Local read haplotagging enables accurate long-read small variant calling

• Published in: Nature communications Vol. 15; no. 1; pp. 5907 - 12
• Main Authors: Kolesnikov, Alexey, Cook, Daniel, Nattestad, Maria, Brambrink, Lucas, McNulty, Brandy, Gorzynski, John, Goenka, Sneha, Ashley, Euan A., Jain, Miten, Miga, Karen H., Paten, Benedict, Chang, Pi-Chuan, Carroll, Andrew, Shafin, Kishwar
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.07.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/794; Accuracy; Algorithms; Artificial neural networks; Duplex plating; Gene sequencing; Genetic screening; Genetic Variation; Genome, Human; Genotyping; Haplotypes; Haplotypes - genetics; High-Throughput Nucleotide Sequencing - methods; Humanities and Social Sciences; Humans; multidisciplinary; Neural networks; Neural Networks, Computer; Platforms; Polymorphism, Single Nucleotide; Science; Science (multidisciplinary); Sequence Analysis, DNA - methods; State of the art
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-50079-5

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. DNA variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve genotyping accuracy, however this increases computational complexity. Here the authors develop an approximate haplotagging method that simplifies the process and enables state-of-the-art variant calling performance with multiple sequencing platforms.