Biology subjects

Eulenstein, O.

Publications and source records attributed to Eulenstein, O..

10 recordsLinked to original sources

bioRxiv2026

Quantifying Asymmetric Coevolutionary Dynamics using Normalized Phylogenetic Costs

AO_SCPLOWBSTRACTC_SCPLOWCoevolutionary studies aim to characterize associations, such as virus-host relationships, by using phylogenetic distances to quantify the topological concordance between the phylogenies of interacting taxa. However, phylogenetic distances cannot capture asymmetrical relationships that arise from differences in sampling, evolutionary rates, or characterizations between datasets. Furthermore, a lack of accurate normalization complicates the interpretation and validation of coevolutionary analyses. To address these limitations, we employed the Asymmetric Cluster Affinity and Cluster Support costs as a general framework to quantify coevolutionary patterns across multiple biological scales. We benchmarked the precision of these costs by reanalyzing a curated dataset documenting interspecies transmission frequencies across nineteen virus-host phylogenies. Our results corroborate prior findings showing that all virus families under study can cross species boundaries; however, the asymmetric costs provide a more granular representation, demonstrating that the frequency of such events varies significantly across families. We then applied the Asymmetric Cluster Support cost to quantify preferential gene segment pairings within the Bluetongue virus genome. This analysis revealed a close phylogenetic association between the outer capsid proteins VP2 and VP5, likely reflecting shared selective pressures due to their critical roles in cell entry and exit. In contrast, gene segments encoding nonstructural proteins exhibited discordant evolutionary histories relative to other segments. Finally, we demonstrated that the Asymmetric Cluster Support cost can detect coevolutionary dynamics in swine influenza A virus, identifying novel gene pairings indicative of major viral reassortment events. Overall, our approach demonstrates that normalized asymmetric phylogenetic costs accurately capture complex biological relationships and provide a robust framework for quantifying fine-scale coevolutionary dynamics in rapidly evolving pathogens.

bioinformatics↗

bioRxiv2026

PREMISE: A Quality-Aware Probabilistic Framework for Pathogen Resolution and Source Assignment in Viral mNGS

The circulation of Influenza A viruses (IAVs) in wildlife and livestock presents a significant public health threat due to their zoonotic potential and rapid genomic diversification. Accurate classification of viral subtypes and characterization of within-host diversity are crucial for risk assessment and vaccine development. Although metagenomic sequencing facilitates early detection, prevalent memory-efficient k-mer-based pipelines often discard critical linkage information. This loss of information can result in missed or imprecise pathogen identification, potentially delaying clinical and public health responses. We introduce PREMISE (Pathogen Resolution via Expectation Maximization In Sequencing Experiments), a probabilistic, alignment-based framework implemented in RUST for high-resolution viral genome identification. By integrating advanced string data structures for efficient alignment with a quality-score-aware Expectation-Maximization algorithm, PREMISE accurately identifies source strains, estimates relative abundances, and performs precise read assignments. This framework provides superior source estimation with statistical confidence, enabling the identification of mixed infections, recombination, and IAV-reassortment directly from raw data. Validated against simulated and empirical datasets, PREMISE outperforms state-of-the-art k-mer methods. Ultimately, this framework represents a significant advancement in viral identification, providing a foundation for novel approaches that can automatically flag reassorted viruses or recombination events in the future, thereby improving the detection of emerging pathogens with zoonotic potential. Availabilityhttps://github.com/sriram98v/premise under a MIT license. Contactsriramv@iastate.edu

bioinformatics↗

bioRxiv2026

A PLUM Job: Peptide modeLs for Understanding and engineering antiMicrobial therapeutics

