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ExpansionHunter Denovo:一种在短读测序数据中定位已知和新的重复扩展的计算方法。

ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data.

机构信息

Illumina Inc., 5200 Illumina Way, San Diego, CA, 92122, USA.

Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Australia.

出版信息

Genome Biol. 2020 Apr 28;21(1):102. doi: 10.1186/s13059-020-02017-z.

Abstract

Repeat expansions are responsible for over 40 monogenic disorders, and undoubtedly more pathogenic repeat expansions remain to be discovered. Existing methods for detecting repeat expansions in short-read sequencing data require predefined repeat catalogs. Recent discoveries emphasize the need for methods that do not require pre-specified candidate repeats. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide repeat expansion detection. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference repeat expansions not discoverable via existing methods.

摘要

重复扩展是 40 多种单基因疾病的致病原因,无疑还有更多的致病重复扩展有待发现。现有的短读测序数据中检测重复扩展的方法需要预定义的重复目录。最近的发现强调了需要不需要预指定候选重复的方法。为了解决这一需求,我们引入了 ExpansionHunter Denovo,这是一种用于全基因组重复扩展检测的高效无目录方法。对真实和模拟数据的分析表明,我们的方法可以识别出 44 个致病性重复中的 41 个大扩展,包括 9 个最近报道的现有方法无法发现的非参考重复扩展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a23/7187524/06586b78a248/13059_2020_2017_Fig1_HTML.jpg

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