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

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae473
Miao Cui, Yadong Liu, Xian Yu, Hongzhe Guo, Tao Jiang, Yadong Wang, Bo Liu
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引用次数: 0

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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miniSNV:从纳米孔测序数据中准确快速地进行单核苷酸变异调用。
纳米孔测序技术具有更长的读数长度,有可能解决短读数测序的局限性,包括长程单倍型分期和准确的变异调用。然而,在单核苷酸变异(SNV)识别性能和计算资源使用方面,最先进的方法仍有改进的余地。miniSNV 利用人群中已知的常见变异作为变异背景,并利用读取堆积、基于读取的分期和共识生成来识别牛津纳米孔技术公司(ONT)长读取的 SNV 并对其进行基因分型。在各种误差情况下对真实和模拟 ONT 数据进行的基准测试表明,miniSNV 在 SNV 检测方面具有卓越的灵敏度和可比的准确性,而且与大多数最先进的变异调用程序相比,运行速度更快、可扩展性更强、内存更低。miniSNV 可从 https://github.com/CuiMiao-HIT/miniSNV 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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