Bioinformatics and Biology Insights最新文献

{"title":"Combating Drug Resistance in <i>Mycobacterium Tuberculosis</i>: A Combinatorial in Silico and Experimental Modeling Approach Toward Novel ATP Synthase Inhibitor Discovery.","authors":"Haidy H El-Zoheiry, Ratul Bhowmik, Ajay Manaithiya, Rajarshi Ray, Mahesh Samantaray, Amutha Ramaswamy, Ashok Aspatwar","doi":"10.1177/11779322261438313","DOIUrl":"10.1177/11779322261438313","url":null,"abstract":"<p><p>Adenosine triphosphate (ATP) synthase in <i>Mycobacterium tuberculosis</i> (Mtb) is essential for energy metabolism through oxidative phosphorylation, where ATP is synthesized from ADP. This enzyme supports bacterial survival during both active growth and dormancy, enabling Mtb to persist under stressful conditions. During dormancy, Mtb enters a non-replicating, drug-tolerant state that reduces the effectiveness of many antibiotics. Inhibition of ATP synthase therefore disrupts ATP-dependent survival mechanisms in Mtb. Although this target has been clinically validated by bedaquiline, the emergence of resistance and the limited chemical diversity of reported inhibitors indicate a clear need for new ATP synthase-targeting compounds. In this study, we employed an integrative pipeline combining structure-based pharmacophore modeling, artificial neural network-driven quantitative structure-activity relationship (ANN-QSAR) modeling, and absorption distribution metabolism excretion and toxicity (ADMET)-based pharmacokinetic filtering strategies to screen an antituberculosis-targeted library of approximately 4200 molecules from the Life Chemicals database. Initial screening identified 8 hit molecules characterized by key molecular features previously highlighted as positive contributors in both Shapley Additive Explanations (SHAP) and Pearson correlation analyses, including SubFP1 (primary carbon), SubFP88 (carboxylic acid derivative), SubFP143 (carbonic acid derivative), SubFP9 (alkyl fluoride), SubFP137 (vinylogous ester), SubFP184 (heteroaromatic), SubFP26 (tertiary aliphatic amine), and SubFP171 (aryl chloride). Molecular docking and molecular dynamics simulation studies (200 ns) further highlighted molecules F0526-1306 and F0526-1309 as the most promising candidates. Subsequent antimycobacterial inhibition assays demonstrated that both molecules significantly reduced mycobacterial biofilm formation. In addition, toxicity evaluations using a zebrafish model confirmed the safety and favorable tolerability of these molecules, supporting their potential as viable candidates for further preclinical and in vivo drug development studies.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261438313"},"PeriodicalIF":2.0,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13065274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670333","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":"EpigeneticAgePipeline: An R Package for Comprehensive Assessment of Epigenetic Age Metrics From Methylation Microarrays.","authors":"Stanislav Rayevskiy, Quinn Le, Julia Nguyen, Steven Q Chen, Christina A Castellani","doi":"10.1177/11779322261433665","DOIUrl":"10.1177/11779322261433665","url":null,"abstract":"<p><p>Epigenetic age is a biological age estimate based on nuclear DNA methylation patterns. Epigenetic clocks measure biological age by analyzing predictable changes in DNA methylation sites associated with aging. This study introduces EpigeneticAgePipeline, an R package that streamlines the estimation of epigenetic age metrics including Horvath, Horvath skin and blood, Hannum, PhenoAge/Levine, GrimAge (V1 and V2), and DunedinPACE plus additional acceleration metrics based on all other clocks. Quality control includes detection <i>P</i>-value filtering (sample- and probe-level), bead-count thresholds, and Illumina quality control intensity checks. EpigeneticAgePipeline supports Illumina Infinium methylation microarrays (HumanMethylation27, HumanMethylation450, HumanMethylationEPIC/EPICv2, and Human Methylation Screening Array). It offers functionalities including data preprocessing, normalization, cell count imputation, residual generation accounting for principal components and batch effects, and extensive visualizations for improved interpretability. Validation was performed using GEO data set GSE237561, confirming the accuracy of the pipeline. EpigeneticAgePipeline provides an integrated workflow from raw data to advanced statistical analyses and visualizations, improving usability over existing tools. In addition to the traditional clocks mentioned, the package also integrates a set of additional epigenetic age clocks (PedBE, Wu, TL, BLUP, and EN). Future updates will include emerging epigenetic age measures to maintain relevance in this evolving field.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261433665"},"PeriodicalIF":2.