Biology subjects

de Vienne, D. M.

Publications and source records attributed to de Vienne, D. M..

9 recordsLinked to original sources

bioRxiv2025

pylifemap: Mapping Data onto the Tree of Life

O_LIThe need to visualize data associated with NCBI Taxonomy Identifiers is growing in various biological fields ranging from comparative genomics to metagenomics and metabarcoding, and even for outreach. C_LIO_LINo tool today allows visualization of such data while still keeping the full vision of the whole taxonomy, possibly causing a biased view of the data at hand. C_LIO_LIHere we introduce pylifemap, a Python package that allows users to map their own data directly on the interactive taxonomic tree proposed by Lifemap. Through a simple syntax, layers (points, lines, icons, heatmaps, etc.) depicting any type of data are superimposed on the Lifemap basemap, producing an interactive map to inspect biological datasets. The produced visualizations can easily be shared with others through notebooks or standalone HTML files, or exported as static images. C_LIO_LIWe illustrate the utility of pylifemap in the exploration of two contrasting datasets: the IUCN Red List of Threatened Species and the output of a large-scale environmental metagenomics experiment. C_LI Data/Code for peer reviewpylifemap is available at PyPI and easily installable with pip or uv. The development version can be found at https://lifemap-tol.github.io/pylifemap/ along with extensive documentation and examples.

evolutionary biology↗

bioRxiv2024

Gene flow can reveal ghost lineages

Ghost species, encompassing extinct, unknown, and unsampled taxa, vastly outnumber those typically included in phylogenetic analyses. This hidden diversity has been shown to influence the study of horizontal gene flow (e.g., introgression and horizontal gene transfer) by complicating the phylogenetic signals commonly used for their detection. In this work, we explore the potential of horizontal gene transfer (HGT) detection methods based on phylogenies (i.e., reconciliation methods) to detect and quantify ghost diversity . We briefly outline the theoretical framework underpinning our approach. Then, using simple simulations and empirical data from Cyanobacteria, we show that HGT signals, as interpreted by reconciliation methods, can reveal the presence and phylogenetic position of ghost clades, despite the absence of genomic data for these taxa. We anticipate possible limitations and difficulties in using HGT detection to explore ghost diversity and suggest promising approaches to address or circumvent them. Altogether, this proof of concept opens new lines of research for the future: a scarce fossil record and a large proportion of unknown lineages, especially in Archaea and Bacteria, does not equate to an absence of information for evolutionary studies.

evolutionary biology↗

bioRxiv2024

HGTs are not SPRs: In the presence of ghost lineages, series of Horizontal Gene Transfers do not result in series of Subtree Pruning and Regrafting

When a gene is horizontally transferred (HGT), under the "replacement" model where the transferred gene replaces its homolog in the recipient genome, the corresponding gene phylogeny departs from the species phylogeny by a Subtree Prune and Regraft (SPR) operation: the "recipient branch" is moved from its initial position to attach to the "donor branch". Based on this observation, various methods have used SPRs to infer HGTs. We examine this apparent equivalence in the light of ghost lineages, i.e. related species absent from the phylogeny because they are extinct, unknown or have not been sampled. In this case an SPR is not directly interpretable by an HGT from the donor branch, because HGTs can have ghost lineages as donors. A possible and frequent interpretation - that we call "induced HGT" - is that the transferred gene leaves the sampled phylogeny for a ghost lineage at the donor branch, and is transferred back from a ghost lineage at the recipient branch. We show by simulations that this interpretation is misleading in a significant number of cases. For instance if the studied phylogeny represents 1% of all the species susceptible to exchange genetic material with the 100 sampled species, and 11 transfers occurred, then SPRs do not correspond to induced HGTs in around 50% of the cases. This leaves the question of a coherent interpretation of SPR in the presence of ghosts open, and applies to a certain extent to other phylogenetic simulation or inference methods of HGT, like reconciliation, or phylogenetic networks.

bioinformatics↗

bioRxiv2023

PhylteR: efficient identification of outlier sequences in phylogenomic datasets

In phylogenomics, incongruences between gene trees, resulting from both artifactual and biological reasons, can decrease the signal-to-noise ratio and complicate species tree inference. The amount of data handled today in classical phylogenomic analyses precludes manual error detection and removal. However, a simple and efficient way to automate the identification of outliers from a collection of gene trees is still missing. Here, we present PhylteR, a method that allows a rapid and accurate detection of outlier sequences in phylogenomic datasets, i.e. species from individual gene trees that do not follow the general trend. PhylteR relies on DISTATIS, an extension of multidimensional scaling to 3 dimensions to compare multiple distance matrices at once. In PhylteR, these distance matrices extracted from individual gene phylogenies represent evolutionary distances between species according to each gene. On simulated datasets, we show that PhylteR identifies outliers with more sensitivity and precision than a comparable existing method. We also show that PhylteR is not sensitive to ILS-induced incongruences, which is a desirable feature. On a biological dataset of 14,463 genes for 53 species previously assembled for Carnivora phylogenomics, we show (i) that PhylteR identifies as outliers sequences that can be considered as such by other means, and (ii) that the removal of these sequences improves the concordance between the gene trees and the species tree. Thanks to the generation of numerous graphical outputs, PhylteR also allows for the rapid and easy visual characterisation of the dataset at hand, thus aiding in the precise identification of errors. PhylteR is distributed as an R package on CRAN and as containerized versions (docker and singularity).

evolutionary biology↗

bioRxiv2022

Endo-parasitoid lifestyle promotes endogenization and domestication of dsDNA viruses

