# Evidencing strain-dependency of metabolic pathways within 1,494 lactic bacteria genomes with the in silico screening Prolipipe pipeline

Noé Robert, Jeanne Got, Pauline Hamon-Giraud, Hélène Falentin, Anne Siegel

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Noé Robert, Jeanne Got, Pauline Hamon-Giraud, Hélène Falentin, Anne Siegel. Evidencing strain-dependency of metabolic pathways within 1,494 lactic bacteria genomes with the in silico screening Prolipipe pipeline. JOBIM 2025 - Journées Ouvertes en Biologie, Informatique et Mathématiques, Romain Bourqui; Clémence Frioux; Simon Labarthe, Jul 2025, Bordeaux, France. hal-05045657v4

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# Evidencing strain-dependency of metabolic pathways within 1,494 lactic bacteria genomes with the in silico screening Prolipipe pipeline

Noé Robert<fnref n="1" />, Jeanne Got<fnref n="1" />, Pauline Hamon-Giraud<fnref n="1" />, Hélène Falentin<fnref n="2" />, and Anne Siegel<fnref n="1" />

DOI not yet assigned

## Abstract

Genomes from bacteria of interest to the food industry exhibit significant functional variability, yet evaluating this characteristic remains challenging. As public repositories continue to accumulate more genomes, large-scale assessment of metabolic potential emerges as a promising method to highlight this functional variability. The primary challenge lies in automating a workflow to construct metabolic networks from genomes on a massive scale. Here, we present Prolipipe, a pipeline designed for the large-scale assessment of metabolic potential in bacteria, focusing on specific pathways. Given a large dataset of hundreds to thousands of bacterial genomes with known taxonomy and a list of targeted pathways, Prolipipe identifies gene functions through a comprehensive annotation step using three different tools. Then it builds genome-scale metabolic networks for each genome. These networks are then parsed to document the presence or absence of each reaction across all processed genomes. The pipeline evaluates the metabolic potential of each genome to carry out the pathway according to its gene content and highlight the best candidates among the large-scale set of genomes. In this study, Prolipipe was applied to 1,494 genomes of lactic acid bacteria, assessing the completeness ratio of 761 pathways. We classified pathways according to their maximum completeness rate, revealing that 137 pathways can be operated by at least one strain in our dataset. By mapping the identifiers of these pathways onto the pathway ontology graph of the Metacyc database, we highlighted that none of the pathways within four functional classes of Metacyc can be entirely recovered in the strain dataset. We then investigated infraspecific variability, a strong indicator of functional variability, and compared the species in our genome dataset based on their tendency to exhibit infraspecific variability. This analysis revealed species potential for strain-dependency, where phenotypes differ among strains of the same species.

**Keywords:** GSM, bacterial strain dependency, in silico pathway screening, lactic acid bacteria (LAB)

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

<footnote n="1">¹Univ Rennes, Inria, CNRS, IRISA, Rennes, 35000, France,</footnote>

<footnote n="2">²INRAE-Institut Agro, UMR STLO, Rennes,</footnote>
## Introduction

Bacterial genomes of interest to the food industry exhibit a wide range of functional variability, facilitated by mechanisms such as horizontal gene transfer or genomic islands (Stefanovic et al., 2017). However, precisely determining the functional roles of these genomes and identifying which species exhibit variability remain challenging. To address these questions, public repository databases are increasingly consolidating more and more genomes; the NCBI database (Sayers et al., 2024) features 255,669,865 annotated sequences in GenBank format and 4,152,691,448 sequences from WGS studies as of February 2025. This vast amount of data enables large-scale analyses of metabolic capabilities, including studies aimed at selecting organisms encoding enzymes catalyzing specific reactions of interest. By focusing on the reactions within a given pathway, we can identify organisms capable of addressing specific challenges through their metabolism—either by synthesizing valuable compounds or degrading unwanted metabolites. The latter approach requires linking a specific pathway to its reactions, the enzymes catalyzing them, their genes, and identifiers—information stored in databases such as KEGG (Kanehisa and Goto, 2000; Kanehisa, 2019; Kanehisa et al., 2025) or MetaCyc (Caspi et al., 2020).