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147589978","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":"ncRNA-Mediated Upregulation of AURKA Promotes Hepatocellular Carcinoma Progression and Alters the Immune Microenvironment.","authors":"Zhitao Chen, Tianshu Chu, Chenchen Ding, Yangjun Gu, Qiyong Li","doi":"10.1177/11779322261435461","DOIUrl":"10.1177/11779322261435461","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide. Aurora kinase A (AURKA), a critical regulator of mitosis and spindle assembly, has been implicated in tumorigenesis, yet its clinical relevance and immune associations in HCC require further clarification. Here, we performed integrated bioinformatic analyses using TCGA/GTEx-derived datasets and public platforms (including TIMER and GEPIA2) to characterize AURKA expression patterns, prognostic significance, and functional pathways in HCC. Potential upstream noncoding RNA (ncRNA) interactions were explored using ENCORI and miRNet 2.0, and immune microenvironment correlations were assessed via TIMER/GEPIA2 and TISIDB. We found that AURKA was significantly overexpressed in HCC and associated with unfavorable prognosis and more advanced clinicopathological features. A putative ncRNA regulatory axis involving DUXAP8 and hsa-miR-490-3p was identified as being associated with AURKA expression. Moreover, AURKA expression correlated with immune cell infiltration and immune-related features, suggesting a relationship with the tumor immune microenvironment. Collectively, our analyses suggest that a putative DUXAP8/hsa-miR-490-3p/AURKA regulatory network may be associated with HCC progression and correlates with features of the tumor immune microenvironment. These findings offer insight into potential biomarkers and therapeutic targets for personalized HCC treatment.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261435461"},"PeriodicalIF":2.0,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13031737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572294","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":"Recent Progress in Sequence Optimization of mRNA Vaccine: Biological Mechanism, Quantitative Metrics, and Computational Model.","authors":"Yunwei Wang, Yuheng Cai, Zhixing Wu, Jingming Zhang, Lance Turtle, Jia Meng","doi":"10.1177/11779322261431255","DOIUrl":"10.1177/11779322261431255","url":null,"abstract":"<p><p>As a groundbreaking advancement in vaccinology, messenger RNA (mRNA) vaccines have transformed the field by offering rapid, flexible, and scalable solutions for combating infectious diseases. However, the efficacy, stability, and immunogenicity of mRNA vaccines are highly dependent on the optimization of their sequences. Recent progress in synthetic biology and computational methods has enabled the optimization of mRNA sequences to enhance their properties, holding the promise to provide deeper insights into the design principles of effective mRNA vaccines. However, it remains a major challenge to determine how to best optimize mRNA sequences for diverse biological contexts and therapeutic applications. In this review, we provide an in-depth analysis of the current advancements in optimizing mRNA vaccine sequences, put forward a comprehensive overview of the latest computational and biological approaches in this field, with a particular focus on the biological mechanisms underlying mRNA translation efficiency and stability, highlighting several quantitative indicators that may affect vaccines' performance, and summarize some methods to optimize mRNA vaccine by algorithms. We also propose the limitations of current models and the need for further research to address the complexity of biological systems.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261431255"},"PeriodicalIF":2.0,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13009642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147509579","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":"Comprehensive Pan-Cancer Analysis Reveals RNF20 as a Candidate Prognostic and Diagnostic Biomarker.","authors":"Jiangbo Liang, Dong Wang, Lei Wang, Yingying Fan, Yiru Wang, Laifeng Ren, Yaoqin Xue","doi":"10.1177/11779322261427126","DOIUrl":"10.1177/11779322261427126","url":null,"abstract":"<p><p>RNF20 is a RING-domain E3 ubiquitin ligase that mediates monoubiquitination of histone H2B at lysine 120 and plays important roles in transcriptional elongation, repair of DNA double-strand breakages, chromatin homeostasis as well as tumor suppression. Even though RNF20 has been investigated in some cancer types, its global biological functions and other related mechanisms in diverse cancers are yet to be fully understood. To investigate the systematical evaluation of the pan-cancer expression pattern of RNF20 and its potential to serve as a diagnostic and prognostic biomarker, we first pulled together the information of several publicly available databases and bioinformatics tools. We compared association between RNF20 expression and important clinical and molecular variables-including clinical survival outcomes, immunomodulatory molecular, tumor stemness index, prognostic use, immunomodulatory molecular, genomic and immunological