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工具评估用于检测下一代全基因组和靶向测序数据中的可变大小插入缺失。

Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data.

机构信息

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Department of Pathology, Laboratory Division, Turku University Hospital, Turku, Finland.

出版信息

PLoS Comput Biol. 2022 Feb 17;18(2):e1009269. doi: 10.1371/journal.pcbi.1009269. eCollection 2022 Feb.

Abstract

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.

摘要

插入和缺失(indels)在人类基因组中与广泛的表型相关,包括各种临床疾病。高通量、下一代测序(NGS)技术能够检测到短的遗传变异,如单核苷酸变异(SNVs)和 indels。然而,indels 的变异调用准确性仍然明显低于 SNVs。在这里,我们通过广泛的 NGS 数据集评估了用于 indel 调用的变异调用工具的性能,并进行了比较研究。虽然没有一种单一的最佳工具适合所有情况,但我们的结果表明,变异调用工具的选择极大地影响了 indel 调用的准确性和召回率。此外,为了可靠地检测 indels,必须选择提供长读长和高覆盖率的 NGS 技术,并结合特定的变异调用工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72ef/8916674/872a44227ac0/pcbi.1009269.g001.jpg

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