Biology subjects

Charvel, E.

Publications and source records attributed to Charvel, E..

3 recordsLinked to original sources

bioRxiv2026

The Vertebrate Genomes Project Phase I: A global reference genome resource

The Vertebrate Genomes Project (VGP) aims to produce complete and near-error-free reference genomes for all [~]70,000 extant vertebrate species1. Organized in four phases, it progressively targets all vertebrate orders, families, genera, and eventually all species. Here we present the completion of VGP Phase I, delivering reference genomes for [~]95% of vertebrate orders, along with additional lineages within those orders, totaling 816 species and 1.6 trillion base pairs of main haplotype sequence. These genomes were assembled and annotated over an 8-year period (2018-2026) of rapid advances in genome sequencing, assembly, and annotation methods2-4, alongside the growth of associated consortium initiatives and international collaborations5-9. They represent some of the highest-quality vertebrate genomes currently available, and most have become the primary reference for their respective species in public databases. Comparative analyses across a subset of 579 species when we reached a threshold of 85% of orders allowed us to reconstruct the genome of the last common ancestor of all vertebrates 500 million years ago, identify diverse modes of sex chromosome evolution, reveal clade-specific three-dimensional genome architecture, discover methylated epigenetic landscapes across vertebrates, and provide a framework for studying gene and pseudogene evolution, immune loci, cancer-associated genes, and other trait-associated loci. Approximately a quarter of this subset are listed as Vulnerable to Critically Endangered by the IUCN Red List of Threatened Species, and have enabled more advanced genomic investigations of extinction risk. VGP Phase I delivers a reference backbone for vertebrate genomics, enabling discoveries that would otherwise remain out of reach across evolution, conservation, and medicine. Talking pointsO_LIThe flagship paper of VGP Phase I C_LIO_LIThe highest quality collection of genomes within the eukaryotic domain of life C_LIO_LIEvolution of genome sequencing technology quality throughout VGP Phase I C_LIO_LIA driver project that has been a model for multiple large-scale, high-quality reference genome projects C_LIO_LIReleases all currently unpublished genomes in Phase I from scientific study embargoes C_LIO_LIMultiple biological discoveries across the vertebrate tree of life C_LI

genomics↗

bioRxiv2026

DipSkmer: Reference-free population genomics with diploid genome skims

Ecologists and conservation biologists rely on genetic diversity as a key essential biodiversity variable (EBV) used to track population health and dynamics, and utilize the population parameter{theta} (estimated by the average pairwise genomic distance) as a key metric of diversity. While whole-genome-sequencing (wgs) is increasingly affordable, it will be considerable time before the full diversity of life is represented by high-quality assembled genomes; even then, constant monitoring will still require repeated sampling of populations. In contrast, genome skimming (low-coverage, short-read wgs) is highly cost-effective but challenging to analyze because the coverage is too low for assembly and reliable error correction. Mature methods, such as Mash, exist for estimating pairwise genomic distances based on the Jaccard similarity of k-mer sets computed using sketching techniques. Some, such as Skmer, additionally model the impacts of low coverage. These methods have been successfully applied to assembly-free species identification and phylogenetics; however, their use in population genetics has been limited. This is because these methods implicitly treat genomes as haploid and heterozygosity confounds true estimates of genomic distance for diploid organisms. In this paper, we address this problem through a number of technical advances. First, we use coalescent theory to mathematically derive how the Jaccard index between two diploid samples changes with the scaled population size parameter ({theta}). Next, we derive an estimator that computes{theta} from the Jaccard index, in addition to several auxiliary variables, which we also estimate from the genome skims. The resulting method, DipSkmer, enables more accurate estimates of coverage, sequencing error, and pairwise nucleotide distance for diploid samples. Analyses of both simulated and empirical datasets show that for diploids and low distances (e.g., < 2%), Dip-Skmer produces the most accurate pairwise distance estimates, outperforming existing alignment-free methods such as Mash and Skmer, and closely approximates ANGSD, a reference and alignment-based tool. AvailabilityThe code for DipSkmer is available at https://github.com/echarvel3/ReSkmer/tree/DipSkmer-REFACTOR. Simulation scripts and environments are available at https://github.com/echarvel3/dipskmer_scripts. Author SummaryThe process of obtaining full-genome population genomic measurements for biodiversity monitoring remains expensive due to the need for high-coverage sequencing and reference assemblies. Genome skimming has been shown to be a viable, low-coverage alternative for obtaining genomic distances, and alignment- and assembly-free methods exist for analyzing nuclear data from skimming data to estimate the distance between samples. However, existing methods fail to model within-sample heterozygosity, expected for diploid organisms. Given the dominance of diploidy among species of interest to ecologists, the implications of these simplifying assumptions warrant further study. Here, we present a mathematical model of the k-mer sets sampled from two diploid genomes from a Wright-Fisher population. We use the model to develop DipSkmer, a k-mer-based, reference-free method for estimating nucleotide diversity and population divergence that, unlike its predecessors, models within-sample heterozygosity. Benchmarking shows more accurate genomic diversity estimates compared to existing reference-free, genome-skimming methods and comparable performance to the popular high-coverage, reference-based method, ANGSD. Thus, DipSkmer enables accessible, less expensive population monitoring through genetic diversity estimates.

bioinformatics↗

bioRxiv2022

Expectation-Maximization enables Phylogenetic Dating under a Categorical Rate Model

Dating phylogenetic trees to obtain branch lengths in time unit is essential for many downstream applications but has remained challenging. Dating requires inferring substitution rates that can change across the tree. While we can assume to have information about a small subset of nodes from the fossil record or sampling times (for fast-evolving organisms), inferring the ages of the other nodes essentially requires extrapolation and interpolation. Assuming a clock model that defines a distribution over rates, we can formulate dating as a constrained maximum likelihood (ML) estimation problem. While ML dating methods exist, their accuracy degrades in the face of model misspecification where the assumed parametric statistical clock model vastly differs from the true distribution. Notably, existing methods tend to assume rigid, often unimodal rate distributions. A second challenge is that the likelihood function involves an integral over the continuous domain of the rates and often leads to difficult non-convex optimization problems. To tackle these two challenges, we propose a new method called Molecular Dating using Categorical-models (MD-Cat). MD-Cat uses a categorical model of rates inspired by non-parametric statistics and can approximate a large family of models by discretizing the rate distribution into k categories. Under this model, we can use the Expectation-Maximization (EM) algorithm to co-estimate rate categories and branch lengths time units. Our model has fewer assumptions about the true clock model than parametric models such as Gamma or LogNormal distribution. Our results on two simulated and real datasets of Angiosperms and HIV and a wide selection of rate distributions show that MD-Cat is often more accurate than the alternatives, especially on datasets with nonmodal or multimodal clock models. Code availabilityThe MD-Cat software is available at https://github.com/uym2/MD-Cat. Data availabilityData are available on Github https://github.com/uym2/MD-Cat-paper and Dryad https://doi.org/10.5061/dryad.pk0p2ngs0.

bioinformatics↗