Local read haplotagging enables accurate long-read small variant calling

Alexey Kolesnikov, Daniel Cook, Maria Nattestad, Lucas Brambrink, Brandy McNulty, John Gorzynski, Sneha Goenka, Euan A. Ashley, Miten Jain, Karen H. Miga, Benedict Paten, Pi-Chuan Chang, Andrew Carroll () and Kishwar Shafin ()
Additional contact information
Alexey Kolesnikov: Google Inc
Daniel Cook: Google Inc
Maria Nattestad: Google Inc
Lucas Brambrink: Google Inc
Brandy McNulty: University of California
John Gorzynski: Stanford University
Sneha Goenka: Stanford University
Euan A. Ashley: Stanford University
Miten Jain: Northeastern university
Karen H. Miga: University of California
Benedict Paten: University of California
Pi-Chuan Chang: Google Inc
Andrew Carroll: Google Inc
Kishwar Shafin: Google Inc

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-50079-5

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