下一代测序数据的组装算法。
Assembly algorithms for next-generation sequencing data.
机构信息
J. Craig Venter Institute, Rockville, MD 20850-3343, USA.
出版信息
Genomics. 2010 Jun;95(6):315-27. doi: 10.1016/j.ygeno.2010.03.001. Epub 2010 Mar 6.
Abstract
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.
摘要
下一代测序平台的出现促使全基因组鸟枪法组装算法和软件的研究再次兴起。与 Sanger 测序数据相比，罗氏 454、Illumina/Solexa 和 ABI SOLiD 平台的 DNA 测序数据通常具有较短的读长、更高的覆盖率和不同的错误分布。自 2005 年以来，已经创建或专门修订了几个组装软件包，用于从头组装下一代测序数据。本综述总结并比较了名为 SSAKE、SHARCGS、VCAKE、Newbler、Celera Assembler、Euler、Velvet、ABySS、AllPaths 和 SOAPdenovo 的软件包的已发表描述。更一般地说，它比较了两种称为 de Bruijn 图方法和重叠/布局/共识方法的两种标准组装方法。
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