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Estimating within-host viral genetic diversity from next-generation sequencing data

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Mathematical, Statistical and Computational Aspects of the New Science of Metagenomics

Next-generation sequencing allows for cost-effective probing of pathogen populations at an unprecedented level of detail. The massively parallel sequencing approach can detect low-frequency alleles and it provides a snapshot of the structure of the entire population. However, analyzing ultra-deep sequencing data obtained from mixed samples is challenging, because reads contain amplification and sequencing errors and the read length is typically shorter than the genomic region of interest. Thus, ultra-deep sequencing experiments provide only indirect evidence of the underlying population structure. We will present computational and statistical methods for read error correction and haplotype reconstruction in intra-patient virus populations, and show how to infer evolutionary parameters from these data.

This talk is part of the Isaac Newton Institute Seminar Series series.

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