Frontiers in bioinformatics最新文献

{"title":"PUDU (pipeline for universal diversity unveiling): an accessible end-to-end workflow for taxonomic profiling and ecological visualization of environmental microbiomes across amplicon, shotgun, and long-read sequencing.","authors":"Alejandro Medaglia-Mata, Pablo Rojas-Rodríguez, Vojtěch Bystrý, Rossy Guillén-Watson, Olman Gómez-Espinoza, Kattia Núñez-Montero","doi":"10.3389/fbinf.2026.1909327","DOIUrl":"10.3389/fbinf.2026.1909327","url":null,"abstract":"<p><strong>Background: </strong>Environmental microbiome research has advanced through three complementary sequencing modalities, targeted 16S rRNA amplicon sequencing, whole-genome shotgun (WGS) metagenomics, and long-read full-length 16S rRNA profiling, each supported by distinct toolsets with heterogeneous outputs, variable configurations, and different levels of reproducibility documentation. Existing pipelines are typically modality-specific, require substantial configuration expertise, or produce outputs that need further custom scripting before standard ecological analyses can begin. This analytical fragmentation introduces avoidable technical variability and complicates cross-study reproducibility and comparability. PUDU addresses this by integrating all three modalities into a single reproducible workflow with simplified configuration, harmonized outputs across classifiers, and direct compatibility with downstream ecological analysis frameworks.</p><p><strong>Results: </strong>We present PUDU (Pipeline for Universal Diversity Unveiling), a modular Snakemake workflow that supports amplicon (short-read 16S), shotgun metagenomics (WGS), and long-read 16S analyses from raw reads to standardized outputs for downstream microbial ecology. PUDU performs technology-aware preprocessing and centralized quality control, and integrates established taxonomic approaches, including DADA2 for amplicons, Emu for full-length 16S long reads, and Kraken2/Bracken and Centrifuger for WGS. Across methods, PUDU produces harmonized count and relative-abundance tables at user-defined taxonomic ranks, Krona files, and a standardized Phyloseq-compatible R object to streamline diversity analyses and statistical workflows. PUDU also provides an integrated Shiny interface for metadata-aware alpha/beta diversity, ordination, community composition, and shared-taxa exploration with exportable figures and taxa tables. We demonstrate PUDU on two publicly available environmental datasets spanning rhizosphere WGS and long-read marine sediment 16S, yielding broadly consistent community-level patterns across classifiers (Spearman ρ = 0.936 at phylum level; PERMANOVA R² = 0.87-0.95) with peak memory below 45 GB on a standard Linux workstation.</p><p><strong>Conclusion: </strong>PUDU is an end-to-end, reproducible, and extensible framework that enables standardized taxonomic profiling and ecology-oriented analysis across sequencing modalities. By combining harmonized outputs, Phyloseq interoperability, and an integrated visualization layer, PUDU facilitates reproducible, standardized, and comparable environmental microbiome analysis from raw reads to interpretable ecological insights.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1909327"},"PeriodicalIF":3.6,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13503585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LungMicroHostR: an R package for integrated host-microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data.","authors":"Nan Li, Jing Hu, Wanning Tong, Chengdong Liu, Yun Ding, Ning Li, Zhigang Cai","doi":"10.3389/fbinf.2026.1906036","DOIUrl":"10.3389/fbinf.2026.1906036","url":null,"abstract":"<p><strong>Introduction: </strong>Bronchoalveolar lavage fluid metagenomic next-generation sequencing captures microbial profiles and host-derived molecular measurements from the same respiratory specimen, but downstream analysis requires coordinated handling of low-biomass microbial signals, negative-control information and multiple feature tables.</p><p><strong>Methods: </strong>We developed LungMicroHostR, an R package for downstream host-microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data. The package brings processed microbial profiles, host-derived molecular measurements, sample metadata and negative-control information into a unified R workflow for feature filtering, comparative model evaluation, visualization and reproducible reporting.</p><p><strong>Results: </strong>Using the public GSE252118 resource comprising 402 samples from patients with lung cancer or pulmonary infections, LungMicroHostR assembled matched microbial, host and clinical feature tables, estimated prevalence in negative controls and compared host transcriptomic, microbial-profile and combined host-microbial models. In the test set, the 10-feature host transcriptome nearest-centroid model achieved an AUC of 0.772 (95% confidence interval, 0.680-0.860), the five-feature RNA microbial logistic model achieved an AUC of 0.745 (0.655-0.832), and the combined host transcriptome-RNA microbial logistic model achieved an AUC of 0.765 (0.655-0.866) with balanced accuracy of 0.720. An external PRJNA714488 BALF shotgun metagenomic dataset was additionally analysed at the mOTU level; LungMicroHostR matched the resulting feature table with phenotype metadata and generated a 388-feature by 26-sample microbial abundance matrix.