Sparse signal recovery methods for variant detection in next-generation sequencing data
Sparse signal recovery methods for variant detection in next-generation sequencing data
复制标题
用于下一代测序数据变异检测的稀疏信号恢复方法
DOI：
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发表时间：
2016
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;R. Almanza;Lasith Adhikari;Suzanne S. Sindi;Roummel F. Marcia
高通量测序技术的最新进展已经导致了大量基因组数据的收集。结构变异（SV）-基因组的重排，大于一个字母，如倒位，插入，缺失和重复-是遗传变异的重要来源，并与一些遗传疾病有关。然而，从测序数据推断SV已被证明是具有挑战性的，因为真正的SV是罕见的，并且容易受到低覆盖噪声的影响。在本文中，我们试图减轻，低覆盖率的序列的有害影响，以下的最大似然SV预测方法。具体来说，我们使用泊松统计对噪声进行建模，并使用稀疏促进的101惩罚来约束解决方案，因为SV实例应该是罕见的。此外，由于后代SV从其父母继承SV，因此我们在优化问题公式中纳入家族关系以增加检测到真实SV发生的可能性。数值结果验证了我们提出的方法。
Recent advances in high-throughput sequencing technologies, have led to the collection of vast quantities of genomic data., Structural variants (SVs) - rearrangements of the genome, larger than one letter such as inversions, insertions, deletions, and duplications - are an important source of genetic, variation and have been implicated in some genetic diseases., However, inferring SVs from sequencing data has proven to, be challenging because true SVs are rare and are prone to, low-coverage noise. In this paper, we attempt to mitigate the, deleterious effects of low-coverage sequences by following a, maximum likelihood approach to SV prediction. Specifically, we model the noise using Poisson statistics and constrain, the solution with a sparsity-promoting ℓ1 penalty since SV, instances should be rare. In addition, because offspring SVs, inherit SVs from their parents, we incorporate familial relationships, in the optimization problem formulation to increase, the likelihood of detecting true SV occurrences. Numerical, results are presented to validate our proposed approach.
影响因子：
5.6
作者：
Pal LR;Moult J
通讯作者：
Moult J
影响因子：
7
作者：
Quinlan, Aaron R.;Clark, Royden A.;Hall, Ira M.
通讯作者：
Hall, Ira M.