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首页> 外文期刊>Journal of the royal statistical society >PairClone: a Bayesian subclone caller based on mutation pairs
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PairClone: a Bayesian subclone caller based on mutation pairs

机译：PairClone：基于突变对的贝叶斯子克隆调用方

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摘要

Tumour cell populations can be thought of as a composition of heterogeneous cell subpopulations, with each subpopulation being characterized by overlapping sets of single-nucleotide variants. Such subpopulations are known as subclones and are an important target for precision medicine. Reconstructing subclones from next generation sequencing data is one of the major challenges in computational biology. We present PairClone as a new tool to implement this reconstruction. The main idea of PairClone is to model short reads mapped to pairs of proximal single-nucleotide variants, which we refer to as mutation pairs. In contrast, other existing methods use only marginal reads for unpaired single-nucleotide variants. Using Bayesian non-parametric models, we estimate posterior probabilities of the number, genotypes and population frequencies of subclones in one or more tumour sample. We use the categorical Indian buffet process as a prior probability model for subclones. Column vectors of categorical matrices record the corresponding sets of mutation pairs for subclones. The performance of PairClone is assessed by using simulated and real data sets with a comparison with existing methods. An open-source software package can be obtained from .
机译：肿瘤细胞群可以被认为是异质细胞亚群的组成，每个亚群的特征是重叠的单核苷酸变体集。这种亚群被称为亚克隆，是精密医学的重要目标。从下一代测序数据重建亚克隆是计算生物学的主要挑战之一。我们将PairClone作为实现此重构的新工具。 PairClone的主要思想是对映射到近端单核苷酸变体对的短读数进行建模，我们将其称为突变对。相反，其他现有方法仅对未配对的单核苷酸变体使用边际读取。使用贝叶斯非参数模型，我们估计一个或多个肿瘤样本中亚克隆的数量，基因型和种群频率的后验概率。我们使用分类印度自助餐过程作为亚克隆的先验概率模型。分类矩阵的列向量记录了亚克隆突变对的相应集合。 PairClone的性能通过使用模拟和真实数据集以及与现有方法的比较来评估。可以从获取开源软件包。

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