期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 17, 期 3, 页码 417-428出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2009.0164
关键词
computational molecular biology; next generation sequencing; statistics; Dirichlet process mixture; quasispecies; cancer genomics
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.
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