Biology subjects

Bazin, A.

Publications and source records attributed to Bazin, A..

6 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↗

bioRxiv2024

zIncubascope: long-term quantitative imaging of multi-cellular assemblies inside an incubator

Recent advances in bioengineering have made it possible to develop increasingly complex biological systems to recapitulate organ functions as closely as possible in vitro. Monitoring the assembly and growth of multi-cellular aggregates, micro-tissues or organoids and extracting quantitative information is a crucial but challenging task required to decipher the underlying morphogenetic mechanisms. We present here an imaging platform designed to be accommodated inside an incubator which provides high-throughput monitoring of cell assemblies over days and weeks. We exemplify the capabilities of our system by investigating human induced pluripotent stem cells (hiPSCs) enclosed in spherical capsules, hiPSCs in tubular capsules and yeast cells in spherical capsules. Combined with a customized pipeline of image analysis, our solution provides insight into the impact of confinement on the morphogenesis of these self-organized systems.

bioengineering↗

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↗