Ultimately, the greatest challenge lies in the large-scale analysis of potential metabolic capabilities using tools designed to construct genome-scale metabolic networks (GSMs). While several solutions exist for building GSMs—such as (Vezina et al., 2023) which relies on ModelSEED (Seaver et al., 2021) and AutoKEGGRec (Karlsen et al., 2018), very few are scalable to hundreds of genomes. Converting annotated genomes into metabolic data demands extensive computational parallelization, a task that tools like Mpwt (Belcour et al., 2020) or CarveMe (Machado et al., 2018) can accomplish. Working with large datasets enables the identification of metabolic specificities but requires strictly standardized processing of raw data to prevent annotation biases or inconsistencies arising from non-homogeneous datasets. To address these challenges, we present Prolipipe (AuReMe/prolipipe 2025), a tool for large-scale metabolic profiling of bacteria, focusing on specific pathways. Its strategy is based on raw genomes as input, which are processed through a robust annotation step, followed by targeted and standardized metabolic network construction. To evaluate its scalability and accuracy, Prolipipe was applied to a dataset of 1,494 bacterial genomes, analyzing 761 MetaCyc pathways. The results revealed infraspecific variability in strains of the same species, demonstrating Prolipipe's capacity to uncover strain-specific metabolic capabilities.

## Material and methods

**Creation of a large catalogue of bacterial genomes.** A dataset of lactic acid bacteria's genomes (LAB) is built in compliance with the following restrictions: the genomes had to be qualified as presumptively safe (QPS) according to the EFSA agreement (EFSA, 2022), display no ability for sporulation, or have any known pathogenic effects on plants. As a result, 1,494 LAB genomes were retrieved from the NCBI FTP server in the fna FASTA format. Taxon and strain files were built to complete Prolipipe's inputs by linking species names and taxonomic identifier to files and strain name and status to files, respectively. The list of considered strains is available at https://github.com/NoeRobert1/prolipipe_on_LAB.
**Selection and creation of Prolipipe-compatible pathways files.** The MetaCyc database (version 27.0) references a total of 3,489 pathways. From these, we selected 1,493 pathways classified as "observed in bacteria" to ensure our analysis focused on reactions catalyzed by enzymes with documented evidence in bacterial genomes. Since pathways with two or fewer reactions—comprising half of the initial set—can introduce bias into downstream metrics (such as pathway completeness, which depends on the number of reactions), they were excluded from the study. This filtering process resulted in a final set of 761 pathways for analysis.

**Prolipipe, a glance of the workflow.** We implemented Prolipipe, a Python package whose steps are summarized in Figure 1. Given a large number of genomes along with a taxon file, a strain file and a list of metabolic pathways, the first step of Prolipipe consists of **genome annotation** using a homogeneous structural and functional annotation approach. This step relies on three tools that have demonstrated their individual role in gene annotation: Prokka version-1.14.6 (Seemann, 2014) using the Prokka database (version 20/02/2023) with the `-compliant` flag; EggNOG-mapper v2.1.12 (Cantalapiedra et al., 2021) relying on the eggNOG 5.0 database (Huerta-Cepas et al., 2019) and using the `-itype genome` and `-genepred prodigal` options; Bakta 1.8.2 (Schwengers et al., 2021) with its own database (version 20/02/2023). Each of these tools generates output files in GenBank format, resulting in three annotation versions per strain. The second step consists of **generating draft genome-scale metabolic networks (GSM).** To achieve this, GBK files are processed via a parallelized application of the PathoLogic algorithm from Pathway Tools (Karp et al., 2021) using the Python package Mpwt (Belcour et al., 2020). The previously generated taxon file is provided to refine the analysis, and the `-patho` flag is used to allow PathoLogic inference and the integration of all reactions from the MetaCyc database. At this stage, draft GSMs (three per genome) contain sets of .dat files, the native format of Pathways Tools. These files are managed through the PADMet toolbox (Aite et al., 2018), a Python package offering a comprehensive suite of tools for metabolic pathway reconstruction and annotated genome comparison. For this study, we used the *pgdb_to_padmet* command along with the `-extract-gene`, `-no-orphan`, and `-source` options for downstream benchmarking, as well as `-padmetRef` option to retain only reactions associated with gene sequences from the draft GSMs and spontaneous reactions. This command generates a single .padmet file per strain and annotation tool. Finally, padmet files corresponding to the same strain are merged to create a consensus GSM per strain using the *padmet_to_padmet* command.

