moire

Author

Kathryn Murie

Published

December 11, 2023



Summary sheet

Main use-cases Estimating multiplicity of infection (MOI), population allele frequencies, and within-host relatedness from polyallelic genomics data.
Authors Maxwell Murphy, Bryan Greenhouse
Latest version v3.1.0
License GNU General Public License v3.0
Website https://eppicenter.github.io/moire/
Code repository https://github.com/EPPIcenter/moire
Publication https://doi.org/10.1101/2023.10.03.560769
Tutorial authors Kathryn Murie
Tutorial date 11-Dec-23

Purpose

The moire (Multiplicity Of Infection and allele frequency REcovery) tool can be used to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. It utilises a Markov Chain Monte Carlo (MCMC) based approach to Bayesian estimation and can take both polyallelic and SNP data as inputs. This tool also introduces a new metric called effective MOI (eMOI), which combines MOI and within-host relatedness into a unified and comparable measure of genetic diversity.

Existing resources

  • The moire website provides basic usage instructions
  • Within the moire website there is a more in depth tutorial using simulated genotyping data.

Citation

Please use the following citation:

@Article{,
  title = {MOIRE: A software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data},
  author = {Maxwell Murphy and Bryan Greenhouse},
  journal = {bioRxiv},
  year = {2023},
  doi = {10.1101/2023.10.03.560769},
  abstract = {Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data has been challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. In this study, we introduce a Bayesian approach called MOIRE (Multiplicity Of Infection and allele frequency REcovery), designed to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. Importantly, MOIRE is flexible in accommodating both polyallelic and SNP data, making it adaptable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Using extensive simulations and real-world data from a malaria study in Namibia, we demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to 7 with moderate sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings, even when allele frequencies or genotyping panels are different. MOIRE represents an important addition to the analysis toolkit for malaria population dynamics. Compared to existing software, MOIRE enhances the accuracy of parameter estimation and enables more comprehensive insights into within-host diversity and population structure. Additionally, MOIRE{  extquoteright}s adaptability to diverse data sources and potential for future improvements make it a valuable asset for research on malaria and other organisms, such as other eukaryotic pathogens. MOIRE is available as an R package at https://eppicenter.github.io/moire/.Competing Interest StatementThe authors have declared no competing interest.},
  publisher = {Cold Spring Harbor Laboratory},
}
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