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Assembly algorithms for next-generation sequencing data.

机译:下一代测序数据的组装算法。

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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.
机译:下一代测序平台的出现导致对全基因组shot弹枪组装算法和软件的研究重新兴起。与Sanger测序数据相比,来自Roche 454,Illumina / Solexa和ABI SOLiD平台的DNA测序数据通常具有较短的读取长度,较高的覆盖率和不同的错误概况。自2005年以来,专门为从头组装下一代测序数据而创建或修订了多个组装软件包。这篇综述总结并比较了名为SSAKE,SHARCGS,VCAKE,Newbler,Celera Assembler,Euler,Velvet,ABySS,AllPaths和SOAPdenovo的软件包的发布说明。更一般而言,它比较了两种标准方法,即de Bruijn图方法和重叠/布局/共识方法进行组装。

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