A Pangenome Approach to Detect and Genotype TE Insertion Polymorphisms.

Pangenome graphs are flexible data structures that contain the genetic variation that exists in a population of genomes and describe the sequences of the many possible ensuing haplotypes. Here, we use such a pangenome graph to represent and genotype transposable element (TE) polymorphisms. By combining the transposable element annotation (Alus, L1s, and SVAs) of the human genome reference with novel transposable element insertions observed in two high-quality assemblies (HG002 and HG00733), we show how to create a transposable element pangenome that consists of ~1.2 million reference and 2939 non-reference transposable elements. We then demonstrate this approach by aligning short-read sequencing data and genotyping transposable element deletions and insertions with reasonable specificity and sensitivity (0.85 F1-score).

Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads.

Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.