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        <title>Frontiers in Bioinformatics | Genomic Analysis section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/bioinformatics/sections/genomic-analysis</link>
        <description>RSS Feed for Genomic Analysis section in the Frontiers in Bioinformatics journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-13T06:08:48.50+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1829278</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1829278</link>
        <title><![CDATA[Preoperative APOE and Alzheimer’s disease polygenic risk profiling for perioperative neurocognitive disorders]]></title>
        <pubdate>2026-05-12T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Mengquan Tan</author><author>Jiling Zeng</author><author>Huixian Zhou</author><author>Shilin Yang</author><author>Tong Wang</author><author>Meiling Zhong</author><author>Tongyu Wang</author><author>Zheng Liu</author><author>Yaling Dai</author><author>Siyuan Song</author>
        <description><![CDATA[Perioperative neurocognitive disorders (PND) include postoperative delirium within 7 days after surgery, delayed neurocognitive recovery up to 30 days, and postoperative neurocognitive disorder up to 12 months. These outcomes are related, but they are not the same. They arise from the interaction of baseline brain vulnerability and perioperative stress, including inflammation, vascular instability, blood–brain barrier injury, metabolic strain, and reduced neural reserve. Preoperative genetic profiling is useful because it can estimate latent susceptibility before surgery. Among current signals, APOE is the strongest and most biologically relevant locus. At the same time, Alzheimer’s disease polygenic risk scores (AD-PRS) can capture non-APOE common-variant burden across lipid transport, endosomal trafficking, innate immune signaling, complement activity, microglial regulation, mitochondrial stress, and neurovascular integrity. Recent perioperative cohort studies have begun to test preoperative APOE-based and polygenic neurocognitive risk in surgical patients. Large delirium genetics studies also show a strong signal at the APOE locus and support overlap between delirium risk and Alzheimer’s disease-related common-variant architecture. These findings support an APOE-aware framework in which APOE genotype is modeled separately from non-APOE AD-PRS. In clinical use, this genomic layer should be combined with baseline cognition, frailty, vascular comorbidity, surgery-related risk, and circulating biomarkers such as neurofilament light chain. This review summarizes the loci, molecular pathways, and translational model designs that can move preoperative genomic profiling from association to perioperative risk stratification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1810235</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1810235</link>
        <title><![CDATA[A machine learning-derived genomic dataset from bacteria frequently reported as probiotics]]></title>
        <pubdate>2026-04-22T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Diego Lucas Neres Rodrigues</author><author>Pedro Alexandre Sodrzeieski</author><author>Sandrine Auger</author><author>Jean-Marc Chatel</author><author>Ana Maria Benko-Iseppon</author><author>Vasco Azevedo</author><author>Siomar de Castro Soares</author><author>Flávia Figueira Aburjaile</author>
        <description><![CDATA[Probiotics are live microorganisms that have been widely investigated for their association with beneficial host outcomes, particularly in the context of gut-associated microbial communities. Despite extensive literature, the probiotic effects are recognized as strain-specific and highly context-dependent, which limits the identification of universal genetic determinants of probiosis. In this study, we present a machine learning-derived genomic dataset generated from comparative analyses of bacterial genomes belonging to taxa frequently reported as probiotics and reference gut-associated bacteria. Using pangenomic analysis combined with supervised machine learning approaches, including Random Forest, Support Vector Machine, and Logistic Regression, we extracted discriminative genomic features from large-scale genome data. The resulting dataset comprises 1,072 non-redundant protein-coding sequences, accompanied by gene presence-absence matrices and functional annotations. These features should not be interpreted as causal determinants of probiotic functionality, but rather as genomic patterns associated with bacterial taxa commonly used as probiotics, which may also reflect taxonomic and ecological signatures. All data and scripts used in this study are publicly available through an open-access repository, providing a reusable resource for exploratory analyses, comparative genomics, and methodological benchmarking in probiogenomics and microbial genomics. The final data, hereby called ProbioSML, is currently available on https://doi.org/10.5281/zenodo.14181443.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1767204</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1767204</link>
        <title><![CDATA[Diversity and evolution of quorum-sensing systems in Rhizobium]]></title>
        <pubdate>2026-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ivana Blancas-Nava</author><author>Erick Cruz-Santiago</author><author>Gabriela Guerrero</author><author>Rosa-Maria Gutierrez-Rios</author><author>Miguel A. Cevallos</author>
