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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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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 |
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author | Assefa, Samuel A. Preston, Mark D. Campino, Susana Ocholla, Harold Sutherland, Colin J. Clark, Taane G. |
author_facet | Assefa, Samuel A. Preston, Mark D. Campino, Susana Ocholla, Harold Sutherland, Colin J. Clark, Taane G. |
author_sort | Assefa, Samuel A. |
collection | PubMed |
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. 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. |
format | Online Article Text |
id | pubmed-3998131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-39981312014-04-24 estMOI: estimating multiplicity of infection using parasite deep sequencing data Assefa, Samuel A. Preston, Mark D. Campino, Susana Ocholla, Harold Sutherland, Colin J. Clark, Taane G. Bioinformatics Applications Notes 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. Oxford University Press 2014-05-01 2014-01-17 /pmc/articles/PMC3998131/ /pubmed/24443379 http://dx.doi.org/10.1093/bioinformatics/btu005 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Assefa, Samuel A. Preston, Mark D. Campino, Susana Ocholla, Harold Sutherland, Colin J. Clark, Taane G. estMOI: estimating multiplicity of infection using parasite deep sequencing data |
title | estMOI: estimating multiplicity of infection using parasite deep sequencing data |
title_full | estMOI: estimating multiplicity of infection using parasite deep sequencing data |
title_fullStr | estMOI: estimating multiplicity of infection using parasite deep sequencing data |
title_full_unstemmed | estMOI: estimating multiplicity of infection using parasite deep sequencing data |
title_short | estMOI: estimating multiplicity of infection using parasite deep sequencing data |
title_sort | estmoi: estimating multiplicity of infection using parasite deep sequencing data |
topic | Applications Notes |
url | 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 |
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