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链接用于准确对准错误的长读数到非循环变化图的错误长读数

Jun Ma¹, Manuel Cáceres¹, Leena Salmela¹

¹Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland.

Bioinformatics (Oxford, England)

|July 26, 2023

概括

GraphChainer通过同线性链接多个种子来改善长读对齐到泛基因组学变异图. 与现有方法相比,这种新的对齐器显著增加了对齐读数的数量和长度.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

将序列阅读与变异图对齐在泛基因组学中对于变异调用等应用至关重要.
像vg这样的现有工具在短时间读取中很受欢迎,而GraphAligner则是长时间错误读取的最先进工具.
GraphAligner的种子扩展方法可以通过共同线性链接多个种子来改进.

研究的目的:

介绍 GraphChainer,这是一个新的算法和对齐器,用于在非循环变化图中同线性链接种子.
有效地将错误的长读数与复杂的泛基因组变异图进行对齐.

主要方法:

开发了一种新的算法,用于在字符串标记的非循环图中对种子进行同线性链接.
在 GraphChainer 调整器中实现了一种高效的同线性链接算法.
在真实和模拟的PacBio CLR上评估GraphChainer的读数具有不同的错误率.

主要成果:

在人类泛基因组数据上,GraphChainer比GraphAligner对准了12-17%的阅读量和21-28%的总阅读长度.
在模拟和真实数据上实现了95-99%的读取和总读取长度对齐.
与微图和微链相比,表现出更高的性能,其精度为<60%.

结论:

GraphChainer代表了长读对应变量图的显著进步.
同线性链接方法提高了对齐的准确性和完整性.
GraphChainer为泛基因组分析提供了一个更强大,更有效的解决方案.

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