Biomedical signal recovery: Genomic variant detection in family lineages
Biomedical signal recovery: Genomic variant detection in family lineages
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生物医学信号恢复：家族谱系中的基因组变异检测
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;R. Almanza;Lasith Adhikari;Suzanne S. Sindi;Roummel F. Marcia
结构变异（SV）-基因组重排，如插入，缺失和重复-代表一类重要的基因组变异。这些突变与两种遗传疾病（例如，癌症）和促进遗传多样性。检测未知基因组中SV的常见方法包括对基因组片段进行测序，将其与参考基因组进行比较，并基于鉴定的不一致片段预测SV。然而，由于DNA测序过程中存在错误和偏差以及将序列与参考基因组比对的问题，从传统DNA测序中检测SV具有挑战性。大多数现有方法使用层次关系来检测这些遗传变化，但通常对这些信息进行后处理。我们的工作旨在从三个方面改进现有的SV检测方法：第一，我们使用连续松弛的容许解来应用基于梯度的优化技术。其次，由于SV是罕见的，我们纳入了l\稀疏促进惩罚项。第三，我们通过使用块坐标下降方法来预测个体家族中的变体来改进我们以前的工作。我们证明了我们的方法的有效性，对各种模拟数据集和真实的基因组的双亲两个孩子的家庭。
Structural variations (SVs) — genomic rearrangements such as insertions, deletions and duplications — represent an important class of genomic variation. These mutations have been associated with both genetic diseases (e.g., cancer) and promoting genetic diversity. The common approach to detecting SVs in an unknown genome involves sequencing fragments of the genome, comparing them to a reference genome, and predicting SVs based on identified discordant fragments. However, detecting SVs from traditional DNA sequencing is challenging due to the presence of errors and biases in the DNA sequencing process as well as problems aligning sequences to a reference genome. The majority of existing methods use hierarchical relationships to detect these genetic changes, but often post-process this information. Our work aims to improve on existing SV detection methods in three ways: First, we use a continuous relaxation of admissible solutions to apply gradient-based optimization techniques. Second, since SVs are rare, we incorporate an l\ sparsity-promoting penalty term. Third, we improve on our previous work by using a block-coordinate descent approach to predict variants in families of individuals. We demonstrate the effectiveness of our method on a variety of simulated datasets and real genomes of a two parent-two child family.
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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