# To the species level and beyond A rationalized approach to study pangenomes

Emeline Bruyère, Romane Junker, Sandra Dérozier, Hélène Chiapello, Guillaume Gautreau

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Emeline Bruyère, Romane Junker, Sandra Dérozier, Hélène Chiapello, Guillaume Gautreau. To the species level and beyond A rationalized approach to study pangenomes. Journées Ouvertes de Biologie, Informatique et Mathématiques 2024, Jun 2024, Toulouse, France. , Nextcloud, https://nextcloud.inrae.fr/s/An986qpn6CqCQek. hal-04673896

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Submitted on 20 Aug 2024

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RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

INRAE
Centre Jouy-en-Josas

université
PARIS-SACLAY

MATHÉMATIQUES ET INFORMATIQUE APPLIQUÉES
MaiAGE
DU GÉNOME À L'ENVIRONNEMENT

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# To the species level and beyond

## A rationalized approach to study pangenomes

Emeline Bruyère¹, Romane Junker¹, Sandra Dérozier¹, Hélène Chiapello¹ and Guillaume Gautreau¹

1 Université Paris-Saclay, MaiAGE, INRAE, Domaine de Vilvert, 78350, Jouy-en-Josas, France

<figure><img src="image_4.png" /></figure>

Presenting author: emeline.bruyere@inrae.fr

## INTRODUCTION

The **pangenome** concept has become an essential framework for analyzing genomic diversity across species in an era of next-generation sequencing [1]. Historically, microbial pangenomic studies have focused on **gene families** to define **core and accessory genomes** [2]. However, **inconsistent criteria** for defining gene families make comparing pangenomic metrics between studies difficult [3].

Most microbial pangenomic studies relies on species-level datasets, often overlooking broader taxonomic analysis. Only a few studies, such as SCARAP [4], have developed suitable methods to **explore pangenomic diversity beyond the species level**.

The Genome Taxonomy DataBase (GTDB) project has paved the way for a genetic redefinition of bacterial taxonomy by proposing an overhaul based on 120 ubiquitous single-copy proteins [5]. In this context, there is a real need for strategies to build gene families and rationalize pangenomic analysis based on genome diversity, with **appropriate parameter values**.

<figure><img src="image_5.png" /><figcaption>**Figure 1:** Schematic representation of pangenomes as Venn diagram [6]</figcaption></figure>

## OBJECTIVES

Following on from the work begun in [7], our aim here is to:

* Provide a workflow that complies with FAIR principles

* Generate an overview of the distribution of percent identity (%id) across the Bacteria domain

* **Suggest appropriate percent identity values** for the generation of homologous gene families when building pangenomes

**Figure 2:** Interrelationship between shared gene content and AAI for bacterial genomes. AAI values of all available completed bacterial genomes were computed in pair-wise mode (x axes) and are plotted against the % of genes in the genome shared between the two genomes in the pair (y axes). [7]

<figure><img src="image_6.png" /></figure>

## METHOD

<figure><img src="image_7.png" /><figcaption>**Figure 3:** BacPan - Bacterial wide Pangenome study, the snakemake pipeline used in this project.</figcaption></figure>

1. We started by **retrieving the nucleotide sequences** of a representative set of complete bacterial genomes from RefSeq, using the GTDB taxonomy. We did so using `genome_updater` [8], a publicly available bash script.

2. We next **identified the protein-coding sequences** (CDS) of each of these genomes using `Prodigal` [9].

3. We stored the CDSs of each genome as `MMseqs2` [10] **indexed databases**.

4. From randomly sampled pairs of genomes (`1per10⁴` of all possible pairs), we then **aligned** the set of CDS of one with those of the other to identify the pairs of **homologous CDS** (%id ≥ 30 and coverage ≥ 70%).