variables, immunotherapy response, response to chemotherapeutic agents, and functional enrichment profiles. The levels of RNF20 were also substantially greater in tumor tissues relative to identical non-tumor tissues; furthermore, the high levels of RNF20 in some cancer tissues are associated with the good prognostic outcome. The subsequent studies demonstrated that RNF20 is highly correlated with the immune checkpoint molecules, immunomodulatory genes, prognostic biomarkers, tumor stemness scores, as well as immune cell infiltration in various tumors. The SN-38 and talazoparib were identified as possible RNF20-targeting agents through drug susceptibility analysis. It was revealed that missense mutations are most frequent type of variations, which is revealed by functional enrichment analysis, mutation profiling, RNA modification mapping and analysis of genomic heterogeneity of the RNF20 related genes within the framework of glioblastoma, glioma, low-grade glioma in addition to thyroid cancer. Collectively, these results support the exculpatory role of RNF20 in the treatment of different types of cancer, which could help to certify the usage of this biological molecule as a predictive biomarker of treatment response.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261427126"},"PeriodicalIF":2.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12957614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147364382","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":"Integrating AI-Based Clustering, Molecular Dynamics, and Binding Energy Analysis to Elucidate Conformational Dynamics and Binding Selectivity in Extracellular Signal-Regulated Kinases 1 and 2 Complexes.","authors":"Abdullah Alqahtani","doi":"10.1177/11779322261427124","DOIUrl":"10.1177/11779322261427124","url":null,"abstract":"<p><p>The development of selective small-molecule inhibitors against extracellular signal-regulated kinases 1 and 2 (ERK1/2) remains a major challenge due to their high structural similarity and conformational flexibility. In this study, we combined classical molecular dynamics (MD) simulations with artificial intelligence (AI)-based unsupervised learning approaches to investigate the conformational dynamics and binding selectivity of ERK1/2-inhibitor complexes. Six systems, comprising ERK1 and ERK2 bound to inhibitors 33A, 38Z, and Z48, were simulated for 250 ns to capture their structural and energetic behaviors. Root mean square deviation (RMSD) and fluctuation (RMSF) analyses confirmed that all complexes reached equilibrium, with ERK2 systems exhibiting greater structural stability than ERK1. Radius of gyration (Rg) and hydrogen-bond profiles support consistent compactness and persistent interactions throughout the trajectories. The AI-based dimensionality reduction (principal component analysis [PCA], t-distributed stochastic neighbor embedding [t-SNE]) and K-means clustering revealed distinct conformational basins, indicating ligand-specific modulation of ERK flexibility. Free energy landscape mapping and state transition analyses demonstrated that Z48-bound systems showed more compact and stable conformational basins, reflecting enhanced conformational stabilization, whereas 33A complexes exhibited higher dynamic variability. In contrast, molecular mechanics/generalized Born surface area (MM/GBSA) binding free energy calculations showed that 38Z exhibits the strongest binding affinity toward both ERK isoforms, driven primarily by favorable van der Waals interactions. Overall, this combined MD-AI framework provides atomistic insights into ERK1/2 conformational plasticity and inhibitor selectivity, highlighting 38Z as the most energetically favorable binder and Z48 as the most effective conformational stabilizer. These findings advance the understanding of ERK regulation and can guide the rational design of next-generation selective kinase inhibitors.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261427124"},"PeriodicalIF":2.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12957601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147364309","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":"Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance.","authors":"Beatus Lyimo","doi":"10.1177/11779322261427123","DOIUrl":"10.1177/11779322261427123","url":null,"abstract":"<p><p>Africa continues to bear a disproportionate burden of infectious diseases, particularly antimicrobial-resistant (AMR) infections, which significantly affect public health and socio-economic development. Addressing these complex health threats requires innovative approaches to data analysis, pathogen surveillance, and intervention design. The emergence of advanced computational tools especially artificial intelligence (AI) is expected to reduce turnaround times for AMR prediction from days to hours by leveraging whole-genome sequencing (WGS)-based models. This article explores the synergistic integration of AI and bioinformatics, focusing on their application in combating