Biology subjects

Ocana, K.

Publications and source records attributed to Ocana, K..

5 recordsLinked to original sources

bioRxiv2026

Evidence for recombination in dengue virus genomes

Recombination is a key driver of RNA virus evolution, yet its extent and evolutionary implications in dengue virus (DENV) remain incompletely understood. We conducted a comprehensive, genome-wide recombination screen across 6,905 complete DENV genomes representing all four serotypes, 82 countries, and eight decades of sampling (1944-2023) retrieved from the Bacterial and Viral Bioinformatics Resource Center. Using seven complementary recombination detection methods implemented in RDP5, we identified 66 recombination events across 53 unique recombinant sequences, of which 29 are newly described. Events included intra-genotypic (n = 18), inter-genotypic (n = 32), and inter-serotypic (n = 16) exchanges spanning 14 genotypes and four continents, with no meaningful serotype-level enrichment (Cramers V = 0.054). Recombination was concentrated in non-structural genes, most frequently NS3 (19 events), NS5 (17), and NS2 (12), while the capsid gene contained no recombination events, consistent with strong functional constraint. Single-nucleotide polymorphism analyses confirmed low divergence between recombinants and their inferred parents in both recombinant and non-recombinant regions. Phylogenomic analysis of 6,642 sequences revealed that recombinants cluster significantly closer to their major parents (p = 8.9 x 10-6) and that their removal does not significantly alter tree topology (p = 0.898), suggesting that the short length of recombinant regions limits phylogenetic conflict. We also introduce RECOSIM, an unsupervised machine-learning tool for recombination detection that achieved higher precision than RDP5 on both simulated (93.4% vs. 80.0%) and empirical (98.1% vs. 39.3%) datasets. Collectively, these results establish recombination as a widespread, pan-serotypic phenomenon in DENV with implications for genomic surveillance, vaccine evaluation, and evolutionary inference.

bioinformatics↗

bioRxiv2026

HP2NET: Empowering Efficient Phylogenetic Network Analysis through High-Performance Computing

Advances in High-Performance Computing (HPC) have enabled increasingly complex genomic analyses, including those in phylogenomics. These analyses contribute to understanding the evolution of viruses and pathogens, improving our knowledge of disease transmission, and supporting targeted public health strategies. However, due to the increasing number of tools and processing steps involved, executing these analyses manually, step by step, becomes error-prone and inefficient. To address this challenge, we present HP2NET, a robust framework for reproducible, efficient, and scalable phylogenetic network analysis. HP2NET integrates five workflows based on state-of-the-art tools such as PhyloNetworks and PhyloNet, allowing the analysis of multiple datasets and workflows in a single execution. The framework includes features such as task packaging and data reuse to improve performance and resource utilization in HPC environments. We perform a comprehensive performance evaluation of the software used within HP2NET, identifying bottlenecks and analyzing gains from parallel processing. Data reuse provided up to 15.35% time reduction, for a small dataset, in our experimental environment, while parallel execution of the five pipelines reduced total runtime by up to 90.96% compared to sequential runs. Finally, we validate HP2NET in a real-world case study by analyzing Dengue virus genomes, demonstrating its applicability value for large-scale phylogenetic analyses.

bioinformatics↗

bioRxiv2025

MettleRNASeq: Complex RNA-Seq Data Analysis and Gene Relationships Exploration Based on Machine Learning

Typical differential gene expression (DGE) analysis might struggle when RNA-Seq datasets possess characteristics that hinder the power of statistical analyses and the obtention of accurate conclusions, such as a limited number of replicates and high variability. We present MettleRNASeq, a robust alternative for complex RNA-Seq data analysis that integrates machine learning techniques - a tailored classification approach, association rule mining, and complementary correlation analysis - to accurately identify key genes that distinguish experimental conditions and emphasize gene relationships. This approach provides full control over critical parameters, making it versatile for transcriptomic analyses and enhancing the comprehension of disease mechanisms, treatments, and their progression. MettleRNASeq was applied for the analysis of complex radiotherapy datasets. While popular DGE tools showed an inability to accurately differentiate the distinct radiotherapy treatments, MettleRNASeq effectively and consistently indicated relevant genes for condition discrimination and identified meaningful gene relationships related to radiotherapy, highlighting condition-specific and shared gene relationships. MettleRNASeq is implemented as an R package and available on GitHub at https://github.com/SamellaSalles/MettleRNASeq.

