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Abstract Details
Activity Number:
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3
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Type:
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Invited
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #300393 |
Title:
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Assessing the Uncertainty in Multiple Alignments of HIV Sequences, with Implications for Vaccine Development
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Author(s):
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Paul T. Edlefsen*+
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Companies:
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Fred Hutchinson Cancer Research Center
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Address:
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1100 Fairview Ave N, Seattle, WA, 98109-1024,
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Keywords:
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HIV ;
vaccine ;
clinical trial ;
sieve analysis ;
profile hmm ;
hidden markov model
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Abstract:
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A major challenge in HIV vaccine development is developing assays to detect vaccine-induced evolutionary pressure on infecting HIV strains. Using so-called 'sieve analysis', Peter Gilbert and colleagues showed that the (failed) "STEP" vaccine trial did have an effect, as measured by amino-acid differences between HIV strains found in infected placebo recipients versus infected vaccine recipients. In this analysis, and in other important analyses for HIV vaccine research, the hand-generated multiple alignments of HIV sequences are accepted as true and error-free. In analyzing the more recent (and borderline statistically significantly efficacious) "Thai" trial, concern about the dependence on a single multiple alignment is particularly justified: in this and future vaccine trials, a primary goal is to elicit antibodies to the extremely-rapidly-evolving envelope protein. I will show that the standard sieve analysis methods are sensitive to the alignment in this context, and will present a model-based alternative using Profile Hidden Markov Models. This approach incorporates some of the alignment uncertainty into the analysis.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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