miniSNV: accurate and fast single nucleotide variant calling from nanopore sequencing data

• Published in: Briefings in bioinformatics 2024-09, Vol.25 (6)
• Main Authors: Cui, Miao, Liu, Yadong, Yu, Xian, Guo, Hongzhe, Jiang, Tao, Wang, Yadong, Liu, Bo
• Format: Article
• Language: English
• Subjects: Algorithms; Benchmarks; Error detection; Genotypes; Haplotypes; High-Throughput Nucleotide Sequencing - methods; Humans; Nanopore Sequencing - methods; Nucleotides; Polymorphism, Single Nucleotide; Sequence Analysis, DNA - methods; Software; State of the art
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae473

Abstract Nanopore sequence technology has demonstrated a longer read length and enabled to potentially address the limitations of short-read sequencing including long-range haplotype phasing and accurate variant calling. However, there is still room for improvement in terms of the performance of single nucleotide variant (SNV) identification and computing resource usage for the state-of-the-art approaches. In this work, we introduce miniSNV, a lightweight SNV calling algorithm that simultaneously achieves high performance and yield. miniSNV utilizes known common variants in populations as variation backgrounds and leverages read pileup, read-based phasing, and consensus generation to identify and genotype SNVs for Oxford Nanopore Technologies (ONT) long reads. Benchmarks on real and simulated ONT data under various error profiles demonstrate that miniSNV has superior sensitivity and comparable accuracy on SNV detection and runs faster with outstanding scalability and lower memory than most state-of-the-art variant callers. miniSNV is available from https://github.com/CuiMiao-HIT/miniSNV.