        <description><![CDATA[Quorum-sensing (QS) systems based on acyl-homoserine lactones (AHLs) regulate gene expression in response to cell density in many bacteria, including Rhizobium. These systems, typically composed of LuxI-like synthases and LuxR-like regulators, control processes such as plasmid conjugation, biofilm formation, and plant interactions. However, their evolutionary dynamics and genomic distribution in Rhizobium remain poorly understood. We analyzed 142 complete Rhizobium genomes using comparative genomics, phylogenetic reconstruction, and genomic context analysis. LuxI/LuxR homologs were identified based on sequence similarity and Pfam domain architecture, and their genomic contexts were examined. Phylogenetic relationships and coevolution between LuxI/LuxR pairs were assessed using cophylogenetic approaches. QS systems showed a highly heterogeneous distribution across Rhizobium genomes: some strains lacked canonical systems, whereas others encoded one or multiple systems in chromosomes and/or plasmids. Chromosomal QS systems were associated with multiple distinct genomic contexts, supporting at least seven independent acquisition events. In contrast, plasmid-encoded systems exhibited substantially greater diversity in both sequence and genomic organization. Phylogenetic and comparative analyses revealed dynamic gains and losses of QS systems, variable coevolution among LuxI/LuxR pairs, and evidence of partner recruitment. Notably, plasmids appear to act as major reservoirs of QS systems and likely sources of their transfer to chromosomes. These findings indicate that QS systems in Rhizobium evolve through a combination of horizontal gene transfer, genomic rearrangement, and differential retention across replicons. The higher diversity and mobility of plasmid-encoded systems highlight their central role in shaping QS evolution and functional innovation. Overall, this study provides a comprehensive framework for understanding the diversification and evolutionary trajectories of QS systems in complex multipartite bacterial genomes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1743474</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1743474</link>
        <title><![CDATA[Bulk RNA-seq datasets analysis integration identifies robust drought-responsive genes and functional networks in Eucalyptus grandis]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>João Vítor Aires-Teixeira</author><author>Nivea Maria Pereira Lima</author><author>Renato Almeida Sarmento</author><author>Gabriel Quintanilha-Peixoto</author><author>Kellen Kauanne Pimenta de Oliveira</author>
        <description><![CDATA[IntroductionEucalyptus grandis is a cornerstone of global forestry, yet its productivity is increasingly threatened by drought. Understanding the molecular mechanisms underlying drought response is essential for improving resilience.MethodsWe conducted a meta-analysis of three independent RNA-Seq drought studies in E. grandis, applying a rigorous bioinformatic pipeline with leave-one-out Jackknife validation to ensure robustness and reduce single-study bias.ResultsWe identified a high-confidence set of 472 differentially expressed genes (DEGs), including 274 upregulated and 198 downregulated genes. Functional analysis revealed a growth-defense tradeoff, with upregulated genes associated with stress response pathways such as protein folding, osmolyte biosynthesis, and ABA signaling, while genes involved in cell division, DNA replication, and cell wall biosynthesis were repressed. Protein–protein interaction analysis showed a coordinated network linking stress response activation to suppression of growth-related processes.DiscussionThese findings provide a robust catalog of candidate genes, including previously uncharacterized proteins, supporting future functional studies and molecular breeding strategies aimed at enhancing drought tolerance in eucalyptus under climate change.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1736501</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1736501</link>
        <title><![CDATA[Integrated molecular dynamics elucidation of TP53 H179 zinc-binding variants: genomic and structural characterization across NSCLC subtypes]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ankur Datta</author><author>C. George Priya Doss</author>
        <description><![CDATA[BackgroundNon-Small Cell Lung Cancer (NSCLC), the most prevalent form of pulmonary malignancy, is primarily classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). TP53 gene is the most frequently mutated gene across numerous cancers. p53, a metalloprotein is stabilized by a tetrahedral Zn2+ binding motif involving Cys176, Cys238, Cys242, and His179. The His179 site, despite its structural importance, remains underexplored.MethodsTCGA mutational profiles were evaluated for 616 LUAD and 544 LUSC individuals. This study focuses on mutational perturbations at the His179 locus, a key residue within the protein’s zinc-binding motif. Frequent substitutions at H179 (Y/R/N/L/D) were identified across LUAD and LUSC cohorts. The structural and functional ramifications of these mutations were studied using combinatorial static structural analysis and atomistic molecular dynamics simulations (MDS). Conformational trajectories were analyzed to assess alterations in protein flexibility and functionally critical regions. Binding affinity values of the protein with Zn2+ were also evaluated for all mutants.ResultsC > A was the predominant single-nucleotide substitution observed, with TP53 gene mutations present in 50% of LUAD and 81% of LUSC cases. All five H179 (Y/R/N/L/D) variants exhibited distinct conformational signatures and resulted in compromised protein stability. Contact maps indicated altered residue-level interaction patterns in the mutants as compared to and the wildtype. The energy landscape of the mutants was also observed to be altered in comparison to the wildtype. Structural perturbations were evident in L1 and L2 loops, indicating that these regions are involved in mutation-induced structural plasticity.DiscussionThe results observed underscore the pathogenic potential of His179 mutations within the p53 Zinc-binding motif. The findings highlight the critical role of the Zinc-binding motif in maintaining p53’s conformational fidelity and suggest that specific substitutions may differentially modulate its tumor-suppressive function.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1677453</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1677453</link>
        <title><![CDATA[Bioinformatics strategies and biomarker refinement using high-throughput transcriptome data in transplantation]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Oliver P. Günther</author><author>Karen R. Sherwood</author><author>Franz Fenninger</author><author>Robert F. Balshaw</author><author>Andreas Scherer</author><author>Zsuzsanna Hollander</author><author>Raymond Ng</author><author>Janet Wilson-McManus</author><author>W. Robert McMaster</author><author>Bruce M. McManus</author><author>Paul A. Keown</author>
