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A Bayesian framework for de novo mutation calling in parents-offspring trios

机译:贝叶斯从头突变的父母-后代三重奏的框架

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Motivation: Spontaneous (de novo) mutations play an important role in the disease etiology of a range of complex diseases. Identifying de novo mutations (DNMs) in sporadic cases provides an effective strategy to find genes or genomic regions implicated in the genetics of disease. High-throughput next-generation sequencing enables genome- or exome-wide detection of DNMs by sequencing parents-proband trios. It is challenging to sift true mutations through massive amount of noise due to sequencing error and alignment artifacts. One of the critical limitations of existing methods is that for all genomic regions the same pre-specified mutation rate is assumed, which has a significant impact on the DNM calling accuracy.
机译:动机:自发(从头)突变在一系列复杂疾病的病因中起着重要作用。识别散发病例中的从头突变(DNM)提供了一种有效的策略,以寻找与疾病遗传相关的基因或基因组区域。高通量下一代测序可通过对亲子先证者三重奏进行测序来实现DNM的全基因组或外显子组检测。通过测序错误和比对伪像,通过大量噪声筛选真实突变是一项挑战。现有方法的关键局限性之一是,对于所有基因组区域,都假定使用相同的预先指定的突变率,这对DNM调用准确度有重大影响。

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