**Prolipipe's GSM post-analysis and metabolic networks database generation.** GSMs are then queried to assess the metabolic profile of each strain based on the input pathways. Prolipipe compiles a database from all GSMs described within the Padmet files, enabling further comparisons using the *compare_padmet* command from the PADMet toolbox (Aite et al., 2018). All results are aggregated into four files, including one named reactions.tsv which documents the presence or absence of reactions across all processed strains. This file is analyzed by Prolipipe to produce pathway-specific result tables, which are then used to generate heatmaps that highlight strain dependency. Alternatively, Prolipipe can produce output files compatible with a SPARQL-endpoint, facilitating the generation of a queryable database. The pipeline is available on GitHub (https://github.com/AuReMe/prolipipe).
<figure><img src="image_3.png" /><figcaption>**Figure 1** – *Prolipipe application to our dataset.* 1,494 genomes along with a taxon file, a strain file and 761 metabolic pathways were processed using Prolipipe, which first annotated the genomes before constructing their genome-scale metabolic networks (GSM). These GSMs were then aggregated into a database and queried to assess the metabolic ability of each strain for each given pathway; the results were stored in pathway-specific tables which were subsequently used to generate heatmaps.</figcaption></figure>

## Results

**Benchmarking of annotation tools used.** A catalogue of 1,494 bacterial GSMs has been built using Prolipipe, which records the annotation source when annotating genomes using Eggnog-mapper, Prokka and Bakta. Genomes were grouped by species and the number of reactions ranged from 845 to 1,918 reactions, as shown in Figure 2. The maximum variance observed originates from the species *Propionibacterium freudenreichii*, where the minimum number of reactions detected in genomes is notably low (845 reactions). This discrepancy results in a maximum standard deviation among strains of the same species of 117, reflecting heterogeneity in the number of annotations derived from the three annotation sources. The bar chart illustrates the contribution of the three annotation tools across all 1,494 GSMs. We observed that the three annotation tools generate consensus predictions accounting for 30 to 43% of the size of the GSMs. Additionally, our analysis highlights a distinct distribution of annotation confidence levels: consensual annotations (predicted by all three tools), strongly supported annotations (predicted by two tools), and tool-specific annotations (predicted by only one tool). Tool-specific annotations represent 10 to 16% of all reactions in genome-scale metabolic models (GSMs). Within this category, Bakta-specific annotations show a significant enrichment in Enzyme Commission (EC) numbers linked to hydrolases acting on halide bonds (EC 3.8.-.-) and ether bonds (EC 3.3.-.-). EggNOG annotations, in turn, are enriched in cysteine-type carboxypeptidases (EC 3.4.18.-) and oxidoreductases acting on other nitrogenous compounds as donors (EC 1.7.-.-). Prokka annotations, meanwhile, exhibit enrichment in endoribonucleases producing products other than 5'-phosphomonoesters (EC 3.1.27.-) and oxidoreductases acting on hydrogen as donors (EC 1.12.-.-).

**Maximum completeness rate per pathway.** Prolipipe pipeline was executed on 1,494 bacterial genomes to assess the completeness rate of 761 bacterial metabolic pathways for each
<figure><img src="image_4.png" /><figcaption>**Figure 2** – Annotation tools benchmark by evaluating the number of annotations from each tool combination within all 1,494 strains. Species are labelled on their last individual. Annotations of enzymes that resulted in a MetaCyc reaction identifier are obtained by PathwayTools (via mpwt) from EC numbers and GO terms.</figcaption></figure>

genome. This rate is calculated as the ratio of reactions within the pathway linked to a gene, as identified by at least one annotation tool, to the total number of reactions in the pathway.

The bar chart in Figure 3A illustrates the distribution of pathways according to their maximums completeness rate, grouped into 10% completeness intervals, with a dedicated column for pathways achieving 100% completeness. Since placement within the intervals depends heavily on the number of reactions in each pathway, bars are color-coded accordingly. A total of 187 pathways exhibited low completeness rates below 10%. This corresponds with the fact that the 1,494 genomes catalogue is composed of lactic acid bacteria which are known for their specialized metabolic properties and inability to perform all known metabolic pathways. Conversely, 137 pathways achieved full completeness in at least one strain, as indicated by the [100%] interval column. Furthermore, 29 pathways exhibited a completeness between 80% and 100%, prompting further analysis. Among them, 10 pathways contained at least one reaction lacking an affiliated EC identifier.