        <description><![CDATA[IntroductionRenal transplantation is the treatment of choice for kidney failure, but most transplants fail prematurely and barely half of recipients survive with a functioning graft for more than a decade. Strategies to induce operational tolerance are therefore at the cutting edge of transplant research, exploiting the dynamic plasticity of the immune system to recapitulate neonatal ontogeny and permit gradual withdrawal of immune suppression. We have shown that whole blood gene expression is profoundly altered in uremia and following graft implantation, and that changes in the blood transcriptome are characteristic of rejection injury. But deriving simple, robust and parsimonious classifiers presents challenges, and pre-filtering methods of varying stringency have been proposed to enhance predictive accuracy.MethodsWe re-analyzed our previous data documenting transcriptome changes in rejection using a case-control design to compare analytical strategies in subjects with or without biopsy-proven rejection. Five pre-filtering methods and eight multivariate classification methods were evaluated using multiple partition nested cross-validation to obtain unbiased estimation of classifier performance.ResultsThe most permissive strategy identified 800 unique genes and the most restrictive identified 71 nested genes differentially expressed in rejectors of which 31%–45% were downregulated and 55%–69% were upregulated, reflecting neutrophil degranulation, regulated necrosis, programmed cell death, pyroptosis, interleukin signaling and other functional pathways. Of the ten most common genes or probe-sets over all panels, nine were increased in BCAR.DiscussionNo individual combination of methods presented superior performance among all those considered although the PAM and XGBoost classifiers were more resistant to over-fitting. It is therefore advisable to apply multiple analytical combinations and compare performances in transcriptome analysis. In limited resource situations, evaluation of at least two complementary classifiers with fixed pre-filter and ranking methods is advisable. For small panel size constraints, feature-selecting methods like PAM or EN could be considered.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1751616</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1751616</link>
        <title><![CDATA[Development and validation of a pipeline for the systematic search for new HLA alleles in WGS data]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Eugene Albert</author><author>Andrei Deviatkin</author><author>Daria Smirnova</author><author>Mary Woroncow</author><author>Gauhar Zobkova</author><author>Olga Mityaeva</author><author>Anna Smirnova</author><author>Viktor Bogdanov</author><author>Pavel Volchkov</author>
        <description><![CDATA[BackgroundHuman leukocyte antigen (HLA) is a highly polymorphic locus in the human genome that also has high clinical significance. New alleles of HLA genes are constantly being discovered but mostly by HLA typing laboratories using field-specific protocols, such as enrichment of the HLA region in high-throughput sequencing data. Nevertheless, a vast amount of whole-genome sequencing (WGS) data has been accumulated over the past years. The main goal of our work was to develop and validate a pipeline specifically tailored toward the identification and characterization of new HLA alleles from 30x WGS sequencing.ResultsIn this article, we present a pipeline, HLAchecker, which is specifically designed to identify potentially new HLA alleles based on discrepancies between predicted HLA types, generated using any other dedicated tool, and the underlying raw 30x WGS data. HLAchecker reports results in a structured way that simplifies further validation of potentially new HLA alleles and streamlines the submission of alleles to appropriate databases. We validated this tool on 4,195 30x WGS samples and 6 HLA genes (A, B, C, DQA1, DQB1, and DRB1) typed by HLA-HD and discovered 17 potentially new HLA alleles with substitutions in exonic regions. We further validated five of these alleles using Sanger sequencing and submitted them to the IPD-IMGT/HLA database.ConclusionHLAchecker is suitable for the identification of new HLA alleles in large WGS cohorts accumulated by the scientific community in recent years. HLAchecker is freely available at https://gitlab.com/EugeneA/hlachecker.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1770550</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1770550</link>
        <title><![CDATA[Japanese genomes for pharmacogenomics: primary and secondary pipelines for population-specific insights]]></title>
        <pubdate>2026-03-19T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Charles W. Crawford</author><author>Yuka Nakano</author><author>Marika Hayashi</author><author>Antony Ibrahim</author><author>Issei Kono</author><author>Iri Sato-Baran</author><author>Takeshi Ozeki</author>
        <description><![CDATA[Pharmacogenomics (PGx) incorporates population level allele frequencies into its analyses. Often these population groupings are based on current or ancestral geographic location. However, these groupings can obscure internal variance caused by population heterogeneity. In order to increase the data accuracy and specificity for researchers, it is necessary to refine the population groupings. Often the necessary datasets have already been collected but have not been fully analyzed past their initial purpose. Here we provide a secondary pipeline that demonstrates a divergence between two datasets: the Clinical Pharmacogenetics Implementation Consortium (CPIC) collection of geographic populations and a Whole Genome Sequencing PGx dataset consisting of 632 Japanese individuals. Three classes of drugs and the relevant genes as identified by CPIC are examined: SSRIs (CYP2D6, CYP2B6, CYP2C19), opioid analgesics (CYP2D6) and statins (SLCO1B1). A meaningful divergence is shown between CPIC’s East Asian population and the Japanese population for opioid analgesics and statins. For opioid analgesics the Japanese population saw an increase in the “Use as directed” designation compared to the East Asian population from 53.2% to 71.0%; the statins data showed a decrease from 75.7% in the East Asian population 67.6% in the Japanese population. This divergence demonstrates that existing WGS data can reveal PGx patterns masked by broad geographic groups through the application of an appropriate secondary pipeline, enabling 0population specific implementation and refined population-level PGx inference without the need for further sample collection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1727493</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1727493</link>
