光学映射法检测结构变异的最优抽样策略
A Note on Optimal Sampling Strategy for Structural Variant Detection Using Optical Mapping
摘要：
结构变异构成了人类遗传变异的大部分，但很难使用目前的基因组测序技术进行评估。光学定位技术，测量标记标记之间的染色体片段的大小，提供了一种替代方法。随着这些技术逐渐成熟成为临床工具，需要开发一种方法来确定用于对生物材料进行采样的最佳策略，以便在某个阈值下检测变体。在这里，我们开发了一种优化方法，使用一个简单的，但现实的，模型的基因组映射过程中使用超几何分布和{概率}浓度不等式。我们的方法是计算和分析上易于处理，并包括一个新的方法来获得超几何分布的尾部边界。我们表明，如果一个基因组定位技术可以在一个样本中的染色体片段的样本，相对较少的生物材料是需要检测到的变异在高置信度。
英文摘要：
Structural variants compose the majority of human genetic variation, but are difficult to assess using current genomic sequencing technologies. Optical mapping technologies, which measure the size of chromosomal fragments between labeled markers, offer an alternative approach. As these technologies mature towards becoming clinical tools, there is a need to develop an approach for determining the optimal strategy for sampling biological material in order to detect a variant at some threshold. Here we develop an optimization approach using a simple, yet realistic, model of the genomic mapping process using a hyper-geometric distribution and {probabilistic} concentration inequalities. Our approach is both computationally and analytically tractable and includes a novel approach to getting tail bounds of hyper-geometric distribution. We show that if a genomic mapping technology can sample most of the chromosomal fragments within a sample, comparatively little biological material is needed to detect a variant at high confidence.
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