Applying identity-by-descent mapping to malaria samples
Associate Professor Melanie Bahlo
Friday 10th June
ESJ King Theatre, Medical Building, The University of Melbourne
Most of the human morbidity and mortality due to malaria is caused by two plasmodium species: falciparum and vivax. Generation of whole genome sequencing data for malaria is now routine. It is generated by extracting the plasmodium DNA from human red blood cells. In countries with a high burden of malaria this can lead to samples with multiple clones, known as multiplicity of infection (MOI). We have developed three algorithms to help detect and make use of this type of data. The first is an expectation-maximisation (EM) algorithm that estimates the number of clones and the clonal proportions. The second and third algorithms implement hidden Markov models (HMMs) to identify relatedness between clones within and between samples. We demonstrate their application on whole genome sequencing data from Papua New Guinea.
Enquiries: Andrew Siebel (firstname.lastname@example.org)