        <title><![CDATA[Identification of key genes in chickpea transcriptomics and the development of ChickpeaOmicsR as a comprehensive resource to advance breeding and genomic studies]]></title>
        <pubdate>2026-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alsamman M. Alsamman</author><author>Khaled H. Mousa</author><author>Asmaa E. Abd El-Hak</author><author>Doaa A. Korkar</author><author>Anas M. Saedwi</author><author>Sandy Khaled</author><author>Al-Sayed Al-Soudy</author><author>Achraf El Allali</author><author>Zakaria Kehel</author><author>Morad M. Mokhtar</author>
        <description><![CDATA[IntroductionChickpea (Cicer arietinum L.) is a key legume crop and a major source of dietary protein in developing countries, yet its productivity is constrained by multiple biotic and abiotic stresses. Advances in RNA-seq and whole-genome sequencing enable detailed exploration of stress-responsive gene expression, but existing resources lack integrated, user-friendly tools for multi-omics analysis in chickpea.MethodsThis study analyzed transcriptomic responses to six stress conditions—drought, heat, cold, salinity, Fusarium infection, and developmental stages—using publicly available RNA-seq datasets. We identified differentially expressed genes (DEGs), enriched gene ontology (GO) terms, and protein–protein interaction (PPI) networks. Critically, we developed ChickpeaOmicsR, the first comprehensive R package that automates the integration of transcriptomic, genomic, and proteomic data and standardizes fragmented chickpea gene nomenclature; enables breeders without bioinformatics expertise to perform complex analyses (e.g., DEG identification, PPI visualization, GWAS integration) in minutes; and provides pre-validated datasets and analytical workflows unavailable in existing tools.ResultsEach stress triggered distinct molecular pathways. Drought and heat stress affected cell wall organization and defense responses, while cold stress influenced circadian rhythm genes. Fusarium stress involved pathways related to innate immunity and secondary metabolism. Developmental stages showed the highest transcriptome variability among the conditions tested.DiscussionThe development of ChickpeaOmicsR addresses critical gaps in chickpea research infrastructure. By providing an integrated and accessible tool that enables complex analyses for breeders without bioinformatics expertise, it accelerates the discovery of stress-resilient genes and the development of improved chickpea varieties.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1750649</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1750649</link>
        <title><![CDATA[Mutational insights and in silico characterization of NEK family kinases in OSCC patients from the Pakistani population]]></title>
        <pubdate>2026-02-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fouzia Nawab</author><author>Wafa Naeem</author><author>Sadia Fatima</author><author>Muhammad Uzair Khan</author><author>Aamir Mehmood</author><author>Sadia Nawab</author><author>Ishaq Khan</author><author>Haseena Nawaz</author><author>Hilal Ahmad</author><author>Ali Talha Khalil</author><author>Ishtiaq Ahmad Khan</author><author>Muhammad Irfan</author><author>Mohammed Alorini</author><author>Syed Ali Khurram</author><author>Asif Ali</author>
        <description><![CDATA[IntroductionOral squamous cell carcinoma (OSCC) is a prevalent malignancy characterized by aggressive behavior, poor prognosis, and limited therapeutic options. Mutations in the NIMA-related kinase (NEK) family are increasingly implicated in tumorigenesis across various cancers. However, their contributions to OSCC pathogenesis remain largely unexplored.MethodsHere, we employed whole-exome sequencing (WES) of formalin-fixed paraffin-embedded (FFPE) tissue blocks from 31 OSCC tumors and 9 adjacent paired normal samples derived from patients of Khyber Pakhtunkhwa (KP), Pakistan, to systematically profile NEK gene alterations. Subsequent in-silico analyses were performed to evaluate the structural and functional consequences of the identified mutations.ResultsWe identified 46 mutations overall (78.3% (36/46) somatic, 21.7% (10/46) germline), consisting of 82.6% (38/46) non-synonymous single-nucleotide variants (SNVs), 10.9% (5/46) frameshift deletions, 2.2% (1/26) non-frameshift deletions, and 4.3% (2/46) stop-gain mutations; notably, 10.9% (5/46) represented novel variants (not reported previously). NEK1 displayed the highest mutation frequency, followed by NEK10, NEK5, NEK11, NEK2, and NEK3. ISPRED-SEQ classified 37.0% (17/46) of mutations as residing at protein-protein interaction interfaces, indicating potential functional relevance, with several mutations including NEK1p.D409Y, NEK1p.N643K, NEK9 p.H174Y, NEK10 p.R275C, and NEK10 p.E596K predicted to be deleterious and destabilizing by multiple tools, occurring at conserved residues and altering structural stability via molecular dynamics simulations. Clinically, NEK4 mutations were significantly associated with tumor site (P=0.02), NEK9 with tobacco exposure (P=0.01), and NEK10 with improved overall survival (P=0.01). Mutations including NEK11p.E347V (31/31), NEK9p.R429H (23/31), NEK10p.L513S (15/31), NEK4p.P136A (7/31), NEK5p.K255Q (6/31) and NEK1 p.E650G (5/31) were found to be recurring mutations and can be validated further in large-scale studies for biomarker applicability.ConclusionCollectively, these findings suggest NEK mutations as candidate drivers of OSCC pathogenesis, underscoring their potential as prognostic biomarkers and therapeutic targets, particularly in tobacco-associated disease.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1740722</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1740722</link>
