Due to sequencing bias, sequencing error, and repeat problems, the genome assemblies usually contain misarrangements and gaps. When tackling these problems, current assemblers commonly consider the read libraries as a whole and adopt the same strategy to deal with them. However, if we can divide reads into different categories and take different assembly strategies for different read categories, we expect to reduce the mutual effects on problems in genome assembly and facilitate to produce satisfactory assemblies. In this paper, we present a new pipeline for genome assembly based on read classification (ARC). ARC classifies reads into three categories according to the frequencies of k-mers they contain. The three categories refer to (1) low depth reads, which contain a certain low frequency k-mers and are often caused by sequencing errors or bias; (2) high depth reads, which contain a certain high frequency k-mers and usually come from repetitive regions; and (3) normal depth reads, which are the rest of reads. After read classification, an existing assembler is used to assemble different read categories separately, which is beneficial to resolve problems in the genome assembly. ARC adopts loose assembly parameters for low depth reads, and strict assembly parameters for normal depth and high depth reads. We test ARC using five datasets. The experimental results show that, assemblers combining with ARC can generate better assemblies in terms of NA50, NGA50, and genome fraction.

中文翻译：

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基于读取分类改进从头组装。

由于测序偏倚，测序错误和重复问题，基因组装配体通常包含错配和缺口。解决这些问题时，当前的汇编程序通常将读取库作为一个整体来考虑，并采用相同的策略来处理它们。但是，如果我们可以将读段划分为不同的类别，并针对不同的读段采用不同的装配策略，那么我们希望减少对基因组装配问题的相互影响，并有助于产生令人满意的装配。在本文中，我们提出了一种基于阅读分类（ARC）的基因组组装新管道。ARC根据它们包含的k-mers的频率将读数分为三类。这三个类别涉及（1）低深度读取，其中包含一定的低频k-mers，通常是由于测序错误或偏倚引起的；（2）高深度读，包含一定的高频k聚体，通常来自重复区域；（3）正常深度读取，即其余的读取。在阅读分类之后，使用现有的汇编器分别组装不同的阅读类别，这对于解决基因组组装中的问题是有益的。ARC采用宽松的汇编参数来进行低深度读取，而采用严格的汇编参数来进行普通深度和高深度读取。我们使用五个数据集测试ARC。实验结果表明，结合ARC的汇编程序可以在NA50，NGA50和基因组分数方面产生更好的汇编。现有的汇编程序可用于分别汇编不同的阅读类别，这对于解决基因组组装中的问题非常有利。ARC采用宽松的汇编参数来进行低深度读取，而采用严格的汇编参数来进行普通深度和高深度读取。我们使用五个数据集测试ARC。实验结果表明，结合ARC的汇编程序可以在NA50，NGA50和基因组分数方面产生更好的汇编。现有的汇编程序可用于分别汇编不同的阅读类别，这对于解决基因组组装中的问题非常有利。ARC采用宽松的汇编参数来进行低深度读取，而采用严格的汇编参数来进行普通深度和高深度读取。我们使用五个数据集测试ARC。实验结果表明，结合ARC的汇编程序可以在NA50，NGA50和基因组分数方面产生更好的汇编。