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Activity Number:
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357
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Type:
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Invited
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Date/Time:
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Wednesday, August 9, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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WNAR
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| Abstract - #304957 |
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Title:
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Detecting Positive Selection in Protein-Coding DNA Sequences in Absence of Substantial Phylogenetic Information
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Author(s):
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Raquel Prado*+ and Daniel Merl
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Companies:
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University of California, Santa Cruz and University of California, Santa Cruz
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Address:
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Baskin School of Engineering, Applied Math & Statistics, Santa Cruz, CA, 95064,
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Keywords:
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positive selection ; GLMS ; hierarchical models ; structured priors ; model comparison
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Abstract:
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Detecting site-specific rates of substitution is a challenging problem when dealing with polymorphic data. These data are characterized by low divergency, and, therefore, little phylogenetic signal is available. We use Bayesian-generalized linear models to describe the substitution patterns in polymorphic alignments. Our models are hierarchical and allow us to include biologically relevant prior information. Once the models are fitted to the data, it is possible to summarize the posterior distributions of key quantities that describe the patterns of evolution at the molecular level, such as the nonsynonymous to synonymous rates ratio for each amino acid site and the transition to transversion rates ratio. In addition, we present tools for comparing models that support neutral evolution in the data versus models that assume the data show evidence of positive and negative selection.
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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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