</p><p><strong>Discussion: </strong>LungMicroHostR provides documented functions for respiratory metagenomic analyses that require joint evaluation of microbial profiles, host-derived measurements, negative-control information and external microbial feature tables.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1906036"},"PeriodicalIF":3.6,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple binding modes underlie <i>Cannabis sativa</i> cannabinoids recognition by peroxisome proliferator-activated receptor gamma.","authors":"N R Carina Alves, Justina Miranda, Lautaro D Alvarez","doi":"10.3389/fbinf.2026.1893303","DOIUrl":"10.3389/fbinf.2026.1893303","url":null,"abstract":"<p><strong>Introduction: </strong>Peroxisome proliferator-activated receptor gamma (PPARγ) is a ligand-activated nuclear receptor with broad therapeutic relevance across various pathologies, including type 2 diabetes, obesity, cancer, and inflammatory disorders. Cannabinoids are a class of terpene-phenolic compounds from <i>Cannabis sativa</i> L. that have been shown to act as partial agonists of PPARγ. Among them, the acidic forms Δ⁹-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) display higher potency than their decarboxylated counterp arts Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD). Despite experimental evidence supporting direct PPARγ-cannabinoid interaction, the molecular determinants governing ligand recognition within the binding pocket have not yet been comprehensively investigated.</p><p><strong>Methods: </strong>A combination of molecular docking and molecular dynamics simulations was employed to characterize the binding modes of THC, CBD, THCA, and CBDA within the PPARγ ligand-binding domain. Docking calculations were performed on a curated set of 70 PPARγ crystal structures co-crystallized with structurally diverse ligands, exploiting thus the conformational variability of the binding pocket. The best-ranked solutions were subjected to 500 ns MD simulations and evaluated on the basis of ligand stability, persistence of polar and aromatic-aromatic interactions with the receptor, and energetic contributions estimated by MM/GBSA. Three candidate binding modes per ligand were selected and their trajectories extended to 1,000 ns.</p><p><strong>Results: </strong>All four cannabinoids yielded at least one stable binding mode at the microsecond timescale. The cannabinoids THCA and CBDA displayed a greater number of stable binding modes than THC and CBD, a result consistent with the higher potency previously reported for these compounds in experimental studies. This behavior may be attributable to the formation of salt bridges with basic residues in the binding pocket.</p><p><strong>Conclusion: </strong>Our findings provide a structural framework for understanding cannabinoid recognition by PPARγ. The ability of these compounds to adopt multiple binding modes may contribute to their partial agonist profile, opening new avenues for the rational design of selective PPARγ modulators with improved therapeutic properties.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1893303"},"PeriodicalIF":3.6,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated analysis of enzymes, mRNAs, and miRNAs provides insight into the regulatory potential of extracellular vesicles in recipient cell glucose metabolism.","authors":"Namita N Kashyap, Sharath Mohan Bhat, Padmanabha Udupa E G, Kavitha S Shettigar, Vinutha R Bhat, Dinesh Upadhya","doi":"10.3389/fbinf.2026.1911554","DOIUrl":"10.3389/fbinf.2026.1911554","url":null,"abstract":"<p><strong>Introduction: </strong>Extracellular vesicles (EVs) are increasingly recognized as active coordinators of metabolic processes rather than mere messengers. By carrying unique subsets of enzymes, metabolites, lipids, and nucleic acids, EVs can directly deliver functional metabolic machinery or dynamically alter intracellular metabolic fluxes in recipient cells. However, their role in regulating specific biochemical pathways remains largely unknown.</p><p><strong>Methodology: </strong>In the current <i>in silico</i> analysis, we explored the dominant metabolic role of EV cargo using publicly available multi-omics data. For this, the top 500 mRNAs and proteins, along with miRNAs reported at least 10 times in humans across independent studies, as catalogued in the EVpedia database are considered and curated into a comprehensive dataset.</p><p><strong>Results: </strong>Enrichment analysis of these mRNAs and proteins revealed that carbohydrate metabolic pathways, including glycolysis, the pentose phosphate pathway and the TCA cycle, were over-represented in EVs. Further, to investigate whether EVs carry miRNAs that regulate these pathways, we analyzed the miRNA targets. Enrichment analysis of EV miRNA targets mapped glycolytic regulatory genes, including <i>HK1, HK2, PFKP and PKM</i>. Interestingly, we also found miRNAs targeting genes encoding glucose transporters (SLC2A1, SLC2A3, SLC2A4, and SLC2A14) reported in EVs. Genomic annotation of these miRNAs revealed them to form clusters, including the miR-17-92 cluster, a well-known regulator of glycolysis.