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局部读取单倍型标记可实现准确的长读长小变异检测。

Local read haplotagging enables accurate long-read small variant calling.

作者信息

Kolesnikov Alexey, Cook Daniel, Nattestad Maria, McNulty Brandy, Gorzynski John, Goenka Sneha, Ashley Euan A, Jain Miten, Miga Karen H, Paten Benedict, Chang Pi-Chuan, Carroll Andrew, Shafin Kishwar

机构信息

Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA.

UC Santa Cruz Genomics Institute, University of California, Santa Cruz, California, USA.

出版信息

bioRxiv. 2023 Sep 12:2023.09.07.556731. doi: 10.1101/2023.09.07.556731.

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 makes DeepVariant a universal variant calling solution for long-read sequencing platforms.

摘要

长读长测序技术能够在基因组中难以映射的区域进行变异检测,并在临床环境中实现快速基因诊断。像太平洋生物科学公司(PacBio)和牛津纳米孔技术公司(ONT)这样快速发展的第三代测序平台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检测方法可以利用长读长的局部单倍型信息来提高基因分型的准确性。然而,使用局部单倍型信息会带来额外开销,因为变异检测需要多次执行,这最终使得在新数据类型和平台出现时难以扩展应用。在这项工作中,我们开发了一种局部单倍型近似方法,该方法在包括PacBio Revio系统、ONT R10.4单工和双工数据在内的多个测序平台上实现了领先的变异检测性能。这种局部单倍型近似方法的加入使DeepVariant成为长读长测序平台的通用变异检测解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbb4/10515762/68b46fa3b40d/nihpp-2023.09.07.556731v1-f0001.jpg

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