Biology subjects

Vallenet, D.

Publications and source records attributed to Vallenet, D..

11 recordsLinked to original sources

bioRxiv2026

PanGBank: a large-scale resource of precomputed microbial pangenomes built with PPanGGOLiN

PanGBank (https://pangbank.genoscope.cns.fr) is a comprehensive open-access database providing precomputed prokaryotic pangenomes at a broad taxonomic scale. Built upon PPanGGOLiN partitioned pangenome graphs, PanGBank addresses the growing need for large-scale comparative genomics through a standardized, regularly updated, and fully accessible resource. The initial release comprises two complementary collections covering more than 4,600 prokaryotic species from the Genome Taxonomy Database (GTDB), encompassing over 393,000 genomes: GTDB all, maximizing taxonomic and environmental diversity through the inclusion of MAGs and SAGs, and GTDB refseq, focusing on high-quality, annotation-rich genomes. Each species-level pangenome integrates graph-based statistical partitions into persistent, shell, and cloud gene families, together with regions of genomic plasticity (panRGP) and co-localized functional modules (panModule). PanGBank offers multiple access modes, including a REST API, a command-line interface (PanGBank-cli), and an interactive web interface. By combining large-scale pangenome resources with advanced graph-based analyses, PanGBank provides a scalable framework for exploring microbial diversity, genome evolution, functional variation, and the dissemination of adaptive traits across prokaryotic populations, as illustrated by a use case on Acinetobacter baumannii pangenome investigating the distribution and evolution of antimicrobial resistance determinants. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/742796v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@12c441org.highwire.dtl.DTLVardef@12b079org.highwire.dtl.DTLVardef@ffd7f8org.highwire.dtl.DTLVardef@bc013f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Panorama: a robust pangenome-based method for predicting and comparing biological systems across species

Over the last decade, the expansion in the number of available genomes has profoundly transformed the study of genetic diversity, evolution, and ecological adaptation in prokaryotes. However, traditional bioinformatic approaches based on the analysis of individual genomes are showing their limitations when faced with the sheer scale of the data. To overcome these constraints, the concept of pangenome has emerged, offering a comprehensive framework to capture the full genetic repertoire of a species. In this study, we present PANORAMA, an innovative pangenomic tool designed to exploit pangenome graphs and enable them to be annotated and compared in order to explore the genomic diversity of several species. Based on the PPanGGOLiN pangenome graphs, PANORAMA integrates advanced methods for rule-based prediction of macromolecular systems and comparative analysis of conserved features between different pangenomes, such as spots of insertion. We illustrate the use of PANORAMA on a dataset of 941 Pseudomonas aeruginosa genomes, evaluating its performance against reference defense system prediction tools such as PADLOC and DefenseFinder. The analysis was then extended to a larger set, including four species of Enterobacteriaceae (>6,000 genomes), demonstrating PANORAMAs ability to annotate, compare, and explore the diversity and distribution of biological systems across multiple species. This work provides new methods for the large-scale comparative study of microbial genomes and underlines the relevance of pangenome approaches in deciphering their evolutionary dynamics. PANORAMA is freely available and accessible through: https://github.com/labgem/PANORAMA Author summaryMicroorganisms are present in nearly all environments on Earth. Uncovering their diversity through the study of their genomes is essential for understanding their biology and evolution. This includes characterizing the species complete genetic repertoire, known as the pangenome. Such research also enables new applications in health, ecology, biotechnology, etc. Here, we present PANORAMA, a novel computational tool designed to predict macromolecular systems, such as defense mechanisms against phages, and to compare pangenome graphs across different species. By using rule-based models that combine gene function and genomic context, we can search for systems directly within pangenome graphs. This graph-based approach provides a global view of the functional content of entire species, moving beyond the analysis of individual genomes. It greatly facilitates the analysis of thousands of genomes by reducing the required computation time and directly integrating the results to identify shared and specific systems. Furthermore, PANORAMAs comparative functionality enables the identification of conserved structures across species, such as shared spots of insertion, revealing common evolutionary mechanisms and functional modules. This work establishes a foundation for comparative pangenomics, offering an unprecedented framework to explore the adaptive potential and evolutionary dynamics of prokaryotes at scale.

bioinformatics↗

bioRxiv2025

Identification of specific metabolic capacities associated with major extraintestinal pathogenic Escherichia coli lineages

