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Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions

机译:基因组装配的纳米孔测序技术和工具:当前状态,瓶颈和未来方向的计算分析

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Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze themultiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, wemake four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, performsimilarly in terms of accuracy. However, Minimap has a lowermemory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assemblerMiniasmcan be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish.We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability.We conclude that our observations can guide researchers and practitioners inmaking conscious and effect
机译:纳米孔测序技术有可能使其他测序技术过时,其能够产生长读取并提供便携性。然而,技术的高误差率在产生准确的基因组组件时构成挑战。用于纳米孔序列分析的工具具有至关重要的重要性,因为它们应该克服技术的高误差率。我们在这项工作中的目标是全面分析当前公开可用的工具,以了解他们的优缺点和性能瓶颈。重要的是要了解当前工具不表现出更好的工具。为此,我们(1)使用纳米孔序列数据分析基因组组装管道中的主题步骤和相关工具,(2)提供用于确定每个步骤的适当工具的指导。基于我们的分析,Wemake四个关键观测:(1)基于克服的工具的选择在克服纳米孔测序技术的高误差速率时起着关键作用。 (2)阅读阅读重叠查找工具,GraphMap和Minimap,在准确性方面执行灵活。但是,Minimap具有LowerMemory使用,它比GraphMap更快。 (3)在决定装配步骤的适当工具时,在准确性和性能之间存在权衡。快速但不太精确的组件粘附量用于快速初始装配,并且可以在其顶部应用进一步的抛光以提高精度,从而导致更快的整体组装。 (4)最先进的抛光工具罗恩产生高质量共识序列,同时在另一个抛光工具,纳米诺波南诺波尔的纳米波隆中提供了显着的加速。我们分析了不同工具的各种组合,并在精度之间暴露权衡。性能,内存使用和可扩展性。我们得出结论,我们的观察可以指导研究人员和从业者制造意识和效果

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