Biology subjects

Linard, B.

Publications and source records attributed to Linard, B..

7 recordsLinked to original sources

bioRxiv2026

Simulating population pangenomes under coalescent demographic models with MSpangenome

MotivationPangenome variation graphs (PVGs) are increasingly used to represent genomic diversity, yet there is currently no general framework for generating population pangenomes directly from explicit evolutionary histories. Existing simulators typically focus on individual classes of variation and do not integrate these variations within a genealogy-aware framework driven by explicit demographic histories. As a result, evaluating pangenome methods in realistic population-genetic settings remains challenging, and benchmark datasets with known evolutionary ground truth are scarce. ResultsWe present MSpangenome, a genealogy-aware framework that bridges coalescent population genetic simulations and pangenome graph analyses. The pipeline combines ancestry simulation with msprime and a de novo graph construction algorithm to generate PVGs directly from simulated genealogies. By explicitly modeling recombination, demographic history and incomplete lineage sorting, MSpangenome produces structurally complex pangenomes in which nested and overlapping structural variants emerge naturally from the underlying genealogies, while their evolutionary history and graph topology remain known by construction. This provides a general framework for generating realistic population pangenomes and establishing ground-truth datasets for methodological evaluation. We demonstrate its utility by generating population-scale pangenomes and using them as controlled references to benchmark the widely used graph construction tools, PGGB and Minigraph-Cactus. Our analyses reveal contrasting performance regimes across levels of sequence diversity, sample sizes and classes of structural variation, highlighting the value of simulation-based benchmarking for identifying reconstruction errors that are hard to detect using empirical datasets alone. Availability and implementationMSpangenome is implemented in Python, fully containerized, freely available at https://forge.inrae.fr/pangepop/MSpangepop and mirrored at https://github.com/inrae/MSpangepop.

bioinformatics↗

bioRxiv2026

Pan1c : a pipeline to easily build chromosome-level pangenome graphs

The advances of sequencing technologies and the availability of high-quality genome assemblies for many genotypes per species, give the opportunity to improve sequence alignment rate and quality, and the variant calling accuracy by including all genomic variations in a graph reference, called a pangenome graph. Because the process of building and analysing a pangenome graph is still complex, with related software packages under development, there is an important need for releasing user-friendly pipelines for this emerging research area. Pan1C is a pipeline based on a chromosome-by-chromosome graph construction strategy. It integrates two complementary strate-gies for building pangenomes and produces informative metric plots and graphics using a large set of tools. By benchmarking Pan1C on human, fungal, and wheat assemblies, which span a wide range of genome sizes and complexities, we showed the interest of Pan1C for assembly and graph validation as well as for performing primary analyses.

bioinformatics↗

bioRxiv2025

Building pangenomes for domesticated and wild tree species: genomic complexity and strategies

Long-read sequencing and pangenomics are revolutionizing crop research by providing more complete genome information and revealing crucial structural variations linked to important agricultural traits. Building on recent advances in intraspecific pangenome construction, this study addresses the challenge of creating broader, cross-taxon pangenomes, using the Armeniaca taxonomic section as a model. Leveraging a diverse panel of genome assemblies, we constructed a pangenome graph and cataloged associated single nucleotide polymorphisms (SNPs) and structural variants. We characterized the diversity of these variants and assessed the extent to which different taxa contribute to overall pangenome expansion. Additionally, we evaluated the performance of low-depth sample mapping to the graph-based reference, highlighting key technical limitations that may affect the quality of downstream analyses. We further identified specific subsets of SVs that exhibit associations with particular classes of transposable elements. As a case study illustrating the potential functional and phenotypic relevance of graph-derived SVs, we examined the genomic configuration of the DAM locus within the Armeniaca pangenome.

bioinformatics↗

bioRxiv2020

Rapid screening and detection of inter-type viral recombinants using phylo-k-mers

MotivationNovel recombinant viruses may have important medical and evolutionary significance, as they sometimes display new traits not present in the parental strains. This is particularly concerning when the new viruses combine fragments coming from phylogenetically-distinct viral types. Here, we consider the task of screening large collections of sequences for such novel recombinants. A number of methods already exist for this task. However, these methods rely on complex models and heavy computations that are not always practical for a quick scan of a large number of sequences. ResultsWe have developed SHERPAS, a new program to detect novel recombinants and provide a first estimate of their parental composition. Our approach is based on the precomputation of a large database of "phylogenetically-informed k-mers", an idea recently introduced in the context of phylogenetic placement in metagenomics. Our experiments show that SHERPAS is hundreds to thousands of times faster than existing software, and enables the analysis of thousands of whole genomes, or long sequencing reads, within minutes or seconds, and with limited loss of accuracy. Availability and ImplementationThe source code is freely available for download at https://github.com/phylo42/sherpas Contactpardi@lirmm.fr, gllm.scholz@gmail.com Supplementary informationSupplementary Materials are available online.

bioinformatics↗

bioRxiv2018

Rapid alignment-free phylogenetic identification of metagenomic sequences

MotivationTaxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However,likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing.\n\nResultsWe have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics.\n\nAvailabilityProgram and sources freely available for download at gite.lirmm.fr/linard/RAPPAS.\n\nContactbenjamin.linard@lirmm.fr

bioinformatics↗

bioRxiv2018

The contribution of mitochondrial metagenomics to largescale data mining and phylogenetic analysis of Coleoptera

A phylogenetic tree at the species level is still far off for highly diverse insect orders, including the Coleoptera, but the taxonomic breadth of public sequence databases is growing. In addition, new types of data may contribute to increasing taxon coverage, such as metagenomic shotgun sequencing for assembly of mitogenomes from bulk specimen samples. The current study explores the application of these techniques for large-scale efforts to build the tree of Coleoptera. We used shotgun data from 17 different ecological and taxonomic datasets (5 unpublished) to assemble a total of 1942 mitogenome contigs of >3000 bp. These sequences were combined into a single dataset together with all mitochondrial data available at GenBank, in addition to nuclear markers widely used in molecular phylogenetics. The resulting matrix of nearly 16000 species with two or more loci produced trees (RAxML) showing overall congruence with the Linnaean taxonomy at hierarchical levels from suborders to genera. We tested the role of full-length mitogenomes in stabilizing the tree from GenBank data, as mitogenomes might link terminals with non-overlapping gene representation. However, the mitogenome data were only partly useful in this respect, presumably because of the purely automated approach to assembly and gene delimitation, but improvements in future may be possible by using multiple assemblers and manual curation. In conclusion, the combination of data mining and metagenomic sequencing of bulk samples provided the largest phylogenetic tree of Coleoptera to date, which represents a summary of existing phylogenetic knowledge and a defensible tree of great utility, in particular for studies at the intra-familial level, despite some shortcomings for resolving basal nodes.

evolutionary biology↗

bioRxiv2017

EvoKEN: evolutionary knowledge extraction in networks

We introduce a multi-factorial, multi-level approach to build and explore evolutionary scenarios of complex protein networks. EvoKEN combines a unique formalism for integrating multiple types of data associated with network molecular components and knowledge extraction techniques for detecting cohesive/anomalous evolutionary processes. We analyzed known human pathway maps and identified perturbations or specializations at the local topology level that reveal important evolutionary and functional aspects of these cellular systems.

evolutionary biology↗