Biology subjects

Mainguy, J.

Publications and source records attributed to Mainguy, J..

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↗

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

Nested PCR to optimize rpoB metabarcoding for low-concentration and host-associated bacterial DNA

BackgroundHousekeeping genes have proved effective taxonomic markers for characterizing bacterial microbiota in short-read amplicon metabarcoding studies. A region of the rpoB gene, in particular, has been shown to minimize OTU overestimation bias with a high degree of accuracy, providing better species-level taxonomic resolution (Ogier et al. BMC Microbiol. 19:171). However, the primers for rpoB are highly degenerate, leading to potential problems in the amplification of bacterial DNA present at low concentration in the sample or embedded within eukaryotic matrices, as for the host-associated microbiota. We addressed these limitations by using a two-step PCR approach to optimize the rpoB procedure. The first PCR amplifies a 906-nucleotide region of the rpoB gene with the classical primers, referred to here as outer primers, and the second PCR then uses primers incorporating Illumina adapters, referred to here as inner primers, to amplify a 435-nucleotide subregion, the taxonomic marker for metabarcoding. ResultsWe first used in silico approaches to evaluate the universality of the outer and inner rpoB primers. We then tested the nested rpoB PCR method on commercial mock samples of known composition. The nested PCR approach increased amplification efficiency for dilute samples without biasing the bacterial composition of the mock sample revealed by metabarcoding relative to single-step PCR. We also tested the nested rpoB PCR method on field-collected samples of the lepidopteran Spodoptera frugiperda. The nested PCR outperformed single-step PCR, increasing amplification efficiency for bacterial DNA present at low concentrations (oral secretions from S. frugiperda) or embedded in eukaryotic DNA matrices (S. frugiperda larvae). ConclusionsThis method provides a promising new strategy for characterizing insect-associated microbiota that can also be applied to other host microbiomes.

microbiology↗

bioRxiv2024

metagWGS, a comprehensive workflow to analyze metagenomic data using Illumina or PacBio HiFi reads

BackgroundTo study communities of micro-organisms taxonomically and functionally, metagenomic analyses are now often used. If there is no reference gene catalogue, a de novo approach is required. Because genomes are easier to interpret than contigs, the recovery of metagenome-assembled genomes (MAGs) by binning of contigs from metagenomic data has recently become a common task for microbial studies. However, during this process, there is a significant loss of information between the assembly and the binning of contigs. This is why it is important to produce taxonomic and functional matrices for all contigs and not just those included in correct bins. In addition, Pacbio HiFi reads (long and of good quality) are now a possible, albeit more expensive, alternative to short Illumina reads. We therefore developed a workflow that is easy to install with dependencies fixed using singularity images and easy to use on a computing cluster, that is capable of analyzing either short or long reads, and that should allow analysis at the contig and/or bin level, depending on the users choice. Following is a presentation of metagWGS, a fully automated workflow for metagenomic data analysis. It uses a new tool for refining bins (called Binette) that we will demonstrate is more efficient than competing tools. MethodsmetagWGS is a Nextflow workflow distributed with two singularity images and complete documentation to facilitate its installation and use. Because the main original features of metagWGS concern binning (short and long reads) and the analysis of HiFi reads, we compared metagWGS with the MAG construction workflow proposed by PacBio to a public dataset used by Pacbio to promote its workflow. ResultsmetagWGS differs from existing workflows by (i) offering flexible approaches for the assembly; (ii) supporting short reads (Illumina) or PacBio HiFi reads; (iii) combining multiple binning algorithms with a new bin refinement tool, referred to as "Binette", to achieve high-quality genome bins; and (iv) providing taxonomic and functional annotation for all genes, all contigs built and bins. metagWGS produces more medium (708) and high-quality (255) bins on 11 public metagenomic samples from human gut data than the Pacbio HiFi dedicated workflow, referred to as the HiFi-MAGS-pipeline (659 medium quality bins and 231 high quality bins), primarily due to the better performance of Binette.

bioinformatics↗

bioRxiv2024

Binette: a fast and accurate bin refinement tool to construct high quality Metagenome Assembled Genomes.

Metagenomics enables the study of microbial communities and their individual members through shotgun sequencing. An essential phase of metagenomic analysis is the recovery of metagenome-assembled genomes (MAGs). In a metagenomic analysis, sequence reads are assembled into contigs, which are then grouped into bins based on common characteristics - a process known as binning - to generate MAGs. The approach of applying multiple binning methods and combining them in a process called bin refinement allows us to obtain more and higher quality MAGs from metagenomic datasets. We present Binette, a bin refinement tool inspired by metaWRAPs bin refinement module, which addresses the limitations of the latter and ensures better results. Binette achieves this by creating new hybrid bins using basic set operations from the input bin sets. CheckM2 is then used to assess bin quality and select the best possible bins.

bioinformatics↗

bioRxiv2020

Population structure and genomic evidence for local adaptation to freshwater and marine environments in anadromous Arctic Char (Salvelinus alpinus) throughout Nunavik, Canada

Distinguishing neutral and adaptive genetic variation is one of the main challenges in investigating processes shaping population structure in the wild, and landscape genomics can help identify signatures of adaptation to contrasting environments. Arctic Char (Salvelinus alpinus) is an anadromous salmonid and the most harvested fish species by Inuit people, particularly so in Nunavik (Canada), one of the most recently deglaciated region in the world. Unlike most other anadromous salmonids, Arctic Char occupy coastal habitats near their overwintering rivers during their marine phase. The main objective of this study was to document the putative neutral and adaptive genomic variation of anadromous Arctic Char populations in Nunavik (Quebec, Canada) and bordering regions. A second objective was to interpret our results in the context of fisheries management in Nunavik. We used genotyping-by-sequencing (GBS) to genotype 18,112 filtered single nucleotide polymorphisms (SNPs) for 650 individuals sampled in 23 locations (average sample size per location = 28) along >2,000 km of coastline. Our results reveal a hierarchical genetic structure, whereby neighboring hydrographic systems harbour distinct populations grouping within major oceanographic basins, namely the Hudson Bay, Hudson Strait, Ungava Bay and Labrador Sea. We found genetic diversity and differentiation to be consistent with both the expected post-glacial recolonization history and patterns of isolation-by-distance reflecting contemporary gene flow. Furthermore, using three gene-environment association (GEA) methods we found genomic evidence for local adaptation to freshwater and marine environmental components, especially in relation to sea-surface and air temperatures during summer, as well as salinity. Our results support fisheries management at a regional level, and other implications on hatchery projects and adaptation to climate change are discussed.

genomics↗