Analysis of a large Next Generation Sequencing dataset for malaria epidemiology investigations

Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. Nearly half of the world's population is at risk of malaria. Understanding how malaria parasites spread and evolve is a key step in eradicating the disease. The goal of this thesis work is to develop and test bioinformatics tools that allow an effective data analysis that responds to the control needs of useful information from the epidemiological point of view of malaria. A dataset of Next Generation Sequencing (NGS) data resulting from the sequencing of a set of samples collected in Burkina Faso was used. The informations that can be extrapolated according to the epidemiological context are useful for planning and evaluating control programs. Obtaining this information from NGS data is not trivial as there is no obvious or standardized bioinformatics analysis pipeline and there are a whole series of choices and methodological proposals that must be made starting from the organization of the data