Revolutionising Bacterial Genomics: Graph-Based Strategies for Improved Variant Identification

TL;DR

This paper introduces a graph-based bioinformatics pipeline that significantly improves variant detection accuracy in bacterial genomes by utilizing pangenome graphs instead of traditional linear references.

Contribution

The study presents a new pipeline using PGGB and vg giraffe for aligning, variant calling, and constructing pangenomes, enhancing bacterial genomics analysis.

Findings

Improved mapping rates with pangenome graphs

Enhanced variant calling accuracy

Effective analysis of diverse bacterial datasets

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.