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Comparative studies of de novo assembly tools for next-generation sequencing technologies

机译:新一代测序技术从头组装工具的比较研究

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Motivation: Several new de novo assembly tools have been developed recently to assemble short sequencing reads generated by next-generation sequencing platforms. However, the performance of these tools under various conditions has not been fully investigated, and sufficient information is not currently available for informed decisions to be made regarding the tool that would be most likely to produce the best performance under a specific set of conditions.Results: We studied and compared the performance of commonly used de novo assembly tools specifically designed for next-generation sequencing data, including SSAKE, VCAKE, Euler-sr, Edena, Velvet, ABySS and SOAPdenovo. Tools were compared using several performance criteria, including N50 length, sequence coverage and assembly accuracy. Various properties of read data, including single-end/paired-end, sequence GC content, depth of coverage and base calling error rates, were investigated for their effects on the performance of different assembly tools. We also compared the computation time and memory usage of these seven tools. Based on the results of our comparison, the relative performance of individual tools are summarized and tentative guidelines for optimal selection of different assembly tools, under different conditions, are provided.
机译:动机:最近开发了几种新的从头组装工具,用于组装由下一代测序平台产生的短测序读段。但是,尚未对这些工具在各种条件下的性能进行充分的调查,并且目前尚没有足够的信息来做出有关在特定条件下最有可能产生最佳性能的工具的明智决策。 :我们研究并比较了专门为下一代测序数据设计的常用从头组装工具的性能,这些工具包括SSAKE,VCAKE,Euler-sr,Edena,Velvet,ABySS和SOAPdenovo。使用几种性能标准对工具进行了比较,包括N50长度,序列覆盖率和组装精度。研究了读取数据的各种属性,包括单端/成对末端,序列GC内容,覆盖深度和碱基检出错误率,这些属性对不同组装工具的性能产生影响。我们还比较了这七个工具的计算时间和内存使用情况。根据我们的比较结果,总结了各个工具的相对性能,并提供了在不同条件下最佳选择不同组装工具的暂行准则。

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