        <title><![CDATA[GPXplore: an intelligent computational framework for precise gene promoter extraction]]></title>
        <pubdate>2026-02-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shruti Godara</author><author>Samarth Godara</author><author>Shbana Begam</author><author>Anil Kumar Singh</author>
        <description><![CDATA[Efficient and precise extraction of gene promoter regions is vital for understanding gene regulation, with broad implications in gene editing, functional genomics, and disease research. However, existing tools often fall short in scalability, usability and performance. To address these limitations, we present “GPXplore,” a computational tool designed for the precise and user-friendly extraction of gene promoters from genomic data. It leverages vectorized data processing techniques to significantly reduce data processing time, enhancing speed and efficiency in large-scale promoter extraction tasks. GPXplore retrieves upstream and downstream sequences relative to gene loci and supports customizable parameters, enabling users to define region lengths based on specific research needs. The tool is implemented in Python, features both a command-line and graphical user interface, and is compatible with Windows and Ubuntu platforms. GPXplore was rigorously validated using eight diverse genomic datasets, demonstrating high accuracy and reliability. By combining automation, flexibility, and accessibility, GPXplore provides a robust solution for researchers across varying levels of computational expertise, facilitating high-throughput promoter analysis in modern genomics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1703356</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1703356</link>
        <title><![CDATA[REST missense mutations reveal disrupted Re1 motif binding and co-repressor interactions in uterine fibroids]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Srineevas Sriram</author><author>Chandresh Palanichamy</author><author>P. T. Subash</author><author>Manshi Kumari Gupta</author><author>C. Sudandiradoss</author>
        <description><![CDATA[IntroductionThe Re1-Silencing Transcription Factor (REST) is a master regulator of gene silencing, orchestrating transcriptional repression by tethering chromatin-modifying co-repressors to the Re1 motif of target genes. While REST is recognized as a sentinel of cellular identity, its role in uterine fibroids (UF) remains unclear. This study aims to investigate how structural perturbations in REST may compromise its regulatory function and contribute to altered transcriptional control in fibroid biology.MethodsA deep structural interrogation of REST was performed through expansive in silico analysis of 938 missense SNPs. Evolutionary conservation was assessed across ten primate species to identify structurally disruptive variants. Structural modelling, protein–protein and protein–DNA docking analyses were conducted to evaluate interactions with co-repressors and DNA. Molecular dynamics simulations were used to assess conformational stability, flexibility, compactness, and energetic changes in wild-type and mutant REST variants.ResultsFive structurally disruptive REST variants (Y31C, Y31D, L76Q, Y283C, L427Q) were identified at evolutionarily conserved residues. Structural modelling and docking analyses revealed weakened affinity for co-repressors, with the Y283C variant showing a marked reduction in SIN3A interaction (Z-score: 2.4 to −1.2) and impaired DNA binding (Z-score: 2.0 to −1.3). Molecular dynamics simulations demonstrated that Y283C increased rigidity (RMSF: 0.33 to 0.27 nm), reduced compactness (Rg: 3.48–3.51 nm), and lowered potential energy. Upon Re1 binding, destabilization intensified, with increased RMSD (0.95–1.07 nm) and pronounced shifts in energy.DiscussionThis integrative analysis highlights REST as a candidate regulatory component in uterine fibroid biology. Structural disruption of REST, particularly through the Y283C mutation, may destabilize molecular interactions and compromise DNA-binding precision, potentially unleashing transcriptional noise that fuels fibroid growth. These findings suggest that perturbation of REST-mediated transcriptional repression may be associated with altered regulatory control in this disease and could inform future strategies to investigate dysregulation in uterine fibroids.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1694924</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1694924</link>
        <title><![CDATA[A Transformers-based framework for refinement of genetic variants]]></title>
        <pubdate>2026-01-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Omar Abdelwahab</author><author>Davoud Torkamaneh</author>
        <description><![CDATA[Accurate variant calling refinement is crucial for distinguishing true genetic variants from technical artifacts in high-throughput sequencing data. While heuristic filtering and manual review are common approaches for refining variants, manual review is time-consuming, and heuristic filtering often lacks optimal solutions, especially for low-coverage data. Traditional variant calling methods often struggle with accuracy, especially in regions of low read coverage, leading to false-positive or false-negative calls. Advances in artificial intelligence, particularly deep learning, offer promising solutions for automating this refinement process. Here, we present a Transformers-based framework for genetic variant refinement that leverages self-attention to model dependencies among variant features and directly processes VCF files, enabling seamless integration with standard pipelines such as BCFTools and GATK4. Trained on 2 million variants from the GIAB (v4.2.1) sample HG003, the framework achieved 89.26% accuracy and a ROC AUC of 0.88. Across the tested samples, VariantTransformer improved baseline filtering accuracy by 4%–10%, demonstrating consistent gains over the default caller filters. When integrated into conventional variant calling pipelines, VariantTransformer outperformed traditional heuristic filters and, through refinement of existing caller outputs, approached the accuracy achieved by state-of-the-art AI-based variant callers such as DeepVariant, despite not operating as a standalone caller. By positioning this work as a flexible and generalizable framework rather than a single-use model, we highlight the underexplored potential of Transformers for variant refinement in genomics. This study contributes a blueprint for adapting Transformer architectures to a wide range of genomic quality control and filtering tasks. Code is available at: https://github.com/Omar-Abd-Elwahab/VariantTransformer.