备注:PMID:19801172

  •  标 签

    seminar 

  • 相关链接 DOI URL 

  •  insidi 的文献笔记  订阅

    【原创】

    本文是一篇介绍下一代测序技术与遗传变异分析的综述。系统的阐述了下一代测序技术带来的机遇，挑战及相关展望。

          进化基因组学的发展与新技术的开发来收集高通量数据是紧密相连的。下一代测序技术的可用将为基因组研究带来革新，且能是我们关注以前无法有效分析的许多问题。事实上，我们现在可以通过全基因组扫描来研究遗传变异，分析基因调控进程。不久，我们期待能在个别实验室中实现测序全基因序列。然而，目前，下一代测序数据的分析仍然面临挑战，尤其是大多数测序平台产生了短序列，这对比对和组装造成了困难。此外，下一代测序研究设计中变异的相关信息也所知甚少。对测序数据的错误及偏差进一步深入的理解是有必要的，尤其在数量性状变异研究中。

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