A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data
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ArticleAuthors
Sepúlveda, NunoCampino, Susana G
Assefa, Samuel A
Sutherland, Colin J
Pain, Arnab

Clark, Taane G.

KAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionBioscience Program
Computational Bioscience Research Center (CBRC)
Pathogen Genomics Laboratory
Date
2013-02-26Online Publication Date
2013-02-26Print Publication Date
2013Permanent link to this record
http://hdl.handle.net/10754/325242
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Show full item recordAbstract
Background: 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.Results: Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates.Conclusions: In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data. 2013 Seplveda et al.; licensee BioMed Central Ltd.Citation
Sepúlveda N, Campino SG, Assefa SA, Sutherland CJ, Pain A, et al. (2013) A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data. BMC Genomics 14: 128. doi:10.1186/1471-2164-14-128.Publisher
Springer NatureJournal
BMC GenomicsPubMed ID
23442253PubMed Central ID
PMC3679970ae974a485f413a2113503eed53cd6c53
10.1186/1471-2164-14-128
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Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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