Focusing on the 137 pathways completed by at least by one strain, the sunburst diagram from Figure 3B illustrates their distribution within the Metacyc database ontology for all 761 bacterial pathways. Ontology classes shown in color indicate the presence of at least one fully completed within that class, such as galactose degradation (MetaCyc ID : PWY-6317). This pathway aligns with the expected metabolic profile of lactic acid bacteria LAB - known for being able to catabolize this sugar present in milk - used in this study (Wang et al., 2021). Prolipipe's table output reveals that 1,384 strains out of 1,494 contain all reactions necessary for this pathway. In contrast, ontology classes depicted in gray indicate categories with no fully completed pathways. These include toxin biosynthesis, degradation of aromatic compounds, lipopolysaccharide synthesis and O-antigen biosynthesis.

**Infraspecific variability assessment.** Given a pathway, differences in completeness rates between strains of the same species indicate the potential for infraspecific metabolic variability. These differences reflect variations in genes annotation related to a specific pathway among strains of the same species. By aggregating data derived from pathway completion heatmaps (e.g., Figure 5), we can directly assess infraspecific metabolic variability : since these heatmaps
<figure><img src="image_5.png" /><figcaption>**Figure 3** – A) Bar chart of pathway completeness rate distribution. 761 pathways are dealt depending on their completeness rate which is split into 10%-completeness brackets, with an eleventh column for strictly complete pathways. The number of reactions per pathway is indicated with the shade of color.</figcaption></figure>

B) Sunburst diagram on pathways ontology. 137 pathways are found completed by at least one of the dataset's strain and their repartition in the MetaCyc ontology is screened on all the 761 processed pathways, with colored areas having at least one of these completed pathways. The colorbar used shows headcounts in classes ; that way, colored classes show which pathways can be completed by the dataset's strains.
calculate the percentage of strains falling within specific completion brackets, the absence of a "100" value in a given row indicates variability in pathway completion within the same species (i.e., the "100" value is distributed across multiple columns). To quantify this phenomenon, we counted the occurrence of such events and normalized the result by the total number of pathways analyzed. The bar chart depicted in Figure 4 obtained that way shows the percentage of infraspecific variability observed over 761 pathways per species (45 species represented by 1,485 strains of the catalogue have more than 1 individual, out of 54 species). This percentage ranges from 0 to 43.9% of all pathways with one hand *Brevibacillus brevis*'s B showing 334 pathways out of 761, suggesting intra-species metabolic variability. On the other hand *Bifidobacterium longum* shows less variability with a ratio of 25.8% (196 out of 761) while being the most represented species with 145 individuals. This example illustrates that, even though species are not equally represented in the genome catalogue, differences in intra-species metabolic variability potential can still be detected. Infraspecific variability may occur when only a subset of a species' strains complete a given pathway, leading to phenotypic differences between these strains. This phenomenon, known as strain dependency, is discussed further below.

**Statistical test on the correlation between assembly quality and downstream completion score.**

The dataset comprises genomes assembled at various levels of completeness: Contig (444), Scaffold (570), Chromosome (1), Complete Genome (443), and unknown (36). Given that differences in assembly quality may influence intraspecific diversity due to potential sequence gaps, we investigated the relationship between pathway completion rates and genome assembly quality. Genomes were categorized into two groups: "assembled" (complete genomes and chromosomes) and "draft" (scaffolds, contigs, and unknown assemblies). For each metabolic pathway, we calculated the number of genomes achieving the highest completion rates and assessed their distribution across the two assembly quality classes. To determine whether genome assembly quality affects pathway completion, we performed a Pearson's Chi-Square test for each pathway, with the null hypothesis ($H_0$) stating no relationship between assembly quality and optimal completion scores. A significance threshold of 0.05 was applied. Out of the 761 pathways initially tested, we excluded those with uniform results—specifically, pathways lacking any detectable reactions in the dataset, where all genomes were trivially classified as having the "best completion score". Among the remaining 583 pathways, statistical analysis revealed that 398 pathways exhibited completion rates independent of assembly quality, i.e. 68% of pathways. Conversely, 185 pathways demonstrated a significant correlation between genome assembly quality and optimal pathway completion rates.

**Focusing on L-arginine biosynthesis through acetyl cycle.** The heatmap in Figure 5 depicts the completeness rate of the L-arginine biosynthesis through the acetyl cycle, a metabolic pathway enabling organisms to produce L-arginine from L-glutamine and L-glutamate through 9 different reactions. completeness ratios were obtained for the 1,494 strains (covering 54 species) of our dataset using Prolipipe. The X-axis divides completeness percentage values into 10%-intervals, strains are ordered taxonomically and grouped into the 54 species of the dataset, and color shade represents the proportion of strains within a species whose completeness rate falls within a specific interval.

