Viral sequence data are increasingly being used to estimate evolutionary and epidemiological parameters to understand the dynamics of viral diseases. This thesis focuses on developing novel and improved computational methods for high-throughput analysis of large viral sequence datasets. I have developed a novel computational pipeline, Pipelign, to detect potentially unrelated sequences from groups of viral sequences during sequence alignment. Pipelign detected a large number of unrelated and mis-annotated sequences from several viral sequence datasets collected from GenBank. I subsequently developed ANVIL, a machine learning-based recombination detection and subtyping framework for pathogen sequences. ANVIL's performance was benchmarked using two large HIV datasets collected from the Los Alamos HIV Sequence Database and the UK HIV Drug Resistance Database, as well as on simulated data. Finally, I present a computational pipeline named Phlow, for rapid phylodynamic inference of heterochronous pathogen sequence data. Phlow is implemented with specialised and published analysis tools to infer important phylodynamic parameters from large datasets. Phlow was run with three empirical viral datasets and their outputs were compared with published results. These results show that Phlow is suitable for high-throughput exploratory phylodynamic analysis of large viral datasets. When combined, these three novel computational tools offer a comprehensive system for large scale viral sequence analysis addressing three important aspects: 1) establishing accurate evolutionary history, 2) recombination detection and subtyping, and 3) inferring phylodynamic history from heterochronous sequence datasets.

Clinical genomics/metagenomics is a rapidly developing field and it makes genomic, transcriptomic, and epigenomic evaluations of clinically relevant samples possible due to decreasing sequencing costs and large volumes of sequence datasets. Applications of comprehensive protocols for clinical metagenomic analysis is rapidly moving from the research laboratories to the clinical laboratories in healthcare settings. It has not only improved medical interventions but also continue to contribute to precision treatments. In this dissertation, I am going to discuss (i) the applications of clinical genomics/metagenomics protocols in several clinical cases of infections and (ii) the application of comparative genomics in Mycobacterium riyadhense clinical isolates which provide insights into ancestry and adaptive evolution in MTBC (Mycobacterium tuberculosis complex); The research questions are systematically addressed in four chapters. Briefly, Chapter 1 provides a brief introduction of conventional clinical microbiology and clinical metagenomics and a research summary of the thesis; Chapter 2 presents an analysis of Mycobacterium riyadhense from an evolutionary genomics point of view, followed by functional genomics experiments to look for clues obtained from the comparative genome analysis of M. riyadhense and other mycobacteria including members of the MTBC. The third chapter describes an imported case of Mycobacterium leprae found in Riyadh, Saudi Arabia, revealed by metagenomic sequencing and bioinformatic analysis, which was challenging for the clinicians to treat due to lack of timely diagnosis and occurrence of drug resistance during the course of treatment. The fourth chapter describes how applications of NGS facilitate rapid pathogen discovery in an imported case of Naegleria fowleri from the Cerebrospinal fluid (CSF) of a resident in KSA with recent travel history to Pakistan. The fifth chapter presents how we have monitored an ongoing outbreak by drug-resistant strains of the human pathogenic yeast Candida auris in Saudi Arabia from King Faisal Medical City, Riyadh, Saudi Arabia. In this thesis, I have shown several applications of NGS and bioinformatic analysis protocols in the clinical genomics/metagenomics fields. I believe some of the main clinical applications of NGS will lead to the adoption of these methodologies in clinical settings in Saudi Arabia in the forthcoming future.

Meningitis is an infection of the meninges, a layer of tissue surrounding the brain. In cases of pneumococcal meningitis (where the bacterium Streptococcus pneumoniae is the causat- ive agent) this causes severe inflammation, requiring intensive care and rapid antibiotic treatment. The contribution of variation in host and pathogen genetics to pneumococcal meningitis is unknown. In this thesis I develop and apply statistical genetics techniques to identify genomic variation associated with the various stages of pneumococcal meningitis, including colonisation, invasion and severity. I start by describing the development of a method to perform genome-wide association studies (GWAS) in bacteria, which can find variation in bacterial genomes associated with bacterial traits such as antibiotic resistance and virulence. I then applied this method to longitudinal samples from asymptomatic carriage, and found lineages and specific variants associated with altered duration of carriage. To assess meningitis versus carriage samples I applied similar analysis techniques, and found that the bacterial genome is crucial in determining invasive potential. As well as bacterial serotype, which I found to be the main effect, I discovered many independent sequence variants associated with disease. Separately, I analysed within host-diversity during the invasive phase of disease and found it to be of less relevance to disease progression. Finally, I analysed host genotype data from four independent studies using GWAS and heritability estimates to determine the contribution of human sequence variation to pneumococcal meningitis. Host sequence accounted for some variation in susceptibility to and severity of meningitis. The work concludes with a combined analysis of pairs of bacterial and human sequences from meningitis cases, and finds variation correlated between the two.