</p><p><strong>Discussion: </strong>While the study has limitations-mainly due to the biological heterogeneity of EVs and the difficulty of standardizing cargo-the results potentially suggest that, by delivering enzymes, their mRNAs, regulatory miRNAs or combinations thereof, EVs could potentially mediate recipient cell glucose metabolism.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1911554"},"PeriodicalIF":3.6,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-enhanced virtual screening identifies a potent small-molecule modulator of ClC-3 for cervical cancer drug discovery.","authors":"Chao Liu, Chongxing Ji","doi":"10.3389/fbinf.2026.1887419","DOIUrl":"10.3389/fbinf.2026.1887419","url":null,"abstract":"<p><p>ClC-3 chloride channels play essential roles in cervical cancer progression by regulating lysosomal acidification, cell volume homeostasis, and chemoresistance. However, no highly selective small-molecule modulators of ClC-3 have been reported to date. Motivated by the urgent clinical need to reverse ClC-3-mediated chemoresistance and the challenge of processing massive chemical libraries under limited computational hardware resources, we propose a novel AI-driven Drug Discovery (AIDD) pipeline. Here, we present an integrated virtual drug discovery framework that transitions from traditional Computer-Aided Drug Design (CADD) by combining large-scale molecular docking, deep-learning-based rescoring, pharmacokinetic filtering, and atomistic molecular dynamics (MD) simulations. The primary advantage of this proposed scheme lies in the integration of GNINA 3D-convolutional neural network (CNN) rescoring, which significantly reduces the false-positive rates inherent in empirical scoring functions for membrane proteins. A library of ∼180,000 ZINC15 compounds was initially screened using AutoDock Vina, followed by GNINA convolutional neural network rescoring to refine predicted binding affinity and pose confidence. ADMET profiling further narrowed the candidates, providing computational proof of drug-likeness and toxicity criteria rather than experimental validation. Ultimately, only ZINC000001556308 (Lig8) satisfied all <i>in silico</i> criteria. To validate binding stability, we performed 100-ns all-atom MD simulations of the ClC-3-Lig8 complex embedded in a lipid bilayer. Lig8 induced reduced RMSD fluctuations, lower RMSF values across key transmembrane helices, and a more compact radius of gyration, indicating enhanced structural stabilization of ClC-3. MM/PBSA calculations confirmed favorable binding energetics dominated by van der Waals interactions, while per-residue decomposition identified PHE527, GLY283, and GLY584 as major contributors to ligand recognition. These results reveal a previously uncharacterized binding pocket within the ClC-3 transmembrane domain and highlight Lig8 as a promising lead compound for targeting ClC-3-mediated oncogenic signaling. Overall, this study establishes the first comprehensive computational framework for ClC-3 modulator discovery and provides a validated chemical scaffold for future therapeutic development against cervical cancer.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1887419"},"PeriodicalIF":3.6,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13493587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial transcriptomics reveal heterogeneous cell‒cell interactions among brain regions in cuprizone model consistent with multiple sclerosis lesions.","authors":"Hui-Hsin Tsai, Sarbottam Piya, Jing Wang, Jing Zhu, Wenxing Hu, Andrew R Gehrke, Shaolong Cao, Amanda J Guise, Su Jing Chan, Mark Sheehan, Jenhwa Chu, Zhengyu Ouyang, Matthew Ryals, Michelle Lee, Wanli Wang, Edward Zhao, Patrick Cullen, Ravi Challa, Eric Marshall, Wanyong Zeng, Yea Jin Kaeser-Woo, Chris Ehrenfels, Luke Jandreski, Helen McLaughlin, Thomas M Carlile, Jake Gagnon, Taylor L Reynolds, Mingyao Li, Kejie Li, Baohong Zhang","doi":"10.3389/fbinf.2026.1832826","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1832826","url":null,"abstract":"<p><p>The cuprizone (CPZ) model is widely used for modeling demyelination in multiple sclerosis (MS) and for testing potential remyelination therapies. To better understand the underlying pathology of the CPZ model and evaluate its translatability, we integrated single-cell and spatial transcriptomics (ST) to investigate spatial cellular and molecular interactions during de- and remyelination in multiple brain regions. ST revealed global demyelination and neuroinflammation in the brain beyond the corpus callosum (CC), with region-specific differences. We identified oligodendroglia and microglia as two major cell types with significant transcriptomic changes in the model. CPZ-associated subclusters of oligodendroglia (marker genes <i>Arap2</i>, <i>Dock10</i>, <i>Tenm4</i>, <i>Pex5l</i> and <i>Dock1</i>) and microglia (marker genes <i>ApoE</i>, <i>Axl</i>, <i>Cd9</i> and <i>Lpl</i>) were mapped to the CC by ST. During remyelination, while mature oligodendrocytes (MOL) nearly reversed their phenotype back to the control state, microglia