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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
EstMOI: Estimating multiplicity of infection using parasite deep sequencing data
Bioinformatics, Volume 30, No. 9, Year 2014
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Description
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. © The Author 2013. Published by Oxford University Press.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC3998131/bin/supp_30_9_1292__index.html
https://efashare.b-cdn.net/share/pmc/articles/PMC3998131/bin/supp_btu005_SupplementaryMaterials.pdf
Authors & Co-Authors
Assefa, Samuel
Unknown Affiliation
Preston, Mark D.
Unknown Affiliation
Campino, Susana G.
Unknown Affiliation
Ocholla, Harold
Unknown Affiliation
Sutherland, Colin J.
Unknown Affiliation
Clark, Taane Gregory
Unknown Affiliation
Statistics
Citations: 66
Authors: 6
Affiliations: 4
Identifiers
Doi:
10.1093/bioinformatics/btu005
ISSN:
13674803
e-ISSN:
14602059
Research Areas
Genetics And Genomics
Health System And Policy
Infectious Diseases
Study Design
Cross Sectional Study