estMOI: Estimating multiplicity of infection using parasite deep sequencing data.


Assefa, SA; Preston, MD; Campino, S; Ocholla, H; Sutherland, CJ; Clark, TG; (2014) estMOI: Estimating multiplicity of infection using parasite deep sequencing data. Bioinformatics (Oxford, England), 30 (9). pp. 1292-4. ISSN 1367-4803 DOI: 10.1093/bioinformatics/btu005

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Abstract

: Individuals living in endemic areas generally harbour multiple parasite strains. Multiplicity of infection (MOI) can be an indicator of immune status and transmission intensity. It has a potentially confounding effect on a number of population genetic analyses, which often assume isolates are clonal. Polymerase chain reaction-based approaches to estimate MOI can lack sensitivity. For example, in the human malaria parasite Plasmodium falciparum, genotyping of the merozoite surface protein (MSP1/2) genes is a standard method for assessing MOI, despite the apparent problem of underestimation. The availability of deep coverage data from massively parallizable sequencing technologies means that MOI can be detected genome wide by considering the abundance of heterozygous genotypes. Here, we present a method to estimate MOI, which considers unique combinations of polymorphisms from sequence reads. The method is implemented within the estMOI software. When applied to clinical P.falciparum isolates from three continents, we find that multiple infections are common, especially in regions with high transmission.<br/>

Item Type: Article
Faculty and Department: Faculty of Infectious and Tropical Diseases > Dept of Immunology and Infection
Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Faculty of Infectious and Tropical Diseases > Dept of Pathogen Molecular Biology
Research Centre: Malaria Centre
PubMed ID: 24443379
Web of Science ID: 336095100016
URI: http://researchonline.lshtm.ac.uk/id/eprint/1472192

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