Motivation: Antibiotic-resistant infections in humans and animals are rising, creating an urgent need for new antimicrobial strategies. This challenge extends from human health to food production, where foodborne pathogens cause substantial animal and human illness annually. Antimicrobial peptides (AMPs) are promising alternatives because of their broad activity and potentially lower resistance risk. However, rational AMP design remains challenging, particularly when generating targeted peptides with desired function and length for downstream experimental testing. Results: We introduce Peptide modeLs for Understanding and engineering antiMicrobial therapeutics (PLUM), a structured conditional variational autoencoder for controlled AMP generation. PLUM organizes the latent space into components associated with function, length, and residual sequence context, enabling de novo and prototype-guided generation across the 5-35 amino acid range. In de novo generation, PLUM achieved the highest classifier-confirmed yields for both AMP and non-AMP targets. PLUM-generated AMPs were also substantially distinct from the training set and exhibited broad internal sequence diversity. They further showed favorable predicted safety, stability, and potency profiles, including low toxicity, the lowest median instability index among the evaluated models, and a large number of candidates predicted to have high antibacterial potency. In prototype-guided generation, PLUM yielded more AMP-classified variants than HydrAMP while also supporting non-AMP-targeted generation from existing prototypes. Together, these results support PLUM as a scalable framework for function- and length-controlled AMP design. Availability: https://github.com/priyamayur/PLUM

bioinformatics↗

bioRxiv2025

SmartHisto: Bayesian Active Learning for Histology Images

Accurate and efficient characterization of biological images is crucial for advancing systems biology and medical research. Recent advancements in deep learning and image processing have enabled neural network models to rapidly accelerate image analysis by utilizing large expert-annotated datasets. However, in histopathology, the size of whole-slide images makes expert annotation expensive, limiting the acquisition of sufficiently large annotated datasets and posing a major challenge for developing automated, AI-driven image analysis pipelines. To address this limitation, we propose a novel active learning-based framework to train image segmentation models interactively. Our approach employs a Bayesian neural network to identify informative regions in unlabeled images rather than entire images, making expert labeling more cost-effective. We validate our framework on multiple benchmark datasets spanning different staining techniques and magnifications, demonstrating substantial reductions in annotation effort. Notably, our method achieves a mean IoU of 0.75, significantly outperforming competing approaches, which average 0.60. Author summaryHistopathology is fundamental to investigating tissue and immune responses, host-pathogen interactions, and disease mechanisms. However, histopathology is highly resource-intensive and requires specialized training, dramatically increasing the costs of annotating whole-slide images and, consequently, the expenses of large-scale studies involving numerous labs and specialists. We developed a computational tool to overcome these challenges, implementing a robust uncertainty-based sampling algorithm in conjunction with a next-generation Bayesian Convolutional Neural Network. This algorithm can be used for hypothesis testing and discovery by reducing the reliance on large, precisely annotated training datasets required in automated image analysis pipelines. The base model, when trained to identify lung tissue types using a small set of annotated images, outperforms state-of-the-art models and can efficiently annotate thousands of images much more quickly than a human. Models trained by the proposed algorithm will serve as a standardized approach for pathologists and disease researchers to train automated image segmentation pipelines for large-scale histopathology.

bioinformatics↗

bioRxiv2025

Phylo-rs: an extensible phylogenetic analysis library in Rust

We introduce Phylo-rs: a fast, extensible, general-purpose library for phylogenetic analysis and inference written in the Rust programming language. Phylo-rs leverages a combination of speed, memory-safety, and native WebAssembly support offered by Rust to provide a robust set of memory-efficient data structures and elementary phylogenetic algorithms. Phylo-rs is focused on efficient and convenient deployment of software aimed at large-scale phylogenetic analysis and inference. Phylo-rs is available under an open-source license on GitHub at https://github.com/sriram98v/phylo-rs, with documentation available at https://docs.rs/phylo/latest/phylo/.

bioinformatics↗

bioRxiv2022

PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny

The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires bias free subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than current approaches, and applied it to select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data. Our objective selection of 4 to 6 strains selected every two years from the 16 distinct genetic clades were sufficient to cover 80% of diversity circulating in US swine. We suggest that this method, through the objective selection of representatives in a phylogeny, provides criteria for rational multivalent vaccine design and for quantifying diversity. PARNAS is available at https://github.com/flu-crew/parnas.

evolutionary biology↗

bioRxiv2022

Discovering genomic islands in unannotated bacterialgenomes using sequence embedding

