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

Osvaldo Zagordi, and Lukas Geyrhofer, and Volker Roth, and Niko Beerenwinkel, . (2009) Deep Sequencing of a Genetically Heterogeneous Sample : Local Haplotype Reconstruction and Read Error Correction. In: Research in Computational Molecular Biology. Berlin, Heidelberg, pp. 271-284.

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Official URL: http://edoc.unibas.ch/dok/A5253626

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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.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:Springer Berlin Heidelberg
ISBN:978-3-642-02008-7 ; 978-3-642-02007-0
Series Name:Lecture Notes in Computer Science
Issue Number:5541
Note:Publication type according to Uni Basel Research Database: Conference paper
Identification Number:
Last Modified:22 Mar 2012 14:25
Deposited On:22 Mar 2012 13:48

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