SLR: a scaffolding algorithm based on long reads and contig classification
SLR: a scaffolding algorithm based on long reads and contig classification
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SLR：一种基于长读长和重叠群分类的支架算法
DOI：
10.1186/s12859-019-3114-9
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发表时间：
2019-10-30
影响因子：
3
通讯作者：
Yan, Chaokun
中科院分区：
文献类型：
--
作者：
Luo, Junwei;Lyu, Mengna;Yan, Chaokun
背景：支架构建是基因组组装中的一个重要步骤，它对组装器产生的重叠群进行排序和定向。然而，重叠群中的重复区域通常会阻碍支架构建产生准确的结果。如何解决重复区域的问题受到了大量关注。在过去几年中，第三代测序技术（太平洋生物科学公司和牛津纳米孔公司）测序的长读长已被证明对基因组中重复区域的测序有用。尽管已经提出了一些基于长读长的独立支架构建算法，但支架构建仍然需要一种新策略来充分利用长读长的特性。
结果：在此，我们提出了一种基于长读长和重叠群分类的新型支架构建算法（SLR）。通过长读长和重叠群的比对信息，SLR将重叠群分为独特重叠群和模糊重叠群，以解决重复区域的问题。接下来，SLR仅使用独特重叠群来生成支架草图。然后，SLR将模糊重叠群插入到支架草图中并生成最终的支架。我们使用太平洋生物科学公司和牛津纳米孔技术测序的长读长数据集，将SLR与三种流行的支架构建工具进行比较。实验结果表明，SLR在准确性和完整性方面能够产生更好的结果。SLR的开源代码可在https://github.com/luojunwei/SLR获取。
结论：在本文中，我们描述了SLR，它旨在使用长读长对重叠群进行支架构建。我们得出结论，SLR能够提高基因组组装的完整性。
BackgroundScaffolding is an important step in genome assembly that orders and orients the contigs produced by assemblers. However, repetitive regions in contigs usually prevent scaffolding from producing accurate results. How to solve the problem of repetitive regions has received a great deal of attention. In the past few years, long reads sequenced by third-generation sequencing technologies (Pacific Biosciences and Oxford Nanopore) have been demonstrated to be useful for sequencing repetitive regions in genomes. Although some stand-alone scaffolding algorithms based on long reads have been presented, scaffolding still requires a new strategy to take full advantage of the characteristics of long reads.ResultsHere, we present a new scaffolding algorithm based on long reads and contig classification (SLR). Through the alignment information of long reads and contigs, SLR classifies the contigs into unique contigs and ambiguous contigs for addressing the problem of repetitive regions. Next, SLR uses only unique contigs to produce draft scaffolds. Then, SLR inserts the ambiguous contigs into the draft scaffolds and produces the final scaffolds. We compare SLR to three popular scaffolding tools by using long read datasets sequenced with Pacific Biosciences and Oxford Nanopore technologies. The experimental results show that SLR can produce better results in terms of accuracy and completeness. The open-source code of SLR is available at https://github.com/luojunwei/SLR.ConclusionIn this paper, we describes SLR, which is designed to scaffold contigs using long reads. We conclude that SLR can improve the completeness of genome assembly.