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Abstract Details

Activity Number: 381
Type: Invited
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #303824
Title: Detecting Mutations in Mixed Sample Sequencing Data Using Empirical Bayes
Author(s): Omkar Muralidharan*+ and John Bell and Georges Natsoulis and Nancy Zhang
Companies: Stanford University and Stanford University and Iconix Pharmaceuticals and Stanford University
Address: Department of Statistics, Stanford, CA, 94305-4065,
Keywords: Empirical Bayes ; False discovery rates ; Discrete data ; DNA sequencing ; Genome variation

We develop methods to detect mutations in mixed sample sequencing data in a statistically sound way. Sequencing generates counts of the number of times each base was observed at hundreds of thousands to billions of genome positions in each sample. Using these counts to detect mutations is challenging because mutations may have very low prevalence and sequencing error rates vary dramatically by genome position. The discreteness of sequencing data also makes this a difficult multiple testing problem - current false discovery rate methods are designed for continuous data, and work poorly, if at all, on discrete data. We show that a simple randomization technique lets us use continuous false discovery rate methods on discrete data. Our approach is a useful way to estimate false discovery rates for any collection of discrete test statistics, and is not limited to sequencing data. We then use an empirical Bayes model to capture different sources of variation in sequencing error rates. The resulting method outperforms existing detection approaches on example data sets.

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