remained associated with the demyelination phenotype. Ligand‒receptor (LR) pairing analyses predicted growth factor and phagocytic pathway enrichment during demyelination, which is consistent with changes in MS lesions, and microglia were predicted to be the major sender cells. LR pairing also predicted a high likelihood of interaction between oligodendroglia and microglia, and a novel interaction between MOL and oligodendrocyte precursor cells (OPC), underscoring their roles during de- and remyelination. Finally, astrocytes in the CPZ model had the greatest preservation of disease-associated modules in MS lesions, while MOL, OPC, and microglia showed moderate to low preservation, which overall suggests that the CPZ model has moderate translatability to chronically active MS lesions.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1832826"},"PeriodicalIF":3.6,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13490942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SiaRNA: a siamese neural network with bidirectional cross-attention for pairwise siRNA-mRNA efficacy prediction.","authors":"Vaishnavi Sapireddy, Rajkumar Nathi, Venkata Harshit Meruva, Varun Raju Nannapuraju, Bhargava Chary Basangari, Vani Kondaparthi","doi":"10.3389/fbinf.2026.1827877","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1827877","url":null,"abstract":"<p><strong>Introduction: </strong>Small interfering RNA (siRNA) therapeutics have extraordinary potential for targeted gene silencing. They mediate post-transcriptional gene regulation by binding to complementary messenger RNA (mRNA) sequences and degrading them, thereby preventing the production of unwanted proteins. Recent machine learning and deep learning frameworks for predicting siRNA efficacy have only achieved moderate success as these models solely rely either on handcrafted features or on sequential relations and therefore cannot capture the full complexity of siRNA-mRNA interactions.</p><p><strong>Methods: </strong>In the above context, we propose SiaRNA, which uses a Siamese Neural Network for feature-derived representations and a bidirectional cross-attention mechanism for sequence-level relationships. It uniquely identifies mRNAs and their corresponding siRNAs as paired entities, allowing unified and context-aware modeling. Unlike previous models, which discard 2-nucleotide (2-nt) overhangs at the 3' end while using 21-nt efficacy labels, SiaRNA both trains and tests on 21-nt sequences to ensure biologically consistent predictions.</p><p><strong>Results: </strong>Our model sets a new performance benchmark, outperforming previous state-of-the-art models. SiaRNA is trained on the HUVK dataset, achieving an accuracy of 0.881, and its generalization is confirmed by testing on the independent Simone dataset.</p><p><strong>Discussion: </strong>The results prove SiaRNA's potential as a reliable and biologically accurate framework to guide siRNA design and improve therapeutic outcomes. On performing a case study using Patisiran siRNA and its target transthyretin (TTR) mRNA, an efficacy value of 0.7134 was observed indicating that our model can successfully identify therapeutically effective targets.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1827877"},"PeriodicalIF":3.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13488420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the hypoxic injury landscape and hypoxic risk model construction in diabetic kidney disease: a multi-omics study.","authors":"Li Jiang, Chien Chieh, Haojun Zhang, Tingting Zhao, Xiai Wu","doi":"10.3389/fbinf.2026.1894439","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1894439","url":null,"abstract":"<p><strong>Objectives: </strong>To identify the core hypoxic injury pattern of DKD, construct a DKD risk model based on hypoxic injury-related (HIR) score, and explore the potential therapeutic targets of DKD.</p><p><strong>Methods: </strong>DKD-related microarray-based transcriptomic analyses, single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics were retrieved from the Gene Expression Omnibus (GEO) database. Seven HIR gene sets were obtained from various public databases. Core hypoxic genes were identified using different machine-learning algorithm. LASSO and nomogram were applied to construct a HIR risk score for cellular hypoxic damage. The detailed expression of hub gene would be showed in the single cell and kidney region. The prognostic value of the HIR score was externally validated using plasma proteomics from the United Kingdom Biobank.</p><p><strong>Results: </strong>Five core hypoxic injury pathways in DKD were identified: Hypoxia, Autophagy, Ferroptosis, Endoplasmic Reticulum (ER) Stress, and Apoptosis. The HIR risk score was constructed based on three hub genes: CASP3, DUSP1, and ZFP36. The HIR score demonstrated high diagnostic efficiency for DKD patients. Higher HIR scores were associated with significantly infiltrated immune cells and poorer kidney function. In United Kingdom Biobank validation, the HIR score significantly improved the prediction of kidney outcomes, renal death, and secondary endpoints beyond demographic and metabolic variables (AUC increments 0.04-0.06), and correlated negatively with eGFR and positively with lipoprotein(a). The calculated tissue-level HIR scores also showed a highly significant and robust increase in the renal microenvironment of BTBR ob/ob mice.