ArticleBriefings in bioinformatics2024
miniSNV: accurate and fast single nucleotide variant calling from nanopore sequencing data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Protocol for haplotype-resolved structural variant detection via long-read sequencing using cuteHap.STAR protocols · 2026Article
- HAlign-G: rapid and low-memory multiple-genome aligner for large-scale closely related genomes.Genome biology · 2025Article
- Indel calling from ONT sequencing data of family trios via sparse attention and 3D convolution.Briefings in bioinformatics · 2025Article
- Accurate and rapid single nucleotide variation detection inFrontiers in medicine · 2025Article
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Authors and funding
7 authors.
Funding
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.
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