5. In parallel, the taxonomic ranks and the size of each genome were stored using a bash script of our own.

6. For each pair of homologous CDS, the **percent identity and coverage rates** of the alignment were calculated. Then, for each pair of genomes, we determined the average value of these rates across all homologous CDS (i.e. **Average Amino acid Identity**, AAI), as well as the **alignment fraction (AF)**, and stored those information into a global dataframe.

7. Finally, we analyzed and **visualized the distribution** of these values **highlighting the last taxonomic level shared** by each pair of genomes.

## RESULTS

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

### Main observations:

* **Clear species delineation** compared to the other taxonomic ranks, as expected

* Subtle but **visible delineation between each rank**, except between the genus and family ranks which are almost blended

* **Wide variance at the genus rank**

* **Partial correlation** between AF (a proxy for core genome size) and AAI

### Legend:

* The 15th percentile arbitrarily chosen as a threshold to classify genes as belonging to the same gene family.

* Concave inflection points estimated at the species, family, and genus levels.

<table><caption>**Table 1:** Overview of the genomes used in the pipeline.</caption><thead><tr><td></td><td>Full dataset</td><td>Small dataset</td></tr></thead><tbody><tr><td># of species</td><td>8 082</td><td>7 924</td></tr><tr><td># of genomes</td><td>18 114</td><td>17 626</td></tr><tr><td># of pairs</td><td>> 328 Millions</td><td>32 806</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Figure 4:** Schematic representation of the bacterial taxonomy, with example for each rank.</figcaption></figure>

**Figure 5:** By last shared taxonomic rank,

A) Approximation of the distribution of percent identity across the core genome of any pair of mapped genomes.

B) Mean percent identity vs. alignment fraction of each pair of mapped genomes.

C) Distribution of the mean percent identity per core genome of each pair of mapped genomes.

<figure><img src="image_11.png" /></figure>

## CONCLUSION

Recommended **minimum threshold for %id:**

<table><thead><tr><td>Method</td><td>Species rank</td><td>Genus rank</td><td>Family rank</td><td>Order rank</td><td>Class rank</td><td>Phylum rank</td><td>Domain rank</td></tr></thead><tbody><tr><td>15th percentile</td><td>98</td><td>56</td><td>44</td><td>37</td><td>35</td><td>33</td><td>30</td></tr><tr><td>Concave inflection</td><td>95</td><td>65</td><td>55</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table>

These thresholds seems pretty similar to Rodriguez et al.'s results [7].

Our Snakemake workflow is available at: https://forgemia.inra.fr/emeline.bruyere/bacpan

### Next steps:

* Use of even **more data** → Full dataset

* Improve sampling of pairs as highest last shared rank pairs are enriched in our dataset

* Add of Average Nucleotide Identity (ANI) calculation to the pipeline

* Use Hidden Markov Model to identify homologous CDS pairs

* Study the conservation of **gene synteny**

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

## Acknowledgements

We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migue bioinformatics Facility, doi: 10.15454/1.5572390655343293E12) for providing help, computing and storage resources.

[1] Vernikos et al. Ten years of pan-genome analyses. Current Opinion in Microbiology. 2015.

[2] Tettelin et al. Comparative genomics: the bacterial pan-genome. Current Opinion in Microbiology. 2008.

[3] Marin et al. Analysis of the limited M. tuberculosis accessory genome reveals potential pitfalls of pan-genome analysis approaches. bioRxiv. 2024.

[4] Wittouck S. "Flexible, scalable and sensitive pangenome inference with SCARAP." Doctoral thesis, University of Antwerp, KU Leuven. 2021.

[5] Parks et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018.

<figure><img src="image_14.png" /></figure>

## References

[6] McInerney et al. Why Prokaryotes Have Pangenomes. Nature Microbiology. 2017.

[7] Rodriguez-R and Konstantinidis. Bypassing cultivation to identify bacterial species. Microbe. 2014.

[8] https://github.com/pirovc/genome_updater

[9] Hyatt et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010

[10] Steinegger and Söding. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017