Highly Sensitive and Specific Detection of Rare Variants in Mixed Viral Populations from Massively Parallel Sequence Data

Alexander R Macalalad, Michael C Zody, Patrick Charlebois, Niall J Lennon, Ruchi M Newman, Christine M Malboeuf, Elizabeth M Ryan, Christian L Boutwell, Karen A Power, Doug E Brackney, Kendra N Pesko, Joshua Z Levin, Gregory D Ebel, Todd M Allen, Bruce W Birren and Matthew R Henn

PLOS Computational Biology, 2012, vol. 8, issue 3, 1-10

Abstract: Viruses diversify over time within hosts, often undercutting the effectiveness of host defenses and therapeutic interventions. To design successful vaccines and therapeutics, it is critical to better understand viral diversification, including comprehensively characterizing the genetic variants in viral intra-host populations and modeling changes from transmission through the course of infection. Massively parallel sequencing technologies can overcome the cost constraints of older sequencing methods and obtain the high sequence coverage needed to detect rare genetic variants ( 97% sensitivity and >97% specificity on control read sets. On data derived from a patient after four years of HIV-1 infection, V-Phaser detected 2,015 variants across the ∼10 kb genome, including 603 rare variants (

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1002417

DOI: 10.1371/journal.pcbi.1002417

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