Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction Export

@incollection{citeulike:5891979, abstract = {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.}, author = {Zagordi, Osvaldo and Geyrhofer, Lukas and Roth, Volker and Beerenwinkel, Niko}, citeulike-article-id = {5891979}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-02008-7_21}, citeulike-linkout-1 = {http://www.springerlink.com/content/2610016205431k53}, doi = {10.1007/978-3-642-02008-7_21}, journal = {Research in Computational Molecular Biology}, keywords = {454, bayes, dirichlet, hiv, mcmc, sequencing, variants}, pages = {271--284}, posted-at = {2009-10-08 20:37:53}, priority = {0}, title = {Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction}, url = {http://dx.doi.org/10.1007/978-3-642-02008-7_21}, year = {2009} }

TY - CHAP ID - citeulike:5891979 L3 - citeulike-article-id:5891979 N2 - 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. JF - Research in Computational Molecular Biology EP - 284 TI - Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction SP - 271 KW - 454 KW - bayes KW - dirichlet KW - hiv KW - mcmc KW - sequencing KW - variants AU - Zagordi, Osvaldo AU - Geyrhofer, Lukas AU - Roth, Volker AU - Beerenwinkel, Niko PY - 2009/// UR - http://dx.doi.org/10.1007/978-3-642-02008-7_21 ER -