Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction
Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction
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DOI：
10.1089/cmb.2009.0164
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发表时间：
2010-03-01
影响因子：
1.7
通讯作者：
Beerenwinkel, Niko
中科院分区：
文献类型：
--
作者：
Zagordi, Osvaldo;Geyrhofer, Lukas;Beerenwinkel, Niko
我们提出了一种用于分析从遗传多样性样品获得的深度测序数据的计算方法。从深度测序实验获得的读数集代表基础群体的统计样本。我们开发了一个生成概率模型，用于在存在测序错误的情况下将观察到的读段分配给未观察到的单倍型。该聚类问题以贝叶斯方式使用狄利克雷过程混合物来解决，以定义混合物中未知数量的单倍型的先验分布。我们设计了一个Gibbs采样器，用于从单倍型序列的联合后验分布、单倍型的读段分配和测序过程的错误率中进行采样，以获得人口的局部单倍型结构的估计。在模拟数据和从HIV样品获得的实验深度测序数据上评估该方法。
We present a computational method for analyzing deep sequencing data obtained from a genetically diverse sample. The set of reads obtained from a deep sequencing experiment represents a statistical sample of the underlying population. We develop a generative probabilistic model for assigning observed reads to unobserved haplotypes in the presence of sequencing errors. This clustering problem is solved in a Bayesian fashion using the Dirichlet process mixture to define a prior distribution on the unknown number of haplotypes in the mixture. We devise a Gibbs sampler for sampling from the joint posterior distribution of haplotype sequences, assignment of reads to haplotypes, and error rate of the sequencing process, to obtain estimates of the local haplotype structure of the population. The method is evaluated on simulated data and on experimental deep sequencing data obtained from HIV samples.