Abstract Details
Activity Number:
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314
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #309662 |
Title:
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Mixture Models of Metagenomic Read Counts for Ecological Analysis
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Author(s):
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John O'Brien*+
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Companies:
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Bowdoin College
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Keywords:
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Metagenomics ;
ecology ;
mixture model ;
sequence analysis
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Abstract:
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Metagenomics - techniques for ascertaining sequence data from uncultured environmental samples - has become a powerful means to explore ecological dynamics in the environment. The most common technique involves sampling a single gene, such as 16S rRNA, largely conserved across the tree of life to infer the distribution of species present within a set of samples. I show how a Bayesian mixture model applied to the read count data from a set of metagenomic samples can be used to infer a small number of ecological states that underlie the microbial dynamics of the collection. I provide an example from the English Channel showing that these states capture temporal fluctuations in the dominant photosynthetically productive microbial populations.
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Authors who are presenting talks have a * after their name.
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