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1691056</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1691056</link>
        <title><![CDATA[A novel and accelerated method for integrated alignment and variant calling from short and long reads]]></title>
        <pubdate>2026-01-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jinnan Hu</author><author>Donald Freed</author><author>Hanying Feng</author><author>Hong Chen</author><author>Zhipan Li</author><author>Haodong Chen</author>
        <description><![CDATA[BackgroundIntegrating short- and long-read sequencing technologies has become a promising approach for achieving accurate and comprehensive genomic analysis. Although short-read sequencing (Illumina, etc.) offers high base accuracy and cost efficiency, it struggles with structural variant (SV) detection and complex genomic regions. In contrast, long-read sequencing (PacBio HiFi) excels in resolving large SVs and repetitive sequences but is limited by throughput, higher insertion or deletion (indel) error rates, and sequencing costs. Hybrid approaches may combine these technologies and leverage their complementary strengths and different sources of error to provide higher accuracy, more comprehensive results, and higher throughput by lowering the coverage requirement for the long reads.MethodsThis study benchmarks the DNAscope Hybrid (DS-Hybrid) pipeline, a novel integrated alignment and variant calling framework that combines short- and long-read data sequenced from the same sample. The DNAscope Hybrid pipeline is a bioinformatics pipeline that runs on generic x86 CPUs. We evaluate its performance across multiple human genome reference datasets (HG002–HG004) using the draft Q100 and Genome in a Bottle v4.2.1 benchmarks. The pipeline’s ability to detect small variants [single-nucleotide polymorphisms (SNPs)/indels)], SVs, and copy-number variations (CNVs) is assessed using data from the Illumina and PacBio sequencing systems at varying read depths (5×–30×). Benchmark results are compared to those of DeepVariant.ResultsThe DNAscope Hybrid pipeline significantly improves SNP and indel calling accuracy, particularly in complex genomic regions. At lower long-read depths (e.g., 5×–10×), the hybrid approach outperforms stand-alone short- or long-read pipelines at full sequencing depths (30×–35×), reducing variant calling errors by at least 50%. Additionally, the DNAscope Hybrid outperforms leading open-source tools for SV and CNV detection and enhances variant discovery in challenging genomic regions. The pipeline also demonstrates clinical utility by identifying variants in disease-associated genes. Moreover, DNAscope Hybrid is highly efficient, achieving less than 90 min runtimes at single standard instance.ConclusionThe DNAscope Hybrid pipeline is a computationally efficient, highly accurate variant calling framework that leverages the advantages of both short- and long-read sequencing. By improving variant detection in challenging genomic regions and offering a robust solution for clinical and large-scale genomic applications, it holds significant promise for genetic disease diagnostics, population-scale studies, and personalized medicine.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1671693</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1671693</link>
        <title><![CDATA[Identification and functional analysis of hub genes in knee osteoarthritis via bioinformatics and experimental validation]]></title>
        <pubdate>2025-12-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shanyong Jiang</author><author>Jingjing Cao</author><author>Jianshu Lu</author><author>Jianxiao Liang</author><author>Lianxin Li</author><author>Yanqiang Song</author><author>Jincheng Gao</author><author>Baoen Jiang</author>
        <description><![CDATA[ObjectiveKnee osteoarthritis (KOA) is a prevalent chronic degenerative joint disease that causes chronic pain and mobility restrictions in the elderly, significantly impacting quality of life. Current treatments focus on symptom relief, lacking effective interventions targeting the underlying mechanisms. Understanding KOA’s molecular mechanisms and identifying key pathogenic genes are essential for developing targeted therapies.MethodsGene expression data from KOA patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to reveal the associated biological processes and signaling pathways. Protein-protein interaction (PPI) network analysis and Gene Ontology-based semantic similarity calculations were used to identify hub genes. Gene Set Variation Analysis (GSVA) assessed enrichment in KOA-related pathways. Immune infiltration analysis (CIBERSORT) assessed the immune cell distribution in KOA tissues. Finally, hub gene expression changes were validated using the IL-1β-treated CHON-001 cell model and real-time quantitative PCR (RT-qPCR).ResultsA total of 3,290 upregulated and 2,536 downregulated DEGs were identified. GO and KEGG enrichment analyses revealed these genes were primarily involved in extracellular matrix remodeling, transmembrane transport, and inflammation-related pathways. Key hub genes, including HSPA5, FOXO1, and YWHAE, were identified. GSVA showed that these genes were significantly enriched in multiple KOA-associated signaling pathways. Immune infiltration analysis revealed significant differences in the levels of six immune cell types in KOA tissues, which were associated with the hub genes expression. In CHON-001 cell, the expression levels of GRB2, IKBKG, and HSPA12A were upregulated, whereas YWHAE, HSPB1, and DCAF8 were downregulated, consistent with the tissue samples.ConclusionThis study identified key pathogenic genes and their regulatory pathways in KOA, highlighting their potential role in disease progression via inflammation and immune modulation. These findings provide insights for developing targeted therapeutic strategies for KOA.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1708800</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1708800</link>
