混合序列数据中已知单倍型频率的极大似然估计
Maximum Likelihood Estimation of Frequencies of Known Haplotypes from Pooled Sequence Data
摘要：
DNA样本通常被合并，或者是通过实验设计，或者是因为样本本身就是一种混合物。例如，当主要关注群体等位基因频率时，可以将单个样品汇集在一起以降低测序成本。或者，样品本身可以是多种物种或菌株的混合物（例如，血液样品中包含微生物组的细菌物种或病原体菌株）。我们提出了一个期望最大化（EM）算法估计单倍型频率直接从映射的序列读取合并样本中，在可能的单倍型是已知的情况下。该方法与选择实验的合并测序数据的分析以及宏基因组学样本中不同菌株比例的计算相关。我们的方法优于现有的基于单位点等位基因频率的方法，以及使用序列读段数据的简单方法。我们已经在一个免费的开源软件工具中实现了该方法。
英文摘要：
DNA samples are often pooled, either by experimental design, or because the sample itself is a mixture. For example, when population allele frequencies are of primary interest, individual samples may be pooled together to lower the cost of sequencing. Alternatively, the sample itself may be a mixture of multiple species or strains (e.g. bacterial species comprising a microbiome, or pathogen strains in a blood sample). We present an expectation-maximization (EM) algorithm for estimating haplotype frequencies in a pooled sample directly from mapped sequence reads, in the case where the possible haplotypes are known. This method is relevant to the analysis of pooled sequencing data from selection experiments, as well as the calculation of proportions of different strains within a metagenomics sample. Our method outperforms existing methods based on single- site allele frequencies, as well as simple approaches using sequence read data. We have implemented the method in a freely available open-source software tool.
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