Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 11, Issue 1, Pages 182-191Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2013.145
Keywords
HIV; haplotype inference; MCMC; 454 sequencing reads
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Funding
- Swiss National Science Foundation [CR32I2_146331, CR32I2_127017]
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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 analyzing short deep sequencing data called reads. From a statistical perspective, the analysis of such data can be regarded as a nonstandard clustering problemdue to missing pairwise similarity measures between non-overlapping reads. To overcome this problemwe 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.
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