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dc.contributor.authorSepúlveda, Nuno
dc.contributor.authorCampino, Susana G
dc.contributor.authorAssefa, Samuel A
dc.contributor.authorSutherland, Colin J
dc.contributor.authorPain, Arnab
dc.contributor.authorClark, Taane G.
dc.date.accessioned2014-08-27T09:41:56Z
dc.date.available2014-08-27T09:41:56Z
dc.date.issued2013-02-26
dc.identifier.citationSepúlveda 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.
dc.identifier.issn14712164
dc.identifier.pmid23442253
dc.identifier.doi10.1186/1471-2164-14-128
dc.identifier.urihttp://hdl.handle.net/10754/325242
dc.description.abstractBackground: 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.
dc.language.isoen
dc.publisherSpringer Nature
dc.rightsThis 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.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subjectmultidrug resistance protein 1
dc.subjectaccuracy
dc.subjectCLAG3.2 gene
dc.subjectcomparative genomic hybridization
dc.subjectcomputer program
dc.subjectcontrolled study
dc.subjectcopy number variation
dc.subjectfalse positive result
dc.subjectgene
dc.subjectgene amplification
dc.subjectgene deletion
dc.subjectgene locus
dc.subjectintermethod comparison
dc.subjectMDR1 gene
dc.subjectmethodology
dc.subjectnucleotide sequence
dc.subjectPlasmodium falciparum
dc.subjectPoisson distribution
dc.subjectquality control
dc.subjectsequence analysis
dc.subjectsimulation
dc.subjectstrain difference
dc.subjectDNA Copy Number Variations
dc.subjectFalse Positive Reactions
dc.subjectGenomics
dc.subjectModels, Statistical
dc.subjectPlasmodium falciparum
dc.subjectPoisson Distribution
dc.subjectSequence Analysis
dc.subjectSoftware
dc.subjectPlasmodium falciparum
dc.titleA Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalBMC Genomics
dc.identifier.pmcidPMC3679970
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionLondon School of Hygiene and Tropical Medicine, London, United Kingdom
dc.contributor.institutionCenter of Statistics and Applications, University of Lisbon, Lisbon, Portugal
dc.contributor.institutionWellcome Trust Sanger Institute, Hinxton, United Kingdom
dc.contributor.institutionDepartment of Clinical Parasitology, Hospital for Tropical Diseases, London, United Kingdom
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personPain, Arnab
refterms.dateFOA2018-06-14T03:23:26Z


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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.
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.