Bacterial niche colonization relies on multiple factors, among which the metabolic capacity to utilize specific substrates is pivotal, especially within complex microbiota where nutrient competition is intense. Escherichia coli, a gut commensal of humans and vertebrates, is also an opportunistic pathogen responsible for both intestinal and extraintestinal infections, including bloodstream infections (BSI). To identify metabolic specificities of pathogenic strains that could explain the emergence and success of major clones, we leveraged a well-characterized dataset of 1,498 genomes from both commensal and extraintestinal pathogenic epidemiologically relevant French isolates. Using a pangenomic framework, we conducted a large-scale analysis of metabolic pathways. Although metabolism was far more conserved than gene content, we found substantial metabolic diversity across the species, with over 50% of pathways being variable, mainly involving biosynthetic and degradation processes. Phylogeny had a major impact on metabolic profiles, while no specific pathway was associated with lifestyle (commensal vs. pathogenic) or BSI portal of entry. However, we identified clone-specific metabolic capacities. For example, pathways involved in 5-deoxynucleoside recycling were enriched in the major extraintestinal pathogen (ExPEC) clone STc69. In phylogroup B2, several clone-specific pathways enabled degradation of plant-derived compounds. Notably, detailed analysis of the D-apiose degradation revealed a functional pathway strongly associated with the STc131 and STc14 pandemic clones. Overall, our study highlights multiple lineage-specific metabolic capacities that may contribute to the ecological success and dissemination of most prevalent ExPEC clones. ImportanceAccording to the nutrient-niche hypothesis, both commensal and pathogenic E. coli strains must exploit distinct substrates to grow and persist in their primary habitat alongside resident microbiota. Such niche differentiation also occurs between E. coli populations competing within the same environment and partly explains the stable coexistence of strains in the gut. With this in mind, we search for specific associations between metabolic pathways and strain origin. However, metabolic profiles were predominantly shaped by phylogeny, reflecting the species clonal structure and the close link between phylogenetic background and lifestyle. Among the lineage-specific determinants, we identified several pathways associated with worldwide spread clones responsible for bloodstream infections, supporting the existence of clone-specific strategies for niche adaptation.

genomics↗

bioRxiv2025

ASMC: investigating the amino acid diversity of enzyme active sites.

MotivationThe analysis of enzyme active sites is essential for understanding their activity in terms of catalyzed reaction and substrate specificity, providing insights for engineering to obtain targeted properties or modify the substrate scope. In 2010, a first version of the Active Site Modeling and Clustering (ASMC) workflow was published. ASMC predicts isofunctional clusters from enzyme families, based on structural modeling and clustering of active sites. Since then, structure- and sequence-based methods have developed considerably. ResultsWe present here a redesign of the ASMC workflow. This new major version includes recent pocket prediction, structural alignment and clustering methods, as well as a refined amino acid distance matrix, thereby improving the relevance of results and reducing the need for laborious manual analysis to obtain relevant clusters. In addition, we have implemented multiple sequence alignment (MSA) as a possible input for the clustering step, along with an additional script to compare 2D and 3D active sites. Finally, the code has been unified from three to one programming language (Python) to facilitate its installation and maintenance. This new version of ASMC was evaluated on a set of protein families, resulting in overall better performances compared to its original version. Availability and implementationASMC is supported on Linux operating system and freely available at https://github.com/labgem/ASMC, along with a complete documentation (wiki, tutorial). Contactvallenet@genoscope.cns.fr

bioinformatics↗

bioRxiv2023

NetSyn: genomic context exploration of protein families

Background: The growing availability of large prokaryotic genomic datasets presents an opportunity to discover new metabolic pathways and enzymatic reactions useful for industrial or synthetic biological applications. Efforts to identify new enzyme functions in this vast number of sequences cannot be achieved without bioinformatics tools and the development of new strategies. Standard methods for assigning a biological function to a gene are based on sequence similarity. However, complementary approaches rely on mine databases to identify conserved gene clusters (i.e. syntenies). In prokaryotic genomes, genes involved in the same pathway are frequently encoded in a single locus with an operonic organisation. This genomic context conservation is considered as a reliable indicator of functional relationships, and is therefore a promising approach for improving the gene function prediction. Methods. Here we present NetSyn (Network Synteny), a tool to group protein sequences based on the conservation of their genomic context rather than solely on sequence similarity. From a list of protein sequence identifiers, NetSyn searches corresponding genome entries to retrieve neighboring genes. Corresponding protein sequences are grouped into families to define homology relationships and compute a synteny conservation score between the different extracted genomic contexts. A network is then created in which the nodes represent the input proteins and the edges indicate that two proteins share a conserved synteny. Finally, the network is partitioned into clusters grouping proteins with similar genomic contexts, using a community detection algorithm. Results. As a proof of concept, we used NetSyn on two different datasets. The first one is the BKACE protein family (formerly named DUF849) which has previously been divided into isofunctional sub-families. NetSyn was able to go a step further by providing additional sub-families beyond those already described. The second dataset corresponds to a set of non-homologous proteins belonging to three different glycoside hydrolase (GH) families. These GHs are known to work cooperatively in a Polysaccharide-Utilization Loci (PUL) and are therefore grouped together in the same genomic contexts. NetSyn was able to identify a locus grouping 3 GHs, involved in the degradation of xyloglucan, in 162 prokaryotic genomes. Discussion. By highlighting conserved synteny in distantly related prokaryotic species, NetSyn enables functional links between proteins to be established beyond sequence similarity alone. We showed that NetSyn is efficient for exploring large prokaryotic protein families, enabling the definition of isofunctional groups and the identification of functional interactions between non-homologous enzymes. These features enable the prediction of new genomic structures that have not yet been experimentally characterized. Finally, NetSyn is also useful for pinpointing annotation errors that have been propagated across databases, and for suggesting annotations on proteins lacking functional prediction. NetSyn is freely available at https://github.com/labgem/netsyn.

