Biology subjects

Rudar, J.

Publications and source records attributed to Rudar, J..

6 recordsLinked to original sources

bioRxiv2026

Influenza A virus H5Nx genotypes assigned by genetic similarity are incongruent with global phylogenies

Influenza A virus H5N1 and other H5 subtypes (H5Nx) have broad host ranges and pose a pandemic risk. Since early 2022, H5 clade 2.3.4.4b has predominated globally, causing unprecedented mortality in wild birds, poultry, and mammals, with spillovers to humans. Accurate viral classification is essential for tracking H5Nx variant emergence and spread, but reassortment among genome segments with distinct evolutionary histories complicates genotyping. H5Nx genotype assignment tools, GenoFlu in North America and Genin2 in Europe, assign segments to clusters by genetic similarity to reference sequences and define genotypes as constellations of segment clusters. However, genetic similarity does not necessarily imply recent shared ancestry because mutations may occur multiple times across the tree and branches may exhibit variable evolutionary rates. Furthermore, similarity is defined relative to references that may inadequately represent diversity. We evaluated the congruence of genetic similarity-based H5Nx clusters and genotypes with phylogenies of global sequences across eight segments. Publicly available H5Nx nucleotide sequences from 41,366 samples were assigned genotypes using GenoFlu and Genin2, and maximum likelihood phylogenies were inferred for each segment. Phylogenetic clusters were identified using TreeCluster for a range of patristic distance thresholds and CD-HIT clusters were identified for a range of genetic similarity thresholds. Clustering methods were evaluated based on phylogenetic purity, monophyly, and diversity. Reassortment within predominant GenoFlu and Genin2 genotypes was evaluated based on their phylogenetic cluster spans. Expectedly, GenoFlu was largely unable to assign genomes from outside of North America, South America, and Antarctica from 2022 to 2025, whereas Genin2 assignments struggled outside of Europe, Africa, and Western Asia. Only 14.2% of GenoFlu clusters were monophyletic, while Genin2 clusters were more variable across segments (overall 49.3% monophyletic). Despite CD-HIT yielding genetic distance clusters of similar size to TreeCluster, they were less monophyletic (44%) and encompassed greater phylogenetic diversity. We identified several genotypes, such as GenoFlu B3.13, D1.1, and D1.3, with internal segments that had diversified into contiguous clusters. Similarly, the wide phylogenetic span of GenoFlu genotypes B3.5 and A3, and Genin2 genotypes EA-2020-C, EA-2021-AB, and EA-2022-CH suggest they may erroneously combine multiple reassortants. Genetic similarity-based H5Nx genotyping tools are rapid, stable, and sufficient for broad categorization, but may group viruses with distinct evolutionary histories and fail to assign divergent viruses, risking biased surveillance, modeling, and risk assessment.

bioinformatics↗

bioRxiv2026

Interpreting the WaveSeekerNet model to reveal the evolution and biology of influenza A virus

BackgroundInfluenza A virus (IAV) is a major public health burden, causing seasonal epidemics and occasional pandemics. Its transmission from avian species to mammals and subsequent spread requires adaptive changes in the viral genome. Understanding these molecular adaptations is essential for pandemic preparedness, and machine learning offers a powerful approach to uncover the evolution and biology of IAV. ResultsThis study established a well-calibrated WaveSeekerNet model that accurately predicted the host source across all 8 IAV segments (macro F1-score: 0.9728), significantly improving the reliability of predicted probabilities with calibration errors approaching zero. Model interpretation revealed that avian-adapted IAVs consistently activated G/C content, whereas mammalian-adapted IAVs generally activated A/T content. This distinction was confirmed by codon-level analysis, in which G/C-rich codons were rewarded for the avian hosts and A/T-rich codons for the mammalian hosts. In the feature space learned by WaveSeekerNet, we defined host-adaptive distance to quantify species barriers and proposed it as a risk-assessment metric. We hypothesized the Mammalian Adaptation Zone (MAZ), a zone where the virus is expected to adjust its host-adaptive distance to reach, thereby helping it establish persistent mammalian lineages. The analysis also revealed the Hard Distance of avian-origin viruses (e.g., H5Nx, H9N2), indicating they have not yet established persistent mammalian lineages. Finally, analysis of human H7N9 (2013, China) and non-human mammalian H5Nx (North America) viruses showed that WaveSeekerNet accurately identified key mammalian-adaptive mutations, including PB2-E627K and PB2-D701N. ConclusionsWaveSeekerNet elucidated IAV host-adaptation mechanisms in silico, providing insights into the underlying mechanisms of host adaptation and informing improved surveillance and intervention strategies.

