RepAHR: An improved approach for de novo repeat identication by assembly of the high-frequency reads
RepAHR: An improved approach for de novo repeat identication by assembly of the high-frequency reads
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RepAHR：通过组装高频读数进行从头重复识别的改进方法
DOI：
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发表时间：
2020
期刊：
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通讯作者：
Jianxin Wang
中科院分区：
文献类型：
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作者：
Xingyu Liao;Xin Gao;Xiankai Zhang;Fang-Xiang Wu;Jianxin Wang
背景：重复序列在真核生物基因组中占很大比例。重复序列的识别在许多情况下起着重要的作用。应用，如结构变异检测和基因组组装。很多。现有的从头重复识别管道或工具使用的组装。高频k-mers获得重复。但是，有一定的顺序。装配人员需要覆盖才能获得所需的组件。另一方面。另一方面，组装者将reads切割成更短的k-mers进行组装，这可能会破坏重复区域的结构。基于上述原因，它是。难以获得完整和准确的基因组重复区域。结果：本研究提出了一种新的从头重复序列鉴定方法。通过组装高频读取（RepAHR）。首先，RepAHR进行扫描。下一代测序（NGS）读取高频k-mers。其次,。RepAHR根据高频k-mer的一定规则，从整个NGS reads中剔除高频reads。第三，高频读取是。使用SPAdes组装以生成重复，这被认为是一个。结论：我们在5个数据集上对RepAHR进行了测试，结果与实验结果一致。RepAHR在检测重复序列方面优于RepARK和REPdenovo。在N50方面，基准对准比，基准覆盖比，掩模比。Repbase和其他一些指标。
Background: Repetitive sequences account for a large proportion of eukaryotes.genomes. Identication of repetitive sequences plays a signicant role in many.applications, such as structural variation detection and genome assembly. Many.existing de novo repeat identication pipelines or tools make use of assembly of.high-frequency k-mers to obtain repeats. However, a certain degree of sequence.coverage is required for assemblers to get the desired assemblies. On the other.hand, assemblers cut the reads into shorter k-mers for assembly, which may.destroy the structure of the repetitive regions. For the above reasons, it is.dicult to obtain complete and accurate repetitive regions in the genome by.using existing tools..Result: In this study, we present a new method for de novo repeat identication.by assembly of high-frequency reads (RepAHR). Firstly, RepAHR scans.next-generation sequencing (NGS) reads to nd high-frequency k-mers. Secondly,.RepAHR lters high-frequency reads from whole NGS reads according to certain.rules based on high-frequency k-mer. Thirdly, the high-frequency reads are.assembled to generate repeats using SPAdes, which is considered as an.outstanding genome assembler with NGS sequences..Conlusion: We test RepAHR on ve data sets, and the experimental results.show that RepAHR outperforms RepARK and REPdenovo for detecting repeats.in terms of N50, reference alignment ratio, cover ratio of reference, mask ratio of.Repbase and some other metrics.