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dc.contributor.authorMacalalad, Alexander R.
dc.contributor.authorZody, Michael
dc.contributor.authorCharlebois, Patrick
dc.contributor.authorLennon, Niall J.
dc.contributor.authorNewman, Ruchi M.
dc.contributor.authorMalboeuf, Christine M.
dc.contributor.authorRyan, Elizabeth Marie
dc.contributor.authorBoutwell, Christian Lane
dc.contributor.authorPower, Karen A.
dc.contributor.authorBrackney, Doug E.
dc.contributor.authorPesko, Kendra N.
dc.contributor.authorLevin, Joshua Zvi
dc.contributor.authorEbel, Gregory D.
dc.contributor.authorAllen, Todd
dc.contributor.authorBirren, Bruce W.
dc.contributor.authorHenn, Matthew R.
dc.date.accessioned2013-11-01T16:53:37Z
dc.date.issued2012
dc.identifier.citationMacalalad, Alexander R., Michael C. Zody, Patrick Charlebois, Niall J. Lennon, Ruchi M. Newman, Christine M. Malboeuf, Elizabeth M. Ryan, et al. 2012. Highly sensitive and specific detection of rare variants in mixed viral populations from massively parallel sequence data. PLoS Computational Biology 8(3): e1002417.en_US
dc.identifier.issn1553-734Xen_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11248781
dc.description.abstractViruses 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 (<1%) within an infected host, and to assay variants without prior knowledge. Critical to interpreting deep sequence data sets is the ability to distinguish biological variants from process errors with high sensitivity and specificity. To address this challenge, we describe V-Phaser, an algorithm able to recognize rare biological variants in mixed populations. V-Phaser uses covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. Overall, V-Phaser achieved >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 (<1% frequency) detected only using phase information. V-Phaser identified variants at frequencies down to 0.2%, comparable to the detection threshold of allele-specific PCR, a method that requires prior knowledge of the variants. The high sensitivity and specificity of V-Phaser enables identifying and tracking changes in low frequency variants in mixed populations such as RNA viruses.en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdoi:10.1371/journal.pcbi.1002417en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3305335/pdf/en_US
dash.licenseLAA
dc.subjectbiologyen_US
dc.subjectcomputational biologyen_US
dc.subjectgenomicsen_US
dc.subjectpopulation geneticsen_US
dc.subjectpopulation modelingen_US
dc.subjectevolutionary biologyen_US
dc.subjectmicrobiologyen_US
dc.titleHighly Sensitive and Specific Detection of Rare Variants in Mixed Viral Populations from Massively Parallel Sequence Dataen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS Computational Biologyen_US
dash.depositing.authorAllen, Todd
dc.date.available2013-11-01T16:53:37Z
dc.identifier.doi10.1371/journal.pcbi.1002417*
dash.authorsorderedfalse
dash.contributor.affiliatedZody, M
dash.contributor.affiliatedRyan, Elizabeth Marie
dash.contributor.affiliatedLevin, Joshua Z.
dash.contributor.affiliatedBoutwell, C
dash.contributor.affiliatedAllen, Todd


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