Statistical Methods for the Analysis of Deep Sequenced Pneumococcal Data (360G-Wellcome-204016_Z_16_A)

Streptococcus pneumoniae (the pneumococcus) is a major disease causing pathogen and can cause sepsis, meningitis and pneumonia especially in at risk populations such as young children and the elderly. Understanding genetic factors in disease virulence, transmissibility, and drug resistance informs the management and treatment of infectious disease. By using deep sequenced patient samples of S. pneumoniae it is possible to build a clearer picture of its within host diversity. I aim to develop statistical and computational methods for the analysis of deep sequenced pathogen data that are also able to deal with large datasets, of the order of thousands of samples. I aim to apply these methods to the analysis of deep sequencing data derived from nearly 4000 S. pneumoniae samples taken from patients in the Maela refugee camp, Thailand. The methods I develop will help to identify significant genetic factors for disease dynamics and antimicrobial resistance. The project will contribute to the understanding of S. pneumoniae and will also provide tools of more general applicability to the investigation of deep sequenced pathogen data.