MotivationGenomic islands (GEIs) are clusters of genes in bacterial genomes that are typically acquired by horizontal gene transfer. Genomic islands play a crucial role in the evolution of bacteria by helping them adapt quickly to changing environments. Specifically of interest to human health, many GEIs contain pathogenicity and antimicrobial resistance genes. Detecting GEIs is therefore an important problem in biomedical and environmental research. There have been many previous studies for computationally identifying GEIs, but most of the studies rely either on detecting differences between closely related genomes, or on annotated nucleotide sequences with predictions based on a fixed set of known features. ResultsHere we present TreasureIsland, which uses a new unsupervised representation of DNA sequences to predict GEIs. We developed a high precision boundary detection method featuring an incremental fine-tuning of GEI borders, and we evaluated the accuracy of this framework using a new comprehensive reference dataset, Benbow. We show that TreasureIsland performs competitively when compared with other GEI predictors, enabling the identification of genomic islands in unannotated and taxonomically isolated bacterial genomes. AvailabilityThe source code and the datasets used in this study are available at: https://github.com/priyamayur/GenomicIslandPrediction Contactidoerg@iastate.edu Supplementary informationSupplementary Material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

RF-Net 2: Fast Inference of Virus Reassortment and Hybridization Networks

MotivationA phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g., hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens. ResultsHere we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. Additionally, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical influenza A virus evolutionary analyses. AvailabilityRF-Net 2 is available at https://github.com/flu-crew/rf-net-2.

evolutionary biology↗

bioRxiv2019

Robinson-Foulds Reticulation Networks

Phylogenetic (hybridization) networks allow investigation of evolutionary species histories that involve complex phylogenetic events other than speciation, such as reassortment in virus evolution or introgressive hybridization in invertebrates and mammals. Reticulation networks can be inferred by solving the reticulation network problem, typically known as the hybridization network problem. Given a collection of phylogenetic input trees, this problem seeks a minimum reticulation network with the smallest number of reticulation vertices into which the input trees can be embedded exactly. Unfortunately, this problem is limited in practice, since minimum reticulation networks can be easily obfuscated by even small topological errors that typically occur in input trees inferred from biological data. We adapt the reticulation network problem to address erroneous input trees using the classic Robinson-Foulds distance. The RF embedding cost allows trees to be embedded into reticulation networks inexactly, but up to a measurable error. The adapted problem, called the Robinson-Foulds reticulation network (RF-Network) problem is, as we show and like many other problems applied in molecular biology, NP-hard. To address this, we employ local search strategies that have been successfully applied in other NP-hard phylogenetic problems. Our local search method benefits from recent theoretical advancements in this area. Further, we introduce inpractice effective algorithms for the computational challenges involved in our local search approach. Using simulations we experimentally validate the ability of our method, RF-Net, to reconstruct correct phylogenetic networks in the presence of error in input data. Finally, we demonstrate how RF-networks can help identify reassortment in influenza A viruses, and provide insight into the evolutionary history of these viruses. RF-Net was able to estimate a large and credible reassortment network with 164 taxa.

evolutionary biology↗

bioRxiv2017

Ancestral Reconstruction of Gene Blocks using an Event-Based Method

Complexity is a fundamental attribute of life. Complex systems are made of parts that together perform functions that a single component, or subsets containing individual components, cannot. Examples of complex molecular systems include protein structures such as the F1Fo-ATPase, the ribosome, or the flagellar motor: each one of these structures requires most or all of its components to function properly. Given the ubiquity of complex systems in the biosphere, understanding the evolution of complexity is central to biology. At the molecular level, operons are a classic example of a complex system. An operons genes are co-transcribed under the control of a single promoter to a polycistronic mRNA molecule, and the operons gene products often form molecular complexes or metabolic pathways. With the large number of complete bacterial genomes available, we now have the opportunity to explore the evolution of these complex entities, by identifying possible intermediate states of operons. In this work, we developed a maximum parsimony algorithm to reconstruct ancestral operon states, and show a simple vertical evolution model of how operons may evolve from the individual component genes. We describe several ancestral states that are plausible functional intermediate forms leading to the full operon. We also offer Reconstruction of Ancestral Gene blocks Using Events or ROAGUE as a software tool for those interested in exploring gene block and operon evolution.\n\nThe software accompanying this paper is available under GPLv3 license in: https://github.com/nguyenngochuy91/Ancestral-Blocks-Reconstruction.\n\nAll figures in this paper are available in enlarged downloadable form from:https://github.com/nguyenngochuy91/Ancestral-Blocks-Reconstruction/tree/master/images

evolutionary biology↗