The accidental endogenization of viral elements within eukaryotic genomes can occasionally provide significant evolutionary benefits, giving rise to their long-term retention, that is, to viral domestication. For instance, in some endoparasitoid wasps (whose immature stages develop inside their hosts), the membrane-fusion property of double-stranded DNA viruses has been repeatedly domesticated following ancestral endogenizations. The endogenized genes provide female wasps with a delivery tool to inject virulence factors that are essential to the developmental success of their offspring. Because all known cases of viral domestication involve endoparasitic wasps, we hypothesized that this lifestyle, relying on a close interaction between individuals, may have promoted the endogenization and domestication of viruses. By analyzing the composition of 124 Hymenoptera genomes, spread over the diversity of this clade and including free-living, ecto- and endoparasitoid species, we tested this hypothesis. Our analysis first revealed that double-stranded DNA viruses, in comparisons with other viral genomic structures (ssDNA, dsRNA, ssRNA), are more often endogenized and domesticated (that is, retained by selection) than expected from their estimated abundance in insect viral communities. Secondly, our analysis indicates that the rate at which dsDNA viruses are endogenized is higher in endoparasitoids than in ectoparasitoids or free-living hymenopterans, which also translates into more frequent events of domestication. Hence, these results are consistent with the hypothesis that the endoparasitoid lifestyle has facilitated the endogenization of dsDNA viruses, in turn increasing the opportunities of domestications that now play a central role in the biology of many endoparasitoid lineages.

genomics↗

bioRxiv2022

Ghost lineages invalidate or reverse several results on gene flow

Introgression, endosymbiosis and gene transfer, i.e. Horizontal Gene Flow (HGF), are primordial sources of innovation in all domains of life. Our knowledge on HGF relies on detection methods that exploit some of its signatures left on extant genomes. One of them is the effect of HGF on branch lengths of constructed phylogenies. This signature has been formalized in statistical tests for HGF detection, and used for example to detect massive adaptive gene flows in malaria vectors or to order evolutionary events involved in eukaryogenesis. However these studies rely on the assumption that ghost lineages (all unsampled extant and extinct taxa) have little influence. We demonstrate here with simulations and data re-analysis, that when considering the more realistic condition that unsampled taxa are legion compared to sampled ones, the conclusion of these studies become unfounded or even reversed. This illustrates the necessity to recognize the existence of ghosts in evolutionary studies.

evolutionary biology↗

bioRxiv2021

Ghost lineages deceive introgression tests and call for a new null hypothesis

Most species are extinct; those that are not are often unknown. Sequenced and sampled species are often a minority of known ones. Past evolutionary events involving horizontal gene flow, such as horizontal gene transfer, hybridization, introgression and admixture, are therefore likely to involve "ghosts", i.e. extinct, unknown or unsampled lineages. The existence of these ghost lineages is widely acknowledged, but their possible impact on the detection of gene flow and on the identification of the species involved is largely overlooked. It is generally considered as a possible source of error that, with reasonable approximation, can be ignored. We explore the possible influence of absent species on an evolutionary study by quantifying the effect of ghost lineages on introgression as detected by the popular D-statistic method. We show from simulated data that under certain frequently encountered conditions, the donors and recipients of horizontal gene flow can be wrongly identified if ghost lineages are not taken into account. In particular, having a distant outgroup, which is usually recommended, leads to an increase in the error probability and to false interpretations in most cases. We conclude that introgression from ghost lineages should be systematically considered as an alternative possible, even probable, scenario.

evolutionary biology↗

bioRxiv2018

Zombi: A simulator of species, genes and genomes that accounts for extinct lineages.

SummaryHere we present Zombi, a tool to simulate the evolution of species, genomes and sequences in silico, that considers for the first time the evolution of genomes in extinct lineages. It also incorporates various features that have not to date been combined in a single simulator, such as the possibility of generating species trees with a pre-defined variation of speciation and extinction rates through time, simulating explicitly intergenic sequences of variable length and outputting gene tree - species tree reconciliations.\n\nAvailability and implementationSource code and manual are freely available in https://github.com/AADavin/ZOMBI/\n\nContactaaredav@gmail.com

bioinformatics↗

bioRxiv2017

Overtraining Often Results In Topologically Incorrect Species Trees With Maximum Likelihood Methods

BackgroundOvertraining occurs when an optimization process is applied for too many steps, leading to a model describing noise in addition to the signal present in the data. This effect may affect typical approaches for species tree reconstruction that use maximum likelihood optimization procedures on a small sample of concatenated genes. In this context, overtraining may result in trees better describing the specific evolutionary history of the sampled genes rather than the sought evolutionary relationships among the species.\n\nResultsUsing a cross-validation-like approach on real and simulated datasets we showed that overtraining occurs in a significant fraction of cases, leading to species trees that are more distant from a gold-standard reference tree than a previously considered (and rejected) solution in the optimization process. However, we show that the shape of the likelihood curve is informative of the optimal stopping point. As expected, overtraining is aggravated in smaller gene samples and in datasets with increased levels of topological variation among gene trees, but occurs also in controlled, simulated scenarios where a common underlying topology is enforced.\n\nConclusionsOvertraining is frequent in species tree reconstruction and leads to a final tree that is worse in describing the evolutionary relationships of the species under study than an earlier (and rejected) solution encountered during the likelihood optimization process. This result should help develop specific methods for species tree reconstruction in the future, and may improve our understanding of the complexity of tree likelihood landscapes.

bioinformatics↗