bioinformatics↗

bioRxiv2025

Analyses of phylogenetics, natural selection, and protein structure of clade 2.3.4.4b H5N1 Influenza A reveal that recent viral lineages have evolved promiscuity in host range and improved replication in mammals in North America

Influenza A virus has been circulating in birds from Eurasia for more than 146 years, but human infection has been sporadic. H5N1 (clade 2.3.4.4b) has recently infected hundreds of species of wild and domestic birds and mammals in North America. Infections include 70 people with two fatalities. We have developed an analytical bioinformatics, genomics, and structural workflow to understand better how H5N1 is circulating in North America and adapting to new host species.Our time-series analysis reveals that the circulation of H5N1 (clade 2.3.4.4b) in North America follows a distinct annual pattern, with cases in the United States consistently peaking each December. Separate from this seasonal cycle, our analysis also documents an increase in the total number of cases reported since 2021. We also show that H5N1 (clade 2.3.4.4b) spreads in North America as two distinct subclades of interest for human and animal health. These viral lineages have achieved a vast host range by efficiently binding the viral surface protein Hemagglutinin to both mammalian and avian cell surface receptors. This novel promiscuity of host range is concomitant with the additional strengthening of the Polymerase basic 2 viral proteins binding for mammalian and avian immune proteins. Once bound, the immune proteins will have diminished ability to fight the virus, thus allowing for more efficient replication of H5N1 in mammalian and avian cells than seen in the recent past. Finally, structural docking analyses predict that while most current antivirals remain effective, a fatal human isolate showed significantly reduced binding to multiple drugs from different classes. In conclusion, the H5N1 virus is causing an animal pandemic through promiscuity of host rage and strengthening ability to evade the innate immune systems of both mammalian and avian cells.

genomics↗

bioRxiv2023

CellHeap: A scRNA-seq workflow for large-scalebioinformatics data analysis

AbstractO_ST_ABSBackgroundC_ST_ABSSeveral hundred terabytes of single-cell RNA-seq (scRNA-seq) data are available in public repositories. These data refer to various research projects, from microbial population cells to multiple tissues, involving patients with a myriad of diseases and comorbidities. An increase to several Petabytes of scRNA-seq data available in public repositories is a realistic prediction for coming years. Therefore, thoughtful analysis of these data requires large-scale computing infrastructures and software systems optimized for such platforms to generate correct and reliable biological knowledge. ResultsThis paper presents CellHeap, a flexible, portable, and robust platform for analyzing large scRNA-seq datasets, with quality control throughout the execution steps, and deployable on platforms that support large-scale data, such as supercomputers or clouds. As a case study, we designed a workflow to study particular modulations of Fc receptors, considering mild and severe cases of COVID-19. This workflow, deployed in the Brazilian Santos Dumont supercomputer, processed dozens of Terabytes of COVID-19 scRNA-seq raw data. Our results show that most of the workflow total execution time is spent in its initial phases and that there is great potential for a parallel solution to speed up scRNA-seq data analysis significantly. Thus, this workflow includes an efficient solution to use parallel computational resources, improving total execution time. Our case study showed increased Fc receptors transcription in macrophages of patients with severe COVID-19 symptoms, especially FCGR1A, FCGR2A, and FCGR3A. Furthermore, diverse molecules associated with their signaling pathways were upregulated in severe cases, possibly associated with the prominent inflammatory response observed. ConclusionFrom the CellHeap platform, different workflows capable of analyzing large scRNA-seq datasets can be generated. Our case study, a workflow designed to study particular modulations of Fc receptors, considering mild and severe cases of COVID-19, deployed on the Brazilian supercomputer Santos Dumont, had a substantial reduction in total execution time when jobs are triggered simultaneously using the parallelization strategy described in this manuscript. Regarding biological results, our case study identified specific modulations comparing healthy individuals with COVID-19 patients with mild or severe symptoms, revealing an upregulation of several inflammatory pathways and an increase in the transcription of Fc receptors in severe cases.

bioinformatics↗