Biology subjects

Berry, V.

Publications and source records attributed to Berry, V..

5 recordsLinked to original sources

bioRxiv2026

A hybrid approach combining a phylogenetic method and Approximate Bayesian Computation Random Forest for phylogenetic network inference: application to the rice domestication process in Asia

Asian rice is one of the best documented crops in terms of genetic diversity. The domestication process, that probably started 9000 years ago in China, remains difficult to infer since the main vertical signal is blurred by horizontal signals related to gene flow among cultivars and wild relatives. Consequently, a large number of hypotheses on the domestication process of rice have been published. Besides, most of the methods used to infer these scenarios do not model all the known biological phenomena at stake. Here, we present a methodological study based on a rich stochastic model, that incorporates introgression events, incomplete lineage sorting, and mutations that happen over time. The global evolutionary scenario is represented by a phylogenetic network. Furthermore, each locus scenario is modeled according to a locus tree through the Multispecies Network Coalescent. More importantly, for inferring the phylogenetic network, we propose a new hybrid approach combining a phylogenetic network method and a machine learning technique. In particular, our hybrid approach, named SO_SCPLOWNARFC_SCPLOW, benefits from advantages of a mathematical phylogenetic method, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW, and from the potential of a powerful machine learning classifier, i.e. Approximate Bayesian Computation Random Forest (ABC-RF). These two methods are complementary since SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW reconstructs network accurately, whereas ABC-RF is able to handle a large amount of data. The originality is twofold. First, prior distributions required for ABC-RF are calibrated thanks to SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOWs estimates. Secondly, ABC-RF relies on summary statistics inspired by phylogenetic network literature. We show, on simulated data, that the SO_SCPLOWNARFC_SCPLOW hybrid approach enjoys very good performances. On rice real data, it infers a scenario with a unique domestication (that of Japonica), followed by three reticulation events involving early Japonica. It highlights two introgression events at the origin of Indica and cAus, and one admixture event responsible for the emergence of cBas. Author summaryToday, in genomics, there is a real need for methods able to infer phylogenetic networks. A phylogenetic network is a directed graph representing events like hybridization, introgression, and horizontal gene transfer. Understanding these complex biological phenomena, essential for crop adaptation, can help breeders when facing challenges like climate change and population growth. Genome-wide diversity analysis thus requires network methods scaling for large data volumes and incorporating fundamental biological phenomena. In this context, we present a new hybrid approach, SO_SCPLOWNARFC_SCPLOW, that benefits from the potential of a powerful machine learning classifier, Approximate Bayesian Computation Random Forest, and from advantages of a mathematical phylogenetic method, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW. Consequently, SO_SCPLOWNARFC_SCPLOW is able to handle large data-sets thanks to machine learning and is also based on a deep mathematical theory. On simulated data, our hybrid approach performs very well. When applied to real rice genomic data, it supports a scenario with a single domestication event, that of Japonica. The analysis further highlights the role of early Japonica in the origin of both Indica and circumAus. Finally, it identifies an ancient admixture event, involving circumAus in the emergence of circumBasmati. Together, these findings confirm the importance of early rice history along the Himalayan region.

evolutionary biology↗

bioRxiv2024

Multiomics analysis reveals B. MO1 as a distinct Babesia species and provides insights into its evolution and virulence.

Babesiosis, caused by protozoan parasites of the genus Babesia, is an emerging tick-borne disease of significance for both human and animal health. Babesia parasites infect erythrocytes of vertebrate hosts where they develop and multiply rapidly to cause the pathological symptoms associated with the disease. The identification of various Babesia species underscores the ongoing risk of new zoonotic pathogens capable of infecting humans, a concern amplified by anthropogenic activities and environmental shifts impacting the distribution and transmission dynamics of parasites, their vectors, and reservoir hosts. One such species, Babesia MO1, previously implicated in severe cases of human babesiosis in the midwestern United States, was initially considered closely related to B. divergens, the predominant agent of human babesiosis in Europe. Yet, uncertainties persist regarding whether these pathogens represent distinct variants of the same species or are entirely separate species. We show that although both B. MO1 and B. divergens share similar genome sizes, comprising three nuclear chromosomes, one linear mitochondrial chromosome, and one circular apicoplast chromosome, major differences exist in terms of genomic sequence divergence, gene functions, transcription profiles, replication rates and susceptibility to antiparasitic drugs. Furthermore, both pathogens have evolved distinct classes of multigene families, crucial for their pathogenicity and adaptation to specific mammalian hosts. Leveraging genomic information for B. MO1, B. divergens, and other members of the Babesiidae family within Apicomplexa provides valuable insights into the evolution, diversity, and virulence of these parasites. This knowledge serves as a critical tool in preemptively addressing the emergence and rapid transmission of more virulent strains.

