结构变异基因分型的长读序列序列K-mer分析

IF 15.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Adam C. English, Fabio Cunial, Ginger A. Metcalf, Richard A. Gibbs, Fritz J. Sedlazeck
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引用次数: 0

摘要

准确分型结构变异(SV)等位基因是基因组学研究的重要内容。我们提出了一种新的SV基因分型方法(kanpig),该方法利用变异图和k-mer载体快速生成准确的SV基因型。对最新的SV数据集进行基准测试显示,kanpig实现了82.1%的单样本基因分型一致性,显著优于现有工具的平均水平66.3%。我们通过对47个遗传多样性样本的测试,探索了kanpig在多样本项目中的应用,发现了kanpig准确的基因型复合位点(例如,SVs邻近其他SVs),并且比其他工具产生了更高的基因型一致性。Kanpig只需要43秒就能处理单个样品的20倍长读,可以在PacBio或Oxford Nanopore长读上运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

K-mer analysis of long-read alignment pileups for structural variant genotyping

K-mer analysis of long-read alignment pileups for structural variant genotyping

Accurately genotyping structural variant (SV) alleles is crucial to genomics research. We present a novel method (kanpig) for genotyping SVs that leverages variant graphs and k-mer vectors to rapidly generate accurate SV genotypes. Benchmarking against the latest SV datasets shows kanpig achieves a single-sample genotyping concordance of 82.1%, significantly outperforming existing tools, which average 66.3%. We explore kanpig’s use for multi-sample projects by testing on 47 genetically diverse samples and find kanpig accurately genotypes complex loci (e.g. SVs neighboring other SVs), and produces higher genotyping concordance than other tools. Kanpig requires only 43 seconds to process a single sample’s 20x long-reads and can be run on PacBio or Oxford Nanopore long-reads.

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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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