cn.MOPS: mixture of Poissons for discovering copy number variations in next generation sequencing data with a low false discovery rate
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ISMB 2012 Proceedings
Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Technological or genomic variations in the depth of coverage lead to a high false discovery rate (FDR), even upon correction for GC content. We propose ?Copy Number estimation by a Mixture Of PoissonS? (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise, which is the reason for the superior performance.