Title |
A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data
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Published in |
BMC Genomics, February 2013
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DOI | 10.1186/1471-2164-14-128 |
Pubmed ID | |
Authors |
Nuno Sepúlveda, Susana G Campino, Samuel A Assefa, Colin J Sutherland, Arnab Pain, Taane G Clark |
Abstract |
The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model. |
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