Nonconvex regularization for sparse genomic variant signal detection
Nonconvex regularization for sparse genomic variant signal detection
复制标题
用于稀疏基因组变异信号检测的非凸正则化
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
最近的研究表明，绝大多数人类都有基因组结构变异（SV）：基因组中区域的重排，如倒位，插入，缺失和重复。检测未知基因组中SV的标准方法包括对来自所讨论的基因组的配对读段进行测序，将它们映射到参考基因组，并分析所得片段的构型以获得重排的证据。由于SV在人类基因组中相对不频繁地出现，并且错误的读段映射可能暗示SV的存在，SV检测的方法通常遭受高的假阳性率。我们的方法旨在通过两种方式更准确地区分真假SV：首先，我们求解由负泊松对数似然目标函数组成的约束优化方程，该目标函数具有促进稀疏性的附加惩罚项。第二，我们同时分析多个相关的个体，并实施家庭约束。也就是说，我们要求在儿童中预测的任何SV都存在于他们的父母之一中。我们的问题公式降低了假阳性率，尽管大量的错误，从DNA测序和映射。通过结合额外的信息，我们改进了我们的模型配方，提高了SV预测方法的准确性。
Recent research suggests an overwhelming proportion of humans have genomic structural variants (SVs): rearrangements of regions in the genome such as inversions, insertions, deletions and duplications. The standard approach to detecting SVs in an unknown genome involves sequencing paired-reads from the genome in question, mapping them to a reference genome, and analyzing the resulting configuration of fragments for evidence of rearrangements. Because SVs occur relatively infrequently in the human genome, and erroneous read-mappings may suggest the presence of an SV, approaches to SV detection typically suffer from high false-positive rates. Our approach aims to more accurately distinguish true from false SVs in two ways: First, we solve a constrained optimization equation consisting of a negative Poisson log-likelihood objective function with an additive penalty term that promotes sparsity. Second, we analyze multiple related individuals simultaneously and enforce familial constraints. That is, we require any SVs predicted in children to be present in one of their parents. Our problem formulation decreases the false positive rate despite a large amount of error from both DNA sequencing and mapping. By incorporating additional information, we improve our model formulation and increase the accuracy of SV prediction methods.
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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