</p><p><strong>Conclusion: </strong>These results provided a predictive model for clinical evaluation in patients with DKD and also a new insight into the role of HIR genes in the pathogenesis of DKD.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1894439"},"PeriodicalIF":3.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13486302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference.","authors":"Noor Jamal Alkhateeb, Mamoun Awad","doi":"10.3389/fbinf.2026.1883130","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1883130","url":null,"abstract":"<p><strong>Introduction: </strong>Existing methods for gene regulatory network (GRN) inference rely primarily on gene expression data alone or on lower-resolution bulk sequencing data. Despite recent advances in integrating chromatin accessibility and RNA sequencing, inferring GRNs from paired single-cell multi-omics data remains challenging due to noise, sparsity, and complex nonlinear regulatory relationships.</p><p><strong>Methods: </strong>We present MultiCausGRN, a graph attention network (GAT)-based framework for GRN inference from paired scRNA-seq and scATAC-seq data. The model incorporates directed prior-guided graph attention learning to capture biologically grounded regulatory directionality by integrating curated directed regulatory edges into graph representation learning. MultiCausGRN performs supervised transcription factor-target link prediction using integrated multi-omics features within a two-layer graph attention architecture.</p><p><strong>Results: </strong>On the human PBMC multi-omics dataset, prior knowledge integration improved predictive stability and achieved a mean test AUPRC of 0.743 <math><mrow><mo>±</mo></mrow> </math> 0.049 and a mean AUROC of 0.682 <math><mrow><mo>±</mo></mrow> </math> 0.026 across five independent random seeds.</p><p><strong>Discussion: </strong>These results demonstrate that directed prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings. MultiCausGRN is publicly available at: https://github.com/nrr-90/MultiCausGRN.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1883130"},"PeriodicalIF":3.6,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13481796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular docking and dynamics simulation studies uncover the host-pathogen protein-protein interactions in soybean (<i>Glycine max</i> (L.) Merr.) and <i>Groundnut bud necrosis virus</i>: first report.","authors":"Rayees Ahmad Bhat, Ayyagari Ramlal, Amooru Harika, Ashutosh Sharma, Ambika Rajendran, Iten M Fawzy, Sreeramanan Subramaniam, Dhandapani Raju, S K Lal, Sonu Krishankumar, Shyam S Kurup","doi":"10.3389/fbinf.2026.1821527","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1821527","url":null,"abstract":"<p><strong>Background: </strong>Soybean is a widely consumed oilseed crop with economic importance. It is enriched with numerous bioactive compounds that have health-promoting properties. <i>Groundnut bud necrosis virus</i> (GBNV) affects many agriculturally important crops, such as soybean, resulting in crop losses. GBNV is a single-stranded RNA virus from the genus Orthotospovirus and family Bunyaviridae. It is transmitted by insect vectors (thrips) and is further aggravated by secondary transmission from infected plants to others in the same field. There are no commercially available drugs against GBNV; thus, there is a need to explore phytomolecules that control the infection. This study utilizes soybean proteins (CAM, CDK1, Cul1, GSK3, HSP70, LOX, PCNA, and TCP) and GBNV proteins (CP, EGP, MP, NSP, NS, and RDRP) for the analysis of their inhibitory interactions. Physicochemical properties for both protein groups were examined. The molecular basis for the selective predictive association of these protein interactions was evaluated using <i>in silico</i> molecular docking approaches, dynamics simulations analysis, and post-simulation computational analyses.</p><p><strong>Results: </strong>The results indicate that among these 50 protein-protein interactions, heat shock protein 70 (HSP70) exhibited potential inhibitory action against the RNA-dependent RNA polymerase (RDRP) with -12.12 kcal/mol binding affinity (z-score: 0.0). RDRP is an essential enzyme required for the transcription and replication of the viral genome. HSPs function in both abiotic and biotic stresses. Although further studies are required, these preliminary findings will be useful for developing new therapeutic agents against the virus, thus paving the way for researchers to find better alternatives.</p><p><strong>Conclusion: </strong>This is the first study to combine prediction, structural validation, and interface analysis of the interaction between soybean and GBNV proteins.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1821527"},"PeriodicalIF":3.6,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13473904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148764292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}