Sparse diploid spatial biosignal recovery for genomic variation detection
Sparse diploid spatial biosignal recovery for genomic variation detection
复制标题
用于基因组变异检测的稀疏二倍体空间生物信号恢复
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Roummel F. Marcia
中科院分区：
文献类型：
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作者：
Mario Banuelos;Lasith Adhikari;R. Almanza;Andrew Fujikawa;Jonathan Sahagun;Katharine Sanderson;M. Spence;Suzanne S. Sindi;Roummel F. Marcia
结构变异（SVs）——如重复、缺失和倒置——是个体基因组相对于给定参考基因的重排。检测SV的常用方法是对个体基因组片段进行测序，将其映射到适当的参考位点，并通过识别不一致的映射，预测SV的位置和类型。然而，测序和绘图过程中的错误将导致看起来像SVs的信号，从而导致不准确的预测。此外，即使存在SV的证据，由于测序覆盖范围的差异，确定个体的一条或两条染色体上是否存在SV是具有挑战性的。在我们的工作中，我们试图在三个方面改进SV检测的标准方法。首先，为了减少假阳性预测，我们使用继承属性来约束可能的SVs空间，同时预测父节点和子节点的SVs。其次，我们预测一个变异是纯合的（SV在两条染色体上）还是杂合的（SV在一条染色体上）。第三，我们利用基于梯度的优化方法，并使用促进稀疏性的1惩罚约束我们的解决方案（因为SV实例应该很少）。我们展示了我们的计算方法在模拟基因组以及来自1000基因组计划的亲子三人组上的改进性能。
Structural variants (SVs) - such as duplications, deletions and inversions - are rearrangements of an individual's genome relative to a given reference. The common method for detection of SVs is to sequence fragments from an individual's genome, map them to the appropriate reference and, by identifying discordant mappings, predict the locations and type of SV. However, errors in both the sequencing and mapping process will result in signals that look like SVs, resulting in inaccurate predictions. In addition, because of variation in sequencing coverage even when the evidence of an SV is present, determining if an individual has the SV present on one or both of their chromosomes is challenging. In our work, we seek to improve upon standard methods for SV detection in three ways. First, to reduce false-positive predictions, we simultaneously predict SVs in a parent and child using properties of inheritance to constrain the space of possible SVs. Second, we predict if a variant is homozygous (SV is on two chromosomes) or heterozygous (SV is on one chromosome). Third, we utilize a gradient-based optimization approach and constrain our solution with a sparsity-promoting ℓ1 penalty (since SV instances should be rare). We demonstrate the improved performance of our computational approach on both simulated genomes as well as a parent-child trio from the 1000 Genomes Project.
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
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通讯作者：
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影响因子：
7
作者：
Quinlan, Aaron R.;Clark, Royden A.;Hall, Ira M.
通讯作者：
Hall, Ira M.