microbiology↗

bioRxiv2020

On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo

For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC). Phylogenetic networks are an extension of phylogenetic trees that can contain reticulate nodes, which allow to model complex biological events such as horizontal gene transfer, hybridization, introgression and recombination. We present a novel way to compute the likelihood of biallelic markers sampled along genomes whose evolution involved such events. This likelihood computation is at the heart of a Bayesian network inference method called SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW, as it extends the SO_SCPLOWNAPPC_SCPLOW method [1] inferring evolutionary trees under the multispecies coalescent model, to networks. SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW is available as a package of the well-known beast 2 software. Recently, the MCMCBiMarkers method [2] also extended SO_SCPLOWNAPPC_SCPLOW to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using extensive simulations, we compare performances of SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW and MCMCBiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW is more accurate than MCMCBiMarkers on more complex network scenarios. Also, on complex networks, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW is found to be extremely faster than MCMCBiMarkers in terms of time required for the likelihood computation. We finally illustrate SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW performances on a rice data set. SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW infers a scenario that is compatible with simpler schemes proposed so far and provides additional understanding of rice evolution. Author summaryNowadays, to make the best use of the vast amount of genomic data at our disposal, there is a real need for methods able to model complex biological mechanisms such as hybridization and introgression. Understanding such mechanisms can help geneticists to elaborate strategies in crop improvement that may help reducing poverty and dealing with climate change. However, reconstructing such evolution scenarios is challenging. Indeed, the inference of phylogenetic networks, which explicitly model reticulation events such as hybridization and introgression, requires high computational resources. Then, on large data sets, biologists generally deduce reticulation events indirectly using species tree inference tools. In this context, we present a new Bayesian method, called SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW, dedicated to phylogenetic network inference. Our method is competitive in terms of execution speed with respect to its competitors. This speed gain enables us to consider more complex evolution scenarios during Bayesian analyses. When applied to rice genomic data, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW suggested a new evolution scenario, compatible with the existing ones: it posits cAus as the result of an early combination between the Indica and Japonica lineages, followed by a later combination between the cAus and Japonica lineages to derive cBasmati. This accounts for the well-documented wide hybrid compatibility of cAus.

evolutionary biology↗

bioRxiv2018

Three new genome assemblies support a rapid radiation in Musa acuminata (wild banana)

Edible bananas result from interspecific hybridization between Musa acuminata and Musa balbisiana, as well as among subspecies in M. acuminata. Four particular M. acuminata subspecies have been proposed as the main contributors of edible bananas, all of which radiated in a short period of time in southeastern Asia. Clarifying the evolution of these lineages at a whole-genome scale is therefore an important step toward understanding the domestication and diversification of this crop. This study reports the de novo genome assembly and gene annotation of a representative genotype from three different subspecies of M. acuminata. These data are combined with the previously published genome of the fourth subspecies to investigate phylogenetic relationships and genome evolution. Analyses of shared and unique gene families reveal that the four subspecies are quite homogenous, with a core genome representing at least 50% of all genes and very few M. acuminata species-specific gene families. Multiple alignments indicate high sequence identity between homologous single copy-genes, supporting the close relationships of these lineages. Interestingly, phylogenomic analyses demonstrate high levels of gene tree discordance, due to both incomplete lineage sorting and introgression. This pattern suggests rapid radiation within Musa acuminata subspecies that occurred after the divergence with M. balbisiana. Introgression between M. a. ssp. malaccensis and M. a. ssp. burmannica was detected across a substantial portion of the genome, though multiple approaches to resolve the subspecies tree converged on the same topology. To support future evolutionary and functional analyses, we introduce the PanMusa database, which enables researchers to exploration of individual gene families and trees.

evolutionary biology↗

bioRxiv2017

A geography aware reconciliation method to investigate host/parasite cospeciation

Cospeciation studies aim at investigating whether hosts and symbionts speciate simultaneously or whether the associations diversify through host shifts. This problem is often tackled through reconciliation analyses that map the symbiont phylogeny onto the host phylogeny by mixing different types of diversification events. These reconciliations can be difficult to interpret and not always biologically realistic. Researchers have underlined that the biogeographic histories of both hosts and symbionts influence the probability of cospeciation and host switches, but up to now no reconciliation software integrates geographic data. We present a new functionality in the Mowgli software that bridges this gap. The user can provide geographic information on both the host and symbiont extant and ancestral taxa. Constraints in the reconciliation algorithm have been implemented to generate biologically realistic codiversification scenarios. We apply our method to the fig/fig wasp association and infer diversification scenarios that differ from reconciliations ignoring geographic information. In addition, we updated the reconciliation viewer SylvX in order to visualize ancestral character states on the phylogenetic trees and highlight zones that are geographically inconsistent in reconciliations computed without geographic constraints. We suggest that the comparison of reconciliations obtained with and without constraints can help solving ambiguities in the biogeographic histories of the partners. With the development of robust methods in historical biogeography and the advent of next-generation sequencing that leads to better-resolved trees, a geography aware reconciliation method represents a substantial advance that is likely to be useful to researchers studying the evolution of biotic interactions and biogeography.

evolutionary biology↗