# Bridging Gene-level And Sequence-level Pangenome Graph To Explore Microbial Diversity

Hanin Ali, Bastien Degardins, Charles Paperman, Camille Marchet, Guillaume Gautreau

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Hanin Ali, Bastien Degardins, Charles Paperman, Camille Marchet, Guillaume Gautreau. Bridg-
ing Gene-level And Sequence-level Pangenome Graph To Explore Microbial Diversity. Journées
Ouvertes de Biologie, Informatique et Mathématiques 2024, Jul 2025, Bordeaux, France.
https://jobim2025.labri.fr/JOBIM2025_proceedings_posters.pdf. hal-05176404

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Submitted on 22 Jul 2025

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# Bridging Gene-level And Sequence-level Pangenome Graph To Explore Microbial Diversity

Hanin ALI¹, Bastien DEGARDINS², Charles PAPERMAN², Camille MARCHET², Guillaume GAUTREAU¹

1- Université Paris-Saclay, INRAE, MalAGE, 78350, Jouy-en-Josas, France

2- CRISTAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

## Introduction

Pangenomics seeks to capture the full genetic diversity of a species by comparing (most of the time) all available genomes [1,2]. Conventional gene-family graphs often ignore non-coding RNAs, intergenic regions IGRs, and within-family sequence variation. [3,4]

In this work, we extend the PPanGGOLiN features beyond protein-coding genes (CDS) to include all genomic features—coding and non-coding genes, plus the pangenome of intergenic regions (paniGRome) [5].

For each family and intergenic region, we then resolve the structure with compacted de Bruijn subgraphs to reveal nucleotide-level diversity.

This two-layer representation preserves the global structure of microbial pangenomes while illuminating the precise sequence differences that drive functional and evolutionary variation across either CDS, RNA elements and intergenic regions.

<figure><img src="image_2.png" /></figure>

## A - Extension of PPanGGOLiN Features

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With CDS and RNA annotations already in place, we extract intergenic sequences and cluster RNAs into families. We then rebuild the pangenome family neighborhood-based graph to include both Gene and RNA families, extract the Edges between every family, and store all data in an HDF5 file.

## B - Build families and edges de Bruijn Graphs

For each gene family, RNA family and Edges, we load sequence data from HDF5 files and construct a compacted de Bruijn subgraph to reveal nucleotide-level diversity, and then visualize the resulting graph interactively with Vizitig.

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## C - Visualize with Vizitig

We leverage Vizitig to render our hierarchical pangenome graph, seamlessly integrating the PPanGGOLiN-derived locus-level network with per-locus compacted de Bruijn subgraphs. [8]

End users can query and highlight any subgraph—e.g. intergenic→ gene→ gene walks or core versus accessory variant branches—and fluidly toggle between family-level and nucleotide-level views.

<figure><img src="image_5.png" /><figcaption>An overview of Vizitig. [8]</figcaption></figure>

## Conclusion & Perspective

By unifying gene-family and sequence-level graphs into a single, two-layer framework, we deliver an unprecedented view of microbial pangenomes that captures both macro-scale gene-presence patterns and micro-scale nucleotide variation.

Next steps will include either improving the browsing of the visiting interface for gene-scale visualization, or mapping long-read sequencing data directly onto this integrated graph. This will enable the simultaneous detection of structural variants and single-nucleotide polymorphisms in coding regions, non-coding genes, or intergenic regions (IGRs), as well as presence/absence variations. This approach promises to deepen our understanding of genome diversity, accelerate functional discovery, and provide a versatile platform for exploring microbial evolution.

## References

[1] Tettelin H, Massignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pangenome". Proc Natl Acad Sci USA. 2005 Sep;102(39):13950-5.

[2] Computational Pan-Genomics Consortium. Computational pan-genomics: status, promises and challenges. Brief Bioinform. 2018 Jan;19(1):118-35.

[3] Gautreau G, Bazin A, Gachet M, Planel R, Burlot L, Dubois M, et al. PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph. PLoS Comput Biol. 2020 Mar;16(3):e1007732.

[4] Tonkin-Hill, G., MacAlasdair, N., Ruis, C. et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol 21, 180 (2020). https://doi.org/10.1186/s13059-020-02090-4

[5] Thorpe H, Bayliss S, Sheppard S, Feil E. Piggy: a rapid, large-scale pan-genome analysis tool for intergenic regions in bacteria, GigaScience, Volume 7, Issue 4, April 2018, giy015, https://doi.org/10.1093/gigascience/giy015

[6] Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30(14):3059-3066. doi:10.1093/nar/gkf436

[7] Cracco, Andrea & Tomescu, Alexandru. (2023). Extremely fast construction and querying of compacted and colored de Bruijn graphs with GGCAT. Genome research. 10.1101/gr.277615.122.

[8] Degardins B, Mouton M, Guillon B, Paperman C, Marchet C. Vizitig: A visual tool for colored de Bruijn graphs exploration; 2024. Working paper or preprint. Available from: https://hal.science/hal-04735326.

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