Graph-based and alignment-free analysis of bacterial pangenomes

一个种群的集体遗传内容被定义为“泛基因组”，它比许多细菌物种的单个基因组要大得多。在过去的二十年中，研究的重点是如何最好地表征泛基因组，使其形成和维持的进化力量得以表征。在本文中，我探讨了两种方法来表示泛基因组，基于图和无比对方法的应用，以提高传统生物信息学实践的准确性和计算效率。首先，我开发了一个基于图的细菌基因预测和注释工具ggCaller，以解决在单独注释基因组时出现的不一致和计算冗余问题。我详细介绍了ggCaller的开发，并在模拟和真实的细菌基因组数据集上对其进行了基准测试。我展示了ggCaller与当前最先进的工具相比，提高了泛基因组分析的准确性和运行时间。其次，我探索了纳米孔自适应采样（NAS）的应用，这是一种靶向DNA富集的读取比对方法，用于研究混合样品中的泛基因组多样性。当样品中存在密切相关的污染物DNA时，NAS表现不佳，通过靶向结构多样化的物种特异性位点来补救。然后，我开发了一种基于图的无比对方法，用于读取比对，该方法在针对未观察到的结构多样位点变体时提高了NAS的准确性。最后，我开发了使用无比对方法PopPUNK模拟泛基因组进化的方法。PopPUNK使用序列匹配估计基因组相似性，这是高度可扩展的，对基因预测错误不敏感，改进了传统的基于基因的建模方法。我表明PopPUNK数据可以区分具有不同种群结构的物种，并且基于基准分析和模拟的方法可以研究作用于泛基因组的进化力。这项工作为研究社区提供了公开可用的高精度泛基因组分析工具。它还强调了基于图和无比对方法的优点和挑战，这些方法具有在庞大的基因组数据集中研究泛基因组多样性所必需的计算效率。

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.