Biology subjects

Palacios, J. A.

Publications and source records attributed to Palacios, J. A..

6 recordsLinked to original sources

bioRxiv2026

Forecasting viral evolution from phylogenetic trees

Viral mutation forecasting plays a key role in pandemic preparedness by enabling researchers to anticipate novel variants and design proactive interventions. Evolutionary histories, represented as phylogenetic trees, offer key insights into the emergence of past and present strains, yet their role in predicting future sequence changes remains largely unexplored. We introduce antiGen, a machine learning model that forecasts the evolutionary future of viruses by learning from their evolutionary past. antiGen achieves state-of-the-art performance for predicting mutations to the SARS-CoV-2 spike protein, anticipating never-before-seen mutations and mutations that emerge years after the model's training window. antiGen-forecasted spike mutations also retain pseudoviral infectivity in vitro. Moreover, antiGen demonstrates leading predictive performance on surface proteins of influenza virus, respiratory syncytial virus, and dengue virus despite far less available sequencing data. Viral evolution models that explicitly learn from phylogenetic structure offer a valuable resource for applications ranging from epidemiological modeling to therapeutic development.

evolutionary biology↗

bioRxiv2026

Persistent, Multi-Species Outbreaks and Long-Range Transmissions of a Measles-Like Virus

Canine distemper--a measles-like disease with high mortality and presently without a cure--poses a major threat to wild and domestic carnivores globally. Domestic dogs are generally considered to be the main reservoir and long-range transmitter of the disease, but the role of wildlife is likely underestimated and the long-term persistence of CDV in wildlife has never been assessed. We sequenced canine distemper virus (CDV) full and partial genomes that were sampled over a decade (2012-2021) from Arctic foxes and other canids in Alaska and Yellowstone, and compiled a dataset of all published CDV genomes sampled across 32 species globally. We show the first ever evidence of persistence of CDV in wildlife (for almost a decade) with explosive transmission dynamics crossing host species barriers. Strains sampled from the Arctic for the first time connect North America to Eurasia, and are distinct from the Yellowstone strain and other known North American lineages. This suggests that separate wildlife outbreaks occur concurrently in North America, with possible introductions from Eurasia. The long-term persistence, long-range movement and explosive spread of this devastating panzootic virus that we document within wildlife is alarming and highlights the need for increased monitoring efforts to better protect wildlife populations globally.

genomics↗

bioRxiv2025

Efficient Bayesian Phylogenetics under the Infinite Sites Model

Bayesian phylogenetic inference from molecular sequences can provide key insights into the evolutionary history of populations. Existing tools, however, often scale poorly with sample size. We present inPhynite, a highly-efficient Bayesian phylogenetics algorithm for genomic datasets compatible with the infinite sites mutation model. A key advantage of this model is that likelihood calculation, which typically incurs a substantial computational cost, becomes trivial. We show that under the infinite sites assumption, it is possible to sample a coarse space of mutations and coalescences from which we may recover complete phylogenetic trees. We design an efficient Markov chain for this space together with effective population size trajectories, modeled as piecewise constant functions. Based on real and synthetic data, our method significantly outperforms competing methods, offering a speedup of over 225 times in statistical efficiency on large datasets without incurring any loss in accuracy. Finally, we demonstrate how inPhynite can help us understand the evolutionary history and past effective population sizes of human populations based on mitochondrial DNA. SummaryInferring the phylogenetic tree and evolutionary parameters from a sample of molecular sequences plays a key role in the study of how populations evolve over time. Existing inference algorithms face major computational challenges due to the large size of the phylogenetic tree space and high cost of phylogenetic likelihood evaluation. We show that under the infinite sites model of mutations, it is possible to overcome these limitations by instead conducting inference over an ordered sequence of genotypes that encodes the essential information in the tree. This approach achieves superior statistical efficiency compared to existing methods under a range of evolutionary conditions.

evolutionary biology↗

bioRxiv2022

Bayesian Inference of Dependent Population Dynamics in Coalescent Models

The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present a novel probabilistic model that relies on jointly distributed Markov random fields. We use this model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.

bioinformatics↗

bioRxiv2019

A Metric Space of Ranked Tree Shapes and Ranked Genealogies

Genealogical tree modeling is essential for estimating evolutionary parameters in population genetics and phylogenetics. Recent mathematical results concerning ranked genealogies without leaf labels enable new opportunities in the analysis of evolutionary trees. In particular, comparisons between ranked genealogies facilitate the study of evolutionary processes for organisms sampled in multiple time periods. We propose a metric space on ranked genealogies for lineages sampled from both isochronous and time-stamped heterochronous sampling. Our new tree metrics make it possible to conduct statistical analyses of ranked tree shapes and timed ranked tree shapes, or ranked genealogies. Such analyses allow us to assess differences in tree distributions, quantify estimation uncertainty, and summarize tree distributions. We show the utility of our metrics via simulations and an application in infectious diseases.

evolutionary biology↗

bioRxiv2019

Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees

The large state space of gene genealogies is a major hurdle for inference methods based on Kingmans coalescent. Here, we present a new Bayesian approach for inferring past population sizes which relies on a lower resolution coalescent process we refer to as \"Tajimas coalescent\". Tajimas coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajimas Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated data and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingmans coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.

genetics↗