ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data
ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data
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DOI：
10.1186/s13059-020-02017-z
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发表时间：
2020-04-28
期刊：
影响因子：
12.3
通讯作者：
Eberle, Michael A.
中科院分区：
文献类型：
--
作者：
Dolzhenko, Egor;Bennett, Mark F.;Eberle, Michael A.
重复扩张是40多种单基因疾病的原因，毫无疑问，更多的致病性重复扩张仍有待发现。现有的用于检测短读测序数据中的重复扩展的方法需要预定义的重复目录。最近的发现强调了不需要预先指定的候选重复的方法的必要性。为了满足这一需求，我们引入了ExpansionHunter Denovo，这是一种高效的、无目录的全基因组重复扩增检测方法。对真实和模拟数据的分析表明，我们的方法可以识别44个致病重复序列中41个的大扩展，包括最近报道的9个用现有方法无法发现的非参考重复扩展。
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.