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HIV Haplotype Inference Using a Propagating Dirichlet Process Mixture Model

机译:HIV单倍型推断使用传播Dirichlet过程混合模型

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This paper presents a new computational technique for the identification of HIV haplotypes. HIV tends to generate many potentially drug-resistant mutants within the HIV-infected patient and being able to identify these different mutants is important for efficient drug administration. With the view of identifying the mutants, we aim at analyzing short deep sequencing data called reads. From a statistical perspective, the analysis of such data can be regarded as a nonstandard clustering problem due to missing pairwise similarity measures between non-overlapping reads. To overcome this problem we propagate a Dirichlet Process Mixture Model by sequentially updating the prior information from successive local analyses. The model is verified using both simulated and real sequencing data.
机译:本文提出了一种新的计算技术,用于识别HIV单倍型。 HIV往往会在HIV感染的患者体内产生许多潜在的耐药突变体,因此能够识别出这些不同的突变体对于有效给药非常重要。为了识别突变体,我们旨在分析称为读取的短深测序数据。从统计角度来看,由于缺少非重叠读段之间的成对相似性度量,因此可以将此类数据的分析视为非标准聚类问题。为了克服这个问题,我们通过从连续的局部分析中顺序更新先验信息来传播Dirichlet过程混合模型。使用模拟和实际测序数据对模型进行验证。

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