美国贝勒医学院Fritz J. Sedlazeck研究团队利用Sniffles2检测镶嵌变异和群体级结构变异。2024年1月2日，《自然—生物技术》杂志在线发表了这项成果。

研究人员介绍了Sniffles2，它通过实施重复感知聚类，结合快速共识序列和覆盖率自适应过滤，改进了目前的方法。在不同的覆盖率（5-50×）、测序技术（ONT和HiFi）和结构变异（SV）类型下，Sniffles2比最先进的SV寻找器快11.8倍，准确率高 29%。此外，Sniffles2还解决了从家系级到人群级SV寻找的问题，从而生成完全基因分型的VCF文件。

研究人员在11个受试者中准确鉴定出了围绕MECP2的致病SV，包括具有三个重叠SV的高度复杂等位基因。Sniffles2还能在批量长读数数据中检测镶嵌SV。因此，研究人员在一名多系统萎缩患者的脑组织中发现了多个镶嵌SV。鉴定出的SV在扣带回皮层中显示出显著的多样性，既影响神经元功能相关基因，也影响重复元件。

据悉，寻找SV在技术上具有挑战性，但使用长读数仍然是识别复杂基因组改变的最准确方法。

附：英文原文

Title: Detection of mosaic and population-level structural variants with Sniffles2

Author: Smolka, Moritz, Paulin, Luis F., Grochowski, Christopher M., Horner, Dominic W., Mahmoud, Medhat, Behera, Sairam, Kalef-Ezra, Ester, Gandhi, Mira, Hong, Karl, Pehlivan, Davut, Scholz, Sonja W., Carvalho, Claudia M. B., Proukakis, Christos, Sedlazeck, Fritz J.

Issue&Volume: 2024-01-02

Abstract: Calling structural variations (SVs) is technically challenging, but using long reads remains the most accurate way to identify complex genomic alterations. Here we present Sniffles2, which improves over current methods by implementing a repeat aware clustering coupled with a fast consensus sequence and coverage-adaptive filtering. Sniffles2 is 11.8 times faster and 29% more accurate than state-of-the-art SV callers across different coverages (5–50×), sequencing technologies (ONT and HiFi) and SV types. Furthermore, Sniffles2 solves the problem of family-level to population-level SV calling to produce fully genotyped VCF files. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we identified multiple mosaic SVs in brain tissue from a patient with multiple system atrophy. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements.

DOI: 10.1038/s41587-023-02024-y

Source: https://www.nature.com/articles/s41587-023-02024-y