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Open Access Methodology article

A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data

Nuno Sepúlveda12*, Susana G Campino3, Samuel A Assefa13, Colin J Sutherland14, Arnab Pain55 and Taane G Clark1

Author Affiliations

1 London School of Hygiene and Tropical Medicine, London, UK

2 Center of Statistics and Applications, University of Lisbon, Lisbon, Portugal

3 Wellcome Trust Sanger Institute, Hinxton, UK

4 Department of Clinical Parasitology, Hospital for Tropical Diseases, London, UK

5 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

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BMC Genomics 2013, 14:128  doi:10.1186/1471-2164-14-128

Published: 26 February 2013

Abstract

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.