Title: Revolutionising Bacterial Genomics: Graph-Based Strategies for Improved Variant Identification Show Chinese title

Title: 革新细菌基因组学：改进变异识别的图基策略

Abstract: A significant advancement in bioinformatics is using genome graph techniques to improve variation discovery across organisms. Traditional approaches, such as bwa mem, rely on linear reference genomes for genomic analyses but may introduce biases when applied to highly diverse bacterial genomes of the same species. Pangenome graphs provide an alternative paradigm for evaluating structural and minor variations within a graphical framework, including insertions, deletions, and single nucleotide polymorphisms. Pangenome graphs enhance the detection and interpretation of complex genetic variants by representing the full genetic diversity of a species. In this study, we present a robust and reliable bioinformatics pipeline utilising the PanGenome Graph Builder (PGGB) and the Variation Graph toolbox (vg giraffe) to align whole-genome sequencing data, call variants against a graph reference, and construct pangenomes from assembled genomes. Our results demonstrate that leveraging pangenome graphs over a single linear reference genome significantly improves mapping rates and variant calling accuracy for simulated and actual bacterial pathogens datasets. Show Chinese abstract

Abstract: 生物信息学的一个重要进展是使用基因组图技术来改进不同生物体中的变异发现。传统方法，如 bwa mem，依赖于线性参考基因组进行基因组分析，但在应用于同一物种的高多样性细菌基因组时可能会引入偏差。泛基因组图提供了一种替代范式，在图形框架内评估结构和小变异，包括插入、删除和单核苷酸多态性。通过表示一个物种的全部遗传多样性，泛基因组图增强了复杂遗传变异的检测和解释。在本研究中，我们提出了一种利用泛基因组图构建器（PGGB）和变异图工具箱（vg giraffe）的稳健可靠的生物信息学管道，用于对全基因组测序数据进行比对，相对于图形参考调用变异，并从组装的基因组构建泛基因组。我们的结果显示，利用泛基因组图相对于单一线性参考基因组显著提高了模拟和实际细菌病原体数据集的比对率和变异调用准确性。