在无匹配对照的情况下，可精确检测测序数据中的镶嵌变异，这一成果由美国哈佛医学院Peter J. Park小组取得。相关论文2020年1月6日在线发表在国际学术期刊《自然—生物技术》上。

研究人员引入了一种新的机器学习算法MosaicForecast，其利用读取定相和读取级别功能来准确检测镶嵌单核苷酸变异和插入缺失，与现有算法相比其特异性提高了数倍。利用单细胞测序和靶向测序，研究人员验证了在人脑全基因组测序数据中检测到的80–90％的镶嵌单核苷酸变异和60–80％的插入缺失。该方法有助于阐明体细胞中镶嵌突变对疾病起源和发展的影响。

研究人员表示，检测正常发育过程中发生的镶嵌突变具有一定的挑战性，因为这种突变通常仅存在于一小部分细胞中，并且没有用于清除种系变体和系统伪像的明确匹配对照。

附：英文原文

Title: Accurate detection of mosaic variants in sequencing data without matched controls

Author: Yanmei Dou, Minseok Kwon, Rachel E. Rodin, Isidro Corts-Ciriano, Ryan Doan, Lovelace J. Luquette, Alon Galor, Craig Bohrson, Christopher A. Walsh, Peter J. Park

Issue&Volume: 2020-01-06

Abstract: Detection of mosaic mutations that arise in normal development is challenging, as such mutations are typically present in only a minute fraction of cells and there is no clear matched control for removing germline variants and systematic artifacts. We present MosaicForecast, a machine-learning method that leverages read-based phasing and read-level features to accurately detect mosaic single-nucleotide variants and indels, achieving a multifold increase in specificity compared with existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80–90% of the mosaic single-nucleotide variants and 60–80% of indels detected in human brain whole-genome sequencing data. Our method should help elucidate the contribution of mosaic somatic mutations to the origin and development of disease.

DOI: 10.1038/s41587-019-0368-8

Source: https://www.nature.com/articles/s41587-019-0368-8