Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment.
Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment.
复制标题
火神：通过双模式对齐改进了长读映射和结构变体调用。
DOI：
10.1093/gigascience/giab063
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发表时间：
2021-09-24
期刊：
影响因子：
9.2
通讯作者：
Treangen TJ
中科院分区：
文献类型：
--
作者：
Fu Y;Mahmoud M;Muraliraman VV;Sedlazeck FJ;Treangen TJ
长读段测序使得对整个人类基因组结构变异的调查成为可能。为了最大限度地发挥长读序测序在这方面的潜力，出现了主要关注速度或准确性的新型作图方法。在广泛使用的读段映射器（minimap2和NGMLR）中已经实施了各种分析和评分方案，以优化速度或准确性，其在不同的基因组区域和特定的结构变体中具有可变的性能。我们的假设是，将读段映射限制为使用跨不同突变热点的单个空位罚分降低了读段比对准确性并阻碍了结构变体检测。我们通过实施一个名为Vulcan的读取映射管道来测试我们的假设，该管道使用两种不同的空位罚分模式，我们称之为双模对齐。高层次的想法是，Vulcan通过minimap2利用映射读数的计算归一化编辑距离来识别对齐不良的读数，并使用更准确但计算成本更高的长读段映射器（NGMLR）重新对齐它们。为了支持我们的假设，我们表明Vulcan改进了模拟和真实的数据集的牛津纳米孔技术长读段的比对。这些改进反过来导致与单独的读段映射方法相比，对人类基因组数据集的结构变体调用性能的准确性提高。Vulcan是第一个结合了两种不同空位罚分模式的长读段映射框架，用于提高结构变体的查全率和查准率。Vulcan是开源的，可以在https://gitlab.com/treangenlab/vulcan上根据MIT许可证获得。
Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection. We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone. Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.
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影响因子：
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作者：
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DOI：
10.1038/s41576-021-00367-3
发表时间：
2021-09
期刊：
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