McCOILR

Last updated on Sep 2, 2022 R, malaria
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McCOILR is an Rcpp wrapper for THE REAL McCOIL software developed by the Greenhouse Lab that estimates complexity of infection and population allele frequencies using SNP data obtained from Sequenom or similar types of SNP assays. It was simply created to aid incorporating COI estimation more easily within distributed computing pipelines, and I claim no ownership over the original source code, and all attribution and acknowledgement should be referred to the original project, and the associated publication1. I have simply provided this wrapper in the hope that it might be helpful for others, and have provided a tutorial for basic use.

  1. THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites. Chang HH, Worby CJ, Yeka A, Nankabirwa J, Kamya MR, et al. (2017) THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites. PLOS Computational Biology 13(1): e1005348. (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005348#sec015) ↩︎

rpackage R malaria
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OJ Watson
Imperial College Research Fellow

I am an Imperial College Research Fellow supported by an Eric and Wendy Schmidt AI in Science Fellowship, working within Imperial's new AI Initiative: I-X. My primary focus is as an infectious disease modeller, data scientist, epidemiologist and an R developer. My academic work has focussed on modelling the spread of malaria and COVID-19, based at Imperial College London, Brown University and the London School of Hygiene and Tropical Medicine.

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