PDF(Pubmed)
Abstract：
Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.
摘要：
长读测序技术已经使得能够在基因组的难以定位的区域中进行变异检测,并且使得能够在临床环境中进行快速遗传诊断。快速发展的第三代测序平台，如太平洋生物科学（PacBio）和牛津纳米孔技术（ONT），正在引入更新的平台和数据类型。已经证明，基于深度神经网络的变异识别方法可以使用具有长读数的局部单倍型信息来提高基因分型的准确性。然而,使用本地单倍型信息会产生开销，因为变体调用需要执行多次，这最终使得在引入新的数据类型和平台时很难扩展到新的数据类型和平台。在这项工作中,我们已经开发了一种局部单倍型近似方法，该方法可以通过多个测序平台（包括PacBioRevio系统）实现最先进的变体调用性能，ONTR10.4单工和双工数据。这种局部单倍型近似的添加简化了DeepVariant的长阅读变体调用。
