Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data.
Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data.
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DOI：
10.3389/fgene.2021.761791
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发表时间：
2021
影响因子：
3.7
通讯作者：
Magi A
中科院分区：
文献类型：
--
作者：
Bolognini D;Magi A
结构变异（SVs）是涉及至少50个核苷酸的基因组重排，已知对人类健康有严重影响。虽然之前的短读测序技术经常被证明不足以全面评估结构变化，但最近来自牛津纳米孔技术的长读测序技术已经被证明对于发现大型SV非常宝贵，并且具有促进完整SV光谱分辨率的潜力。随着许多长读测序研究的开展，评估影响当前纳米孔测序数据的SV调用管道的因素至关重要。在这篇简短的研究报告中，我们使用真实的和合成的纳米孔数据集，评估和比较了四种长读对齐器中五个长读SV调用器的性能。我们特别关注了读比对、测序覆盖率和变异等位基因深度对不同类型和大小范围的SV检测和基因分型的影响，并通过整合各种长读比对者和SV呼叫者产生的SV呼叫集的精度和召回率提供了见解。我们提出的计算管道可以在https://github.com/davidebolo1993/EViNCe上公开获得，并且可以调整以进一步评估未来的纳米孔测序数据集。
Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.
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DOI：
10.1038/s41576-021-00367-3
发表时间：
2021-09
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
De Coster W;Weissensteiner MH;Sedlazeck FJ
通讯作者：
Sedlazeck FJ
影响因子：
12.3
作者：
Jain M;Olsen HE;Paten B;Akeson M
通讯作者：
Akeson M
影响因子：
5.8
作者：
Li, Heng
通讯作者：
Li, Heng
影响因子：
9.2
作者：
Bolognini D;Magi A;Benes V;Korbel JO;Rausch T
通讯作者：
Rausch T
影响因子：
12.3
作者：
Jiang, Tao;Liu, Yongzhuang;Wang, Yadong
通讯作者：
Wang, Yadong