Muralidharan, Omkar; Natsoulis, Georges; Bell, John; Ji, Hanlee; Zhang, Nancy R. Detecting mutations in mixed sample sequencing data using empirical Bayes. (English) Zbl 1254.62114 Ann. Appl. Stat. 6, No. 3, 1047-1067 (2012). Summary: We develop statistically based methods to detect single nucleotide DNA mutations in next generation sequencing data. 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 creates 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 hence 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. Cited in 4 Documents MSC: 62P10 Applications of statistics to biology and medical sciences; meta analysis 92C40 Biochemistry, molecular biology 62C12 Empirical decision procedures; empirical Bayes procedures 62J15 Paired and multiple comparisons; multiple testing 92D10 Genetics and epigenetics Keywords:false discovery rates; discrete data; DNA sequencing; genome variation Software:GATK × Cite Format Result Cite Review PDF Full Text: DOI arXiv Euclid References: [1] Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. 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