genomics↗

bioRxiv2025

WaveSeekerNet: Accurate Prediction of Influenza A Virus Subtypes and Host Source Using Attention-Based Deep Learning

BackgroundInfluenza A virus (IAV) poses a significant threat to animal health globally, with its ability to overcome species barriers and cause pandemics. Rapid and accurate IAV subtypes and host source prediction is crucial for effective surveillance and pandemic preparedness. Deep learning has emerged as a powerful tool for analyzing viral genomic sequences, offering new ways to uncover hidden patterns associated with viral characteristics and host adaptation. FindingsWe introduce WaveSeekerNet, a novel deep learning model for accurate and rapid prediction of IAV subtypes and host source. The model leverages attention-based mechanisms and efficient token mixing schemes, including the Fourier Transform and the Wavelet Transform, to capture intricate patterns within viral RNA and protein sequences. Extensive experiments on diverse datasets demonstrate WaveSeekerNets superior performance to existing models that use the traditional self-attention mechanism. Notably, WaveSeekerNet rivals VADR (Viral Annotation DefineR) in subtype prediction using the high-quality RNA sequences, achieving the maximum score of 1.0 on metrics including the Balanced Accuracy, F1-score (Macro Average), and Matthews Correlation Coefficient (MCC). Our approach to subtype and host source prediction also exceeds the pre-trained ESM-2 (Evolutionary Scale Modeling) models with respect to generalization performance and computational cost. Furthermore, WaveSeekerNet exhibits remarkable accuracy in distinguishing between human, avian, and other mammalian hosts. The ability of WaveSeekerNet to flag potential cross-species transmission events underscores its significant value for real-time surveillance and proactive pandemic preparedness efforts. ConclusionsWaveSeekerNets superior performance, efficiency, and ability to flag potential cross-species transmission events highlight its potential for real-time surveillance and pandemic preparedness. This model represents a significant advancement in applying deep learning for IAV classification and holds promise for future epidemiological, veterinary studies, and public health interventions.

bioinformatics↗

bioRxiv2022

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta-Diversity in Medically Relevant 16S Amplicon Sequencing Data

Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool to investigate if learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohns Disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log-ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohns Disease patients and healthy controls. Further investigation of our models elucidated the global impact ASVs had on the location of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing datasets since they are better able to learn about the underlying structure of the dataset. Author SummaryThere is an ever-increasing level of interest in accurately modeling and understanding the role that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impact of taxa in these ordinations and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer.

bioinformatics↗

bioRxiv2022

Highly divergent white-tailed deer SARS-CoV-2 with potential deer-to-human transmission

Wildlife reservoirs of SARS-CoV-2 may enable viral adaptation and spillback from animals to humans. In North America, there is evidence of unsustained spillover of SARS-CoV-2 from humans to white-tailed deer (Odocoileus virginianus), but no evidence of transmission from deer to humans. Through a biosurveillance program in Ontario, Canada we identified a new and highly divergent lineage of SARS-CoV-2 in white-tailed deer. This lineage is the most divergent SARS-CoV-2 lineage identified to date, with 76 consensus mutations (including 37 previously associated with non-human animal hosts) and signatures of considerable evolution and transmission within wildlife. Phylogenetic analysis also revealed an epidemiologically linked human case. Together, our findings represent the first clear evidence of sustained evolution of SARS-CoV-2 in white-tailed deer and of deer-to-human transmission.

microbiology↗

bioRxiv2019

COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems

DNA-based biodiversity analysis has gained major attention due to the use of high throughput sequencing technology in approaches such as mixed community or environmental DNA metabarcoding. Many cytochrome c oxidase subunit I (COI) primer sets are now available for such work. The purpose of this study is to look at how COI primer choice affects the recovery of arthropod richness, beta diversity, and recovery of site indicator taxa in benthos kick-net samples typically used in freshwater biomonitoring. We examine 6 commonly used COI primer sets, on samples collected from 6 freshwater sites. Richness is sensitive to primer choice and the combined use of additional multiple COI amplicons recovers higher richness. Thus, to recover maximum richness, multiple primer sets should be used with COI metabarcoding. Samples consistently cluster by site regardless of amplicon choice or PCR replicate. Thus, for broadscale community analyses, overall beta diversity patterns are robust to COI marker choice. Additionally, the recovery of traditional freshwater bioindicator assemblages such as Ephemeroptera, Trichoptera, Plectoptera, and Diptera may not fully capture the diversity of broadscale arthropod site indicators that can be recovered from COI metabarcoding. Based on these results, studies that use different COI amplicons may not be directly comparable. This work will help future biodiversity and biomonitoring studies develop not just standardized, but optimized workflows that maximize taxon-detection or order taxa along gradients.

genomics↗