Graph-based and alignment-free analysis of bacterial pangenomes

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The collective genetic content of a population, defined as its 'pangenome', is much greater than that of a single genome for many bacterial species. Over the last two decades, work has focused on how best to represent pangenomes, enabling characterisation of the evolutionary forces driving their formation and maintenance. In this thesis, I explore the applications of two approaches to representing pangenomes, graph-based and alignment free methods, to improve the accuracy and computational efficiency of conventional bioinformatics practices.Firstly, I developed a graph-based bacterial gene prediction and annotation tool, ggCaller, to address inconsistency and computational redundancy issues occurring when annotating genomes individually. I detail ggCaller development, benchmarking it on simulated and real bacterial genome datasets. I show that ggCaller improves both accuracy and runtime of pangenome analysis against current state-of-the-art tools.Secondly, I explored the application of Nanopore Adaptive Sampling (NAS), a read-alignment method for targeted DNA enrichment, to studying pangenome diversity within mixed samples. I show NAS performs poorly when closely-related contaminant DNA is present in a sample, remedied by targeting structurally-diverse species-specific loci. I then developed a graph-based alignment-free method for read alignment, which improves NAS accuracy when targeting unobserved variants of structurally-diverse loci.Finally, I developed approaches for modelling pangenome evolution using the alignment-free method, PopPUNK. PopPUNK estimates genome similarity using sequence matching, which is highly scalable and insensitive to gene prediction errors, improving upon conventional gene- based modelling approaches. I show that PopPUNK data can distinguish species with distinct population structures, and benchmark analytical and simulation-based methods which enable study of evolutionary forces acting upon pangenomes.This work provides the research community with publicly available high-accuracy tools for pangenome analysis. It also highlights the benefits and challenges of graph-based and alignment-free methods, which have the computational efficiency necessary to study pangenome diversity in huge genome datasets.