bioinformatics↗

bioRxiv2021

panModule: detecting conserved modules in the variable regions of a pangenome graph

The recent years have seen the rise of pangenomes as comparative genomic tools to better understand the evolution of gene content among microbial genomes in close phylogenetic groups such as species. While the core or persistent genome is often well-known as it includes essential or ubiquitous genes, the variable genome is usually less characterized and includes many genes with unknown functions even among the most studied organisms. It gathers important genes for strain adaptation that are acquired by horizontal gene transfer. Here, we introduce panModule, an original method to identify conserved modules in pangenome graphs built from thousands of microbial genomes. These modules correspond to synteny blocks composed of consecutive genes that are conserved in a subset of the compared strains. Identifying conserved modules can provide insights on genes involved in the same functional processes, and as such is a very helpful tool to facilitate the understanding of genomic regions with complex evolutionary histories. The panModule method was benchmarked on a curated dataset of conserved modules in Escherichia coli genomes. Its use was illustrated through a study of a high pathogenicity island in Klebsiella pneumoniae that allowed a better understanding of this region. panModule is freely available and accessible through the PPanGGOLiN software suite (https://github.com/labgem/PPanGGOLiN).

bioinformatics↗

bioRxiv2020

Construction of a complete set of Neisseria meningitidis defined mutants - the NeMeSys collection - and its use for the phenotypic profiling of the genome of an important human pathogen

One of the great challenges in biology is to determine the function of millions of genes of unknown function. Even in model bacterial species, there is a sizeable proportion of such genes, which has fundamental and practical consequences. Here, we constructed a complete collection of defined mutants in protein-coding genes - named NeMeSys 2.0 - in the human pathogen Neisseria meningitidis, consisting of individual mutants in 1,584 non-essential genes. This effort identified 391 essential genes - broadly conserved in other bacteria - leading to a full overview of the essential meningococcal genome, associated with just four underlying basic biological functions: 1) expression of genome information, 2) preservation of genome information, 3) cell membrane structure/function, and 4) cytosolic metabolism. Subsequently, we illustrated the utility of the NeMeSys 2.0 collection for determining gene function by identifying 1) a novel and conserved family of histidinol-phosphatase, 2) 20 genes, including three new ones, involved in the biology of type IV pili, a widespread virulence factor, and 3) several conditionally essential genes found in regions of genome plasticity encoding antitoxins and/or immunity proteins, which become dispensable when the gene encoding the cognate toxin is deleted. These findings have implications beyond the meningococcus. The NeMeSys 2.0 collection is an invaluable resource paving the way for a global phenotypic landscape in a major human bacterial pathogen.

microbiology↗

bioRxiv2020

panRGP: a pangenome-based method to predict genomic islands and explore their diversity

MotivationHorizontal gene transfer (HGT) is a major source of variability in prokaryotic genomes. Regions of Genome Plasticity (RGPs) are clusters of genes located in highly variable genomic regions. Most of them arise from HGT and correspond to Genomic Islands (GIs). The study of those regions at the species level has become increasingly difficult with the data deluge of genomes. To date no methods are available to identify GIs using hundreds of genomes to explore their diversity. ResultsWe present here the panRGP method that predicts RGPs using pangenome graphs made of all available genomes for a given species. It allows the study of thousands of genomes in order to access the diversity of RGPs and to predict spots of insertions. It gave the best predictions when benchmarked along other GI detection tools against a reference dataset. In addition, we illustrated its use on Metagenome Assembled Genomes (MAGs) by redefining the borders of the leuX tRNA hotspot, a well studied spot of insertion in Escherichia coli. panRPG is a scalable and reliable tool to predict GIs and spots making it an ideal approach for large comparative studies. AvailabilityThe methods presented in the current work are available through the following software: https://github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https://github.com/axbazin/panrgp_supdata. Contactvallenet@genoscope.cns.fr and acalteau@genoscope.cns.fr Supplementary informationNone.