A total of 23 species exhibited a fully colored cell in the 100% completeness column, indicating that all their representative strains achieved full pathway completeness. These species are strong candidates for carrying out the pathway and producing L-arginine under suitable environmental
<figure><img src="image_6.png" /><figcaption>**Figure 4** – **Bar-chart of infraspecific variability frequency.** Each species represented by more than one individual is represented by a bar, with the horizontal axis indicating the ratio of observed infraspecific variability across all 761 analyzed pathways. Infraspecific variability is identified when two strains within a species exhibit completeness rates for a given pathway that differ by at least 10%. This suggests that the strains in question have different enzymatic capabilities associated with the metabolic pathway.</figcaption></figure>

conditions. For example, *Streptococcus thermophilus* (70 strains) achieved 100% completeness, confirming its status of candidate for carrying out the pathway entirely.

In contrast, 28 species did not contain any strain with 100% completeness rate. Among these, some exhibited high variability in completeness rates. *Lactobacillus delbrueckii*, for instance, displayed five different completeness rates represented by several cells of different shades on a same line : two strains have genomes annotated with the genes of one reaction ; 21 strains are associated with two reactions ; nine strains are associated with three reactions ; six strains are associated with four reactions and 36 strains are associated with five reactions. For these 28 species, the ability to synthesize L-arginine through the acetyl cycle is considered unlikely.

Finally, three species -*Leuconostoc lactis*, *Lactobacillus fermentum* and *Lactococcus cremoris*- displayed infraspecific variability. In *Leuconostoc lactis*, 6 out of 7 strains (85.7%) achieved 100% completeness. Similarly, in *Lactobacillus fermentum* and *Lactococcus cremoris*, 87.1% and 90% of strains, respectively, reached full completeness. These findings indicate that most strains within these species have the genetic potential to synthesize L-arginine through the acetyl cycle. However, other strains associated with these species have low completeness rates. This suggests that the considered species are candidate to be strain-dependent with respect to the phenotype of arginine production (Bringel and Hubert, 2003).

Discussion

From the previous figures, we demonstrated that Prolipipe has significant potential for study-
ing large-scale datasets of bacterial genomes and enables two key analyses. The first key analysis
is a large-scale detection of pathways which are promising candidates for the effective produc-
tion or degradation of metabolites based on their genome content. In this context, Prolipipe can
be used for genome mining by identifying bacteria with the highest potential for pathways of
interest, such as biosynthesis of target compounds or degradation pathways. This process is facil-
itated by the use of sunburst diagrams that utilize pathway ontology to identify over- and under-
represented functional classes. An example was provided with galactose degradation, where
Prolipipe output indicated that this pathway is predominantly present in our strains' genomes,
which corroborates the literature (Wang et al., 2021) and demonstrates its compatibility with
genome mining approaches.
<figure><img src="image_7.png" /><figcaption>**Figure 5** – Completeness heatmap of ARGSYNBSUB-PWY (Metacyc ID of L-arginine biosynthesis pathway through acetyl cycle) within all 1,494 genomes. Completeness ratio among the 54 species (represented in line, headcount given in label) is dealt on 10%-completion brackets, proportion of individuals inside a species in shades of purple.</figcaption></figure>

A second key analysis is the detection of infraspecific variability in pathway completeness rate within large-scale genome catalogues. This analysis can be extended to comparison between species, provided that the dataset is homogeneous in terms of species representation. Indeed, in our current catalogue, species headcounts range from 1 individual to 145, thereby complicating the discrimination between true infraspecific variability, known to exist within LAB (Thierry et al., 2015) and biases arising from unequal species representation. Nevertheless, these two aspects -the detection of accessible pathways and infraspecific variability- are directly visualized in readily available, pathway-specific heatmaps which serve as valuable tools for more detailed studies of metabolic profiles related to a given metabolic pathway within a bacterial dataset. Moreover, such displays can reveal candidates for strain-dependency, defined as differences in pathway completeness rate among strains of the same species, with a subset of the
specie's strains achieving 100% completeness of the pathway. It is important to note that, since
this analysis is based solely on genomic data, the associated phenotypic traits must be validated
experimentally.

