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estMOI: estimating multiplicity of infection using parasite deep sequencing data

Summary: 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 a...

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Detalles Bibliográficos
Autores principales: Assefa, Samuel A., Preston, Mark D., Campino, Susana, Ocholla, Harold, Sutherland, Colin J., Clark, Taane G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3998131/
https://www.ncbi.nlm.nih.gov/pubmed/24443379
http://dx.doi.org/10.1093/bioinformatics/btu005
Descripción
Sumario:Summary: 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. Availability and implementation: estMOI is freely available from http://pathogenseq.lshtm.ac.uk. Contact: samuel.assefa@lshtm.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.