Biology subjects

Flouri, T.

Publications and source records attributed to Flouri, T..

7 recordsLinked to original sources

bioRxiv2025

The impact of incomplete taxon sampling on inference of gene flow by Bayesian and summary methods using genomic sequence data

Interspecific gene flow is commonly inferred using genomic data under the multispecies coalescent (MSC) model. The rate of gene flow is measured by the expected proportion of immigrants in the recipient population at the time of hybridization/introgression. Incomplete taxon sampling can impact inference of gene flow in multiple ways. First unsampled ghost lineages that are sources of introgression may mislead inference of gene flow in analysis of genomic data from modern species. Second incomplete taxon sampling causes merges of branches on the species phylogeny, which represent populations of different sizes, and complicates the definition and estimation of the introgression probability. We use mathematical analysis and computer simulation to examine the impact of incomplete taxon sampling on inference of gene flow and estimation of its rate using genomic data. We introduce a Bayesian testing approach to select models of gene flow for a species triplet (such as inflow, outflow, and ghost introgression), using the Savage-Dickey density ratio to calculate Bayes factors. We show that the approach has excellent power and specificity. We find that genomic data allow reliable estimation of the proportion of immigrants (rather than the number of immigrants), even when the assumed demographic model is incorrect due to incomplete taxon sampling. When population size differs among species, assuming the same size may lead to seriously biased estimates of the rate of gene flow. The f -branch approach is found to be effective in reducing the number of significant gene-flow events from triplet analyses, providing useful hypotheses for rigorous testing, but often to produce underestimates of the rate of gene flow. Our results highlight the need for improving summary methods to accommodate different population sizes and to infer gene flow between sister lineages.

genomics↗

bioRxiv2024

Inference of continuous gene flow between species under misspecified models

Gene flow between species is increasingly recognized as an important evolutionary process with potential adaptive consequences. Recent methodological advances make it possible to infer different modes of gene flow from genome-scale data, including pulse introgression at a specific time and continuous gene flow over an extended time period. However, it remains challenging to infer the history of species divergence and between-species gene flow from genomic sequence data. As a result, models used in real data analysis may often be misspecified, potentially leading to incorrect biological interpretations. Here, we characterize biases in parameter estimation under continuous migration models using a combination of asymptotic analysis and posterior inference from simulated datasets. When sequence data are generated under a pulse introgression model, isolation-with-initial-migration models assuming no recent gene flow are able to better recover gene flow with less bias than models that assume recent gene flow. When gene flow is assigned to an incorrect branch in the phylogeny, there may be large biases associated with the migration rate and species divergence times. When the direction of gene flow is incorrectly assumed, we may still detect gene flow if it is recent and between non-sister species but not when it is ancestral and between sister species. Overall, the impact of model misspecification is local in the species phylogeny. The pulse introgression model appears to be more robust to model misspecification and is preferable in real data analysis over the continuous migration model unless there is substantive evidence for continuous gene flow.

evolutionary biology↗

bioRxiv2023

Hierarchical heuristic species delimitation under the multispecies coalescent model with migration

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program O_SCPLOWBPPC_SCPLOW, has been found to be prone to over-splitting. Alternatively heuristic criteria based on population parameters under the MSC model (such as population/species divergence times, population sizes, and migration rates) estimated from genomic sequence data may be used to delimit species. Here we extend the approach of species delimitation using the genealogical divergence index (gdi) to develop hierarchical merge and split algorithms for heuristic species delimitation, and implement them in a python pipeline called O_SCPLOWHHSDC_SCPLOW. Applied to data simulated under a model of isolation by distance, the approach was able to recover the correct species delimitation, whereas model comparison by O_SCPLOWBPPC_SCPLOW failed. Analyses of empirical datasets suggest that the procedure may be less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the procedure, as well as the challenges of species delimitation based on heuristic criteria.

bioinformatics↗

bioRxiv2023

Inferring the direction of introgression using genomic sequence data

Genomic data are informative about the history of species divergence and interspecific gene flow, including the direction, timing, and strength of gene flow. However, gene flow in opposite directions generates similar patterns in multilocus sequence data, such as reduced sequence divergence between the hybridizing species. As a result, inference of the direction of gene flow is challenging. Here we investigate the information about the direction of gene flow present in genomic sequence data using likelihood-based methods under the multispecies-coalescent-with-introgression (MSci) model. We analyze the case of two species, and use simulation to examine cases with three or four species. We find that it is easier to infer gene flow from a small population to a large one than in the opposite direction, and easier to infer inflow (gene flow from outgroup species to an ingroup species) than outflow (gene flow from an ingroup species to an outgroup species). It is also easier to infer gene flow if there is a longer time of separate evolution between the initial divergence and subsequent introgression. When introgression is assumed to occur in the wrong direction, the time of introgression tends to be correctly estimated and the Bayesian test of gene flow is often significant, while estimates of introgression probability can be even greater than the true probability. We analyze genomic sequences from Heliconius butterflies to demonstrate that typical genomic datasets are informative about the direction of interspecific gene flow, as well as its timing and strength.

evolutionary biology↗

bioRxiv2021

SPART, a versatile and standardized data exchange format for species partition information

A wide range of data types can be used to delimit species and various computer-based tools dedicated to this task are now available. Although these formalized approaches have significantly contributed to increase the objectivity of SD under different assumptions, they are not routinely used by alpha-taxonomists. One obvious shortcoming is the lack of interoperability among the various independently developed SD programs. Given the frequent incongruences between species partitions inferred by different SD approaches, researchers applying these methods often seek to compare these alternative species partitions to evaluate the robustness of the species boundaries. This procedure is excessively time consuming at present, and the lack of a standard format for species partitions is a major obstacle. Here we propose a standardized format, SPART, to enable compatibility between different SD tools exporting or importing partitions. This format reports the partitions and describes, for each of them, the assignment of individuals to the "inferred species". The syntax also allows to optionally report support values, as well as original trees and the full command lines used in the respective SD analyses. Two variants of this format are proposed, overall using the same terminology but presenting the data either optimized for human readability (matricial SPART) or in a format in which each partition forms a separate block (SPART.XML). ABGD, DELINEATE, GMYC, PTP and TR2 have already been adapted to output SPART files and a new version of LIMES has been developed to import, export, merge and split them.

bioinformatics↗

bioRxiv2019

ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models

ModelTest-NG is a re-implementation from scratch of jModelTest and ProtTest, two popular tools for selecting the best-fit nucleotide and amino acid substitution models, respectively. ModelTest-NG is one to two orders of magnitude faster than jModelTest and ProtTest but equally accurate, and introduces several new features, such as ascertainment bias correction, mixture and FreeRate models, or the automatic processing of partitioned datasets. ModelTest-NG is available under a GNU GPL3 license at https://github.com/ddarriba/modeltest.

bioinformatics↗

bioRxiv2018

RAxML-NG: A fast, scalable, and user-friendly tool for maximum likelihood phylogenetic inference

MotivationPhylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture, and medicine. Finding the optimal tree under the popular maximum like-lihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.\n\nResultsWe present RAxML-NG, a from scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML- NG offers improved accuracy, flexibility, speed, scalability, and usability compared to RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and a the recently introduced transfer bootstrap support metric.\n\nAvailabilityThe code is available under GNU GPL at https://github.com/amkozlov/raxml-ng.RAxML-NG web service (maintained by Vital- IT) is available at https://raxml-ng.vital-it.ch/.\n\nContactalexey.kozlov@h-its.org

bioinformatics↗