Prolipipe's annotation step integrates three complementary annotation methods, each signif-
icantly enhancing the identification of metabolic reactions within genome-scale metabolic (GSM)
models. By leveraging all three methods, the pipeline ensures a comprehensive and robust anno-
tation process. The primary objective of Prolipipe is to efficiently screen large genomic datasets,
prioritizing the reduction of candidate genomes to a manageable subset—even at the risk of in-
troducing false positives. Since each annotation tool relies on distinct sequence databases for
gene identification, the use of multiple tools mitigates potential biases that could arise from
database-specific enrichments. This multi-tool approach prevents the overrepresentation of re-
actions that might otherwise dominate if a single database were disproportionately enriched for
certain sequences. Additionally, because Prolipipe tracks the origin of all gene-reaction associa-
tions, it enables the ranking of pathways based on the number of annotation tools supporting
each gene association, thereby enhancing confidence in functional predictions.

The investigation of nearly completed pathways revealed the risk of false negatives due to
gaps in the MetaCyc database, as some reactions lack an Enzyme Commission (EC) number to
link annotation to metabolic data. This limitation persists despite the explicit inclusion of spon-
taneous reactions during the construction of metabolic networks. The dataset displays hetero-
geneity in assembly quality, which may contribute to the observed variability in enzyme anno-
tation counts across genomes due to potential sequence gaps. However, prior statistical analy-
ses demonstrate that pathway completion scores are independent of genome quality for 68%
of the pathways examined. While working with such a dataset introduces a risk of false neg-
atives, this limitation must be carefully considered when interpreting metrics like intraspecific
variability. Additionally, there is a risk of false positives arising from the triple annotation pro-
cess, where pseudogenes may be erroneously identified as functional genes. Such risk can be
mitigated by adjusting the annotation tools' parameters and increasing the stringency of gene
coverage criteria during gene detection. However, due to these potential errors, Prolipipe should
be considered as a preliminary tool for assessing diversity potential. Its primary role is to filter
out the least promising strains, without guaranteeing quality or exhaustiveness -questions that
can be addressed once the genomes catalogue is significantly reduced. In that way, validation
of Prolipipe hypotheses is only possible through in vitro experimentations, such as controlled
fermentation in bioreactors. Prolipipe currently relies on MetaCyc, a database recognized for
its gaps due to annotation biases that disproportionately favor aerobic and pathogenic bacteria.
However, by analyzing large-scale genomic catalogs, Prolipipe can flag such gaps as anomalies
within the pipeline. For instance, the systematic absence of a reaction—stemming from the lack
of detected enzyme sequences in extensive genomic datasets—may reflect limitations in the
database, such as incomplete functional annotations or gene-reaction associations, rather than
a true biological absence. To address these challenges, Prolipipe implements an "adjusted com-
pletion rate" metric. This approach excludes undetected enzymes, focuses on identifying candi-
date strains, and explicitly acknowledges potential incompleteness in pathway characterization.
The primary limitation of this approach arises when analyzing pathways lacking gene-reaction
associations (linked via Enzyme Commission (EC) and MetaCyc identifiers), as the entire work-
flow depends on these connections. The previously quoted tool suite Padmet, which is part of
the workflow, allows enrichment of the database with reactions coming from other sources, although the following has not been tested yet. The development of specialized annotation tools targeting enzymes presents a promising solution to this bottleneck, and Prolipipe is designed to integrate such tools to enhance its completeness. Prolipipe's capabilities are currently being extended to metagenomics, where metabolic capability assessment would not be limited to individual organisms but would encompass entire bacterial communities, facilitating research on metabolic complementarity. This extension is expected to be valuable for the development of bacterial consortia to address challenges that single strains cannot overcome. Additionally, Prolipipe is being adapted for eukaryotic analysis, specifically for assessing metabolic machineries in yeasts.

## Acknowledgements

The authors thank the bioinformatics core facility Genouest (https://www.genouest.org/). A preprint version of this article has been peer-reviewed and recommended by PCI Genomics (https://doi.org/10.24072/pci.genomics.100441).

## Fundings

The authors declare that they have received no specific funding for this study.

## Conflict of interest disclosure

The authors declare that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.

## Data, scripts, code, and supplementary information availability

Data are available online (https://doi.org/10.5281/zenodo.15351076)
Scripts and code are available online (https://github.com/AuReMe/prolipipe)

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