        <title><![CDATA[Pan-cancer analyses identify oncogenic drivers, expression signatures, and therapeutic vulnerabilities in RHO GTPase pathway genes]]></title>
        <pubdate>2025-12-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rubén Fernández</author><author>L. Francisco Lorenzo-Martín</author><author>Víctor Quesada</author><author>Xosé R. Bustelo</author>
        <description><![CDATA[RHO family GTPases are key regulators of cancer-related processes such as cytoskeletal dynamics and cell migration, proliferation, and survival. Despite this, a comprehensive understanding of RHO signaling alterations across tumors is still lacking. In this study, we present a pan-cancer analysis of 484 genes encoding RHO GTPases, regulators, proximal effectors, distal downstream signaling elements, and components of their proximal interactomes using data from over 10,000 tumor samples and 33 tumor types present in The Cancer Genome Atlas (TCGA). In addition, we have utilized available data from genome-wide functional dependency screens performed in more than 1,000 gene-edited cancer cell lines. This study has uncovered positively selected mutations in both well-known and previously uncharacterized RHO pathway genes. Transcriptomic profiling reveals widespread and tumor-specific differential expression patterns, with some of them correlating with copy number changes. Interestingly, certain regulators exhibit consistent expression profiles across tumors opposite to those predicted from their canonical roles. Co-expression and gene set enrichment analyses highlight coordinated transcriptional programs involving some RHO GTPase pathway genes and their linkage to key cancer hallmarks, including extracellular matrix reorganization, cell motility, cell cycle progression, cell survival, and immune modulation. Functional screens further identify context-specific dependencies on several deregulated RHO GTPase pathway genes. Altogether, this study provides a comprehensive map of RHO GTPase pathway alterations in cancer and identifies new oncogenic drivers, expression-based signatures, and therapeutic vulnerabilities that could guide future mechanistic and translational research in this area.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1695217</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1695217</link>
        <title><![CDATA[Subtractive genomic approach to uncover novel drug targets in Salmonella typhimurium and computational screening of food-based polyphenols as inhibitors]]></title>
        <pubdate>2025-12-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammed Naveez Valathoor</author><author>Subhashree Venugopal</author><author>Anand Prem Rajan</author>
        <description><![CDATA[IntroductionThe rise of multidrug-resistant Salmonella typhimurium is a severe public health threat that renders conventional antibiotics ineffective. This study employed a computational strategy to identify a novel drug target in S. typhimurium and screen food-based polyphenols as potential inhibitors.MethodsA subtractive genomics approach was used to identify essential, pathogen-specific proteins. A lead target was prioritized based on its druggability, localization, and network interactions. The target’s 3D structure was then modeled for molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations with a polyphenol library.ResultsThe screening identified UDP-N-acetylglucosamine transferase (MurG) as a promising and previously unexplored drug target. The polyphenol 6-prenylnaringenin showed a superior binding affinity for MurG compared to the antibiotic ciprofloxacin. Subsequent MD simulations and binding free energy calculations confirmed that the MurG-6-prenylnaringenin complex was significantly more stable.ConclusionThis study validates MurG as a druggable target in S. typhimurium and identifies 6-prenylnaringenin as a potent inhibitor. With computational metrics superior to ciprofloxacin, 6-prenylnaringenin is a promising lead compound for developing new anti-Salmonella therapeutics. Future experimental validation is required to confirm these in silico findings.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1680578</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1680578</link>
        <title><![CDATA[Functional and structural impacts of oncogenic missense variants on human polo-like kinase 1 protein]]></title>
        <pubdate>2025-12-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gayatri Munieswaran</author><author>Venkatraman Manickam</author>
        <description><![CDATA[IntroductionThe polo-like kinase 1 (PLK1), a master key mitotic regulator, is frequently expressed in various types of cancers and associated with poor prognosis. The missense mutations in PLK1 may compromise its structural integrity and functional interactions, contributing to tumorigenesis.MethodsThis study utilized a comprehensive computational pipeline to identify deleterious missense variants across multiple cancers. 