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HIV Haplotype Inference Using a Propagating Dirichlet Process Mixture Model

Prabhakaran, Sandhya and Rey, Melanie and Zagordi, Osvaldo and Beerenwinkel, Niko and Roth, Volker. (2014) HIV Haplotype Inference Using a Propagating Dirichlet Process Mixture Model. IEEE/ACM transactions on computational biology and bioinformatics, Vol. 1, H. 1. pp. 182-191.

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Official URL: http://edoc.unibas.ch/dok/A6194626

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Abstract

This paper presents a new computational technique for the identification of HIV haplotypes. HIV tends to generate many potentially drug-resistant mutants within the HIV-infected patient and being able to identify these different mutants is important for efficient drug administration. With the view of identifying the mutants, we aim at analysing short deep sequencing data called reads. From a statistical perspective, the analysis of such data can be regarded as a nonstandard clustering problem due to missing pairwise similarity measures between non-overlapping reads. To overcome this problem we propagate a Dirichlet Process Mixture Model by sequentially updating the prior information from successive local analyses. The model is verified using both simulated and real sequencing data.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker and Prabhakaran, Sandhya and Rey, Mélanie
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:IEEE
ISSN:1545-5963
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:06 Feb 2015 09:58
Deposited On:06 Feb 2015 09:58

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