bioinformatics↗

bioRxiv2019

PPanGGOLiN: Depicting microbial diversity via a Partitioned Pangenome Graph

The use of comparative genomics for functional, evolutionary, and epidemiological studies requires methods to classify gene families in terms of occurrence in a given species. These methods usually lack multivariate statistical models to infer the partitions and the optimal number of classes and dont account for genome organization. We introduce a graph structure to model pangenomes in which nodes represent gene families and edges represent genomic neighborhood. Our method, named PPanGGOLiN, partitions nodes using an Expectation-Maximization algorithm based on multivariate Bernoulli Mixture Model coupled with a Markov Random Field. This approach takes into account the topology of the graph and the presence/absence of genes in pangenomes to classify gene families into persistent, cloud, and one or several shell partitions. By analyzing the partitioned pangenome graphs of isolate genomes from 439 species and metagenome-assembled genomes from 78 species, we demonstrate that our method is effective in estimating the persistent genome. Interestingly, it shows that the shell genome is a key element to understand genome dynamics, presumably because it reflects how genes present at intermediate frequencies drive adaptation of species, and its proportion in genomes is independent of genome size. The graph-based approach proposed by PPanGGOLiN is useful to depict the overall genomic diversity of thousands of strains in a compact structure and provides an effective basis for very large scale comparative genomics. The software is freely available at https://github.com/labgem/PPanGGOLiN. Author summaryMicroorganisms have the greatest biodiversity and evolutionary history on earth. At the genomic level, it is reflected by a highly variable gene content even among organisms from the same species which explains the ability of microbes to be pathogenic or to grow in specific environments. We developed a new method called PPanGGOLiN which accurately represent the genomic diversity of a species (i.e. its pangenome) using a compact graph structure. Based on this pangenome graph, we classify genes by a statistical method according to their occurrence in the genomes. This method allowed us to build pangenomes even for uncultivated species at an unprecedented scale. We applied our method on all available genomes in databanks in order to depict the overall diversity of hundreds of species. Overall, our work enables microbiologists to explore and visualize pangenomes alike a subway map.

bioinformatics↗

bioRxiv2018

PlaScope: a targeted approach to assess the plasmidome of Escherichia coli strains

Plasmid prediction may be of great interest when studying bacteria such as Enterobacteriaceae. Indeed many resistance and virulence genes are located on such replicons and can have major impact in terms of pathogenicity and spreading capacities. Beyond strains outbreak, plasmids outbreaks have been reported especially for some extended-spectrum beta-lactamase or carbapenemase producing Enterobacteriaceae. Several tools are now available to explore the \"plasmidome\" from whole-genome sequence data, with many interesting and various approaches. However recent benchmarks have highlighted that none of them succeed to combine high sensitivity and specificity. With this in mind we developed PlaScope, a targeted approach to recover plasmidic sequences in Escherichia coli. Based on Centrifuge, a metagenomic classifier, and a custom database containing complete sequences of chromosomes and plasmids from various curated databases, it performs a classification of contigs from an assembly according to their predicted location. Compared to other plasmid classifiers, Plasflow and cBar, it achieves better recall (0.87), specificity (0.99), precision (0.96) and accuracy (0.98) on a dataset of 70 genomes containing plasmids. Finally we tested our method on a dataset of E. coli strains exhibiting an elevated rate of extended-spectrum beta-lactamase coding gene chromosomal integration, and we were able to identify 20/21 of these events. Moreover virulence genes and operons predicted locations were also in agreement with the literature. Similar approaches could also be developed for other well-characterized bacteria such as Klebsiella pneumoniae.\n\nData summaryO_LIAll the genomes were downloaded from the National Center for Biotechnology Information Sequence Read Archive and Genome database (Supplementary table 1 and 2).\nC_LIO_LIThe source code of PlaScope is available on Github (https://github.com/GuilhemRoyer/PlaScope).\nC_LI\n\nImportancePlasmid exploration could be of great interest since these replicons are pivotal in the adaptation of bacteria to their environment. They are involved in the exchange of many genes within and between species, with a significant impact on antibiotic resistance and virulence in particular. However, plasmid characterization has been a laborious task for many years, requiring complex conjugation or electroporation manipulations for example. With the advent of whole genome sequencing techniques, access to these sequences is now potentially easier provided that appropriate tools are available. Many softwares have been developed to explore the plasmidome of a large variety of bacteria, but they rarely managed to combine sensitivity and specificity. Here, we focus on a single species, E. coli, and we use the many data available to overcome this problem. With our tool called PlaScope, we achieve high performance compared with two other classifiers, Plasflow and cBar, and we demonstrate the utility of such an approach to determine the location of virulence or resistance genes. We think that PlaScope could be very useful in the analysis of specific and well-known bacteria.

bioinformatics↗

bioRxiv2017

GROOLS: reactive graph reasoning for genome annotation through biological processes

BackgroundHigh quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation.\n\nResultsWe developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented.\n\nConclusionsGROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations.

bioinformatics↗