207 non-synonymous single nucleotide polymorphisms (nsSNPs) were retrieved from cBioPortal, and 11 high-risk variants were prioritized using functional and structural prediction tools, such as SIFT, PolyPhen-2, I-mutant 2.0, and so on. Prognostic prevalence was evaluated via Kaplan-Meier survival analysis, and functional networks were explored using STRING. The structural dynamics of modeled mutations were analyzed through molecular dynamic simulations over 100 ns.ResultsThe kinase domain mutations such as L244F, R293C, and R293H and polo-box domain mutations such as A520T were found to cause deviations in structural stability, flexibility, solvent exposure, and compactness compared to wild-type. Further, PLK1 overexpression correlated with poor overall survival of patient outcomes in many types of cancers, including breast, liver, lung, kidney, and pancreatic cancers. Protein-protein interaction revealed PLK1’s involvement in oncogenic pathways.DiscussionThe study highlights the structural and functional implications of oncogenic PLK1 mutations, emphasizing their role in cancer progression. Integrating predictive and dynamic exploration approaches facilitates prioritization of variants with potential clinical relevance.ConclusionThe nsSNPs in PLK1 may perturb conformational stability and functions of the protein. Further experimental validation and discovery of novel inhibitors might develop mutation-specific interventions in precision oncology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1690229</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1690229</link>
        <title><![CDATA[Computational analysis of transcriptome data and mapping of functional networks in Parkinson’s disease]]></title>
        <pubdate>2025-11-19T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Konstantinos Perperidis</author><author>Themis P. Exarchos</author><author>Aristidis G. Vrahatis</author><author>Panagiotis Vlamos</author><author>Marios G. Krokidis</author>
        <description><![CDATA[Parkinson’s disease (PD) is the most common neurodegenerative movement disorder. The pathophysiology is defined by a loss of dopaminergic neurons in the substantia nigra pars compacta, however recent studies suggest that the peripheral immune system may participate in PD development. Herein, we analyzed molecular insights examining RNA-seq data obtained from the peripheral blood of both Parkinson’s disease patients and healthy control. Although all age and gender groups were analyzed, emphasis is given on individuals aged 50–70, the most prevalent group for Parkinson’s diagnosis. The computational workflow comprises both bioinformatics analyses and machine learning processes and the yield of the pipeline includes transcripts ranked by their level of significance, which could serve as reliable genetic signatures. Classification outcomes are also examined with a focus on the significance of selected features, ultimately facilitating the development of gene networks implicated in the disease. The thorough functional analysis of the most prominent genes, regarding their biological relevance to PD, indicates that the proposed framework has strong potential for identifying blood-based biomarkers of the disease. Moreover, this approach facilitates the application of machine learning techniques to RNA-seq data from complex disorders, enabling deeper insights into critical biological processes at the molecular level.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1690766</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1690766</link>
        <title><![CDATA[Multi-marker comparative analysis of 18S, ITS1, and ITS2 primers for human gut mycobiome profiling]]></title>
        <pubdate>2025-11-19T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Hiba Orsud</author><author>Sumaya Zoughbor</author><author>Fatima AlDhaheri</author><author>Khalid Hajissa</author><author>Manar Refaey</author><author>Suad Ajab</author><author>Khaled Alswaider</author><author>Nora Mohamed</author><author>Obaid Alkaabi</author><author>Zakeya Al Rasbi</author>
        <description><![CDATA[BackgroundGut fungi play crucial roles in human health. The profiling of the human gut mycobiome continues to progress. However, adjustments in the selection of ribosomal DNA marker regions can substantially affect the taxonomic resolution of a population. In particular, the impact of using primers’ combinations is insufficiently defined. In this study, we investigated the performance of three targeted sequencing regions, ITS1, ITS2 and 18S rRNA, separately and in combination.MethodsEight fecal samples from healthy individuals (n = 4) and cancer patients (n = 4) were selected as proof of principle for amplicon-based sequencing conducted with the DNBSEQ™ sequencing system. Quality-filtered reads were grouped into operational taxonomic units (OTUs) via USEARCH and categorized using the SILVA (18S) and UNITE (ITS) databases. Downstream bioinformatics encompassed diversity analyses, principal component analysis (PCA), and biomarker detection via linear discriminant analysis effect size (LEfSe). To improve taxonomic coverage and compositional understanding, data were examined using ALDEx2 with centered log-ratio (CLR) transformation, facilitating reliable differential abundance and effect size assessment in small sample metagenomic contexts.Results and DiscussionAmong primers, ITS2 and ITS1 enhanced the coverage of identified taxa, with operational taxonomic unit quantities of 183 and 158, respectively, compared to 58 OTUs of 18S. Accordingly, among primer combinations tested, the triple integration of ITS1–ITS2–18S produced the highest fungal richness, while the dual ITS1–ITS2 combined datasets enhanced group discrimination analysis, showing enrichment of Candida albicans and scarcity of Penicillium sp. in cancer patients. Our findings based on ITS sequencing and the combination of ITS1 and ITS2 provide instructive information on the composition and dynamics of gut fungi in our initial test subjects, enhancing our understanding of their roles in gut homeostasis and the microbial shifts associated with cancer. Despite our approach being conducted with a limited cohort to establish methodological feasibility, it brings attention to multi-marker strategies, demonstrating that integrated primer datasets surpass traditional single-marker methods in both taxonomic coverage and biomarker detection sensitivity in low-biomass fecal samples. Our research provides a reliable starting point for future studies on gut mycobiome in both healthy and diseased individuals, which could lead to better diagnostics and treatments based on microbiome profiles.]]></description>
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