Assembly algorithms for next-generation sequencing data.

Assembly algorithms for next-generation sequencing data.
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DOI:
10.1016/j.ygeno.2010.03.001
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发表时间:
2010-06
期刊:
影响因子:
4.4
通讯作者:
Sutton, Granger
Sutton, Granger
中科院分区:
生物学3区
文献类型:
--
作者:
Miller, Jason R.;Koren, Sergey;Sutton, Granger

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下一代测序平台的出现导致了全基因组鸟枪组装算法和软件研究的复兴。与Sanger测序数据相比,来自Roche 454、Illumina/Solexa和ABI Solexa固体平台的DNA测序数据通常呈现更短的读取长度、更高的覆盖率和不同的误差分布。自2005年以来,已经专门为下一代测序数据的从头组装创建或修订了几个组装软件包。本文对已发表的SSAKE、SHARCGS、VCAKE、Newbler、Celera ASSEMBLER、Euler、Velet、Abyss、AllPath和SOAPdenovo包的描述进行了总结和比较。更广泛地说,它比较了两种标准方法,即De Bruijn图法和重叠/布局/共识组装方法。
The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly.
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