AMR in Africa. It details how AI techniques, particularly machine learning (ML) and deep learning (DL) algorithms, can enhance genomic research by automating the analysis of large-scale sequence datasets, predicting resistance patterns, and modeling infections transmission dynamics. In regions with limited laboratory capacity, AI models can detect resistance genes rapidly and assist clinicians in selecting appropriate antibiotics, offering a faster and more scalable alternative to traditional diagnostics. Tools such as convolutional neural networks (CNNs) and support vector machines (SVMs) are examples of models capable of classifying pathogen strains based on genetic data. Furthermore, the article highlights the emerging role of large language models (LLMs) in supporting bioinformatics workflows. These tools aid researchers by generating analysis scripts, interpreting complex outputs, troubleshooting code errors, summarizing literature, and preparing manuscripts or grant proposals particularly benefiting early-career scientists who may lack access to advanced training or mentorship. Despite notable progress, significant challenges remain, including limited infrastructure, barriers to data sharing, and the urgent need for ethical guidelines and policies to govern AI integration. Ultimately, this article underscores the transformative potential of AI in advancing bioinformatics across Africa and advocates for sustained investment in infrastructure, capacity-building, and responsible policy frameworks to harness AI for improved health research and disease control outcomes. We propose 3 priority actions: building African AMR genomic datasets, investing in AI-ready infrastructure, and developing responsible data-governance frameworks.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261427123"},"PeriodicalIF":2.0,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12953961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147353761","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":"Computational and Experimental Characterization of <i>Mycobacterium marinum</i> β-Carbonic Anhydrase Inhibitors.","authors":"Niaz Morshed, Md Selim Reza, Ratul Bhowmik, Ashok Aspatwar","doi":"10.1177/11779322261427120","DOIUrl":"10.1177/11779322261427120","url":null,"abstract":"<p><p>Carbonic anhydrases in <i>Mycobacterium tuberculosis</i> are increasingly recognized as promising therapeutic targets in drug-resistant tuberculosis. In this study, a homology model of β-carbonic anhydrase was developed using the closely related <i>Mycobacterium marinum</i> sequence as a structural basis. A focused antituberculosis compound library was screened, identifying 2 ligands, F2686-0257 and F1011-1367, with strong binding affinities and distinct interaction patterns. Molecular dynamics simulations more than 100 ns confirmed stable backbones and conserved binding pockets, with F2686-0257 stabilized by aromatic anchoring and F1011-1367 by polar interactions. Structure-activity relationship analysis highlighted rigid aromatic scaffolds, controlled molecular size, and balanced polarity as favorable features. In <i>M marinum</i> growth assays, F2686-0257 inhibited bacterial proliferation at 100 µM and enhanced rifampicin activity, whereas F1011-1367 showed weaker inhibition without synergy. The compounds also showed favorable ADMET and drug-likeliness properties. These results support β-carbonic anhydrase as a viable target and provide scaffolds for the rational development of novel antitubercular agents.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322261427120"},"PeriodicalIF":2.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12949308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147324603","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":"Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.","authors":"Rudolph Abel Serage, Clement Nthambazale Nyirenda, Taiwo Gabriel Omomule, Alan Gilbert Christoffels, Dominique Elizabeth Anderson","doi":"10.1177/11779322251415123","DOIUrl":"10.1177/11779322251415123","url":null,"abstract":"<p><p>Biomedical science has made substantial progress toward diagnosing, understanding the pathogenesis, and treating various causative agents of infectious disease. However, novel microbial pathogens continue to emerge, and existing pathogens continue to evolve alternative strategies to thrive in ever-changing environments. Various infectious disease etiological agents originate from animal reservoirs, and several have, over time, acquired the ability to cross the species barrier, altering their host range. Computational approaches in biomedical science capable of analyzing large datasets are invaluable for predicting and monitoring disease outbreaks and their effectiveness is greatly enhanced when integrated with machine learning techniques. The goal of this study is to develop a machine learning model for the prediction of potentially zoonotic organisms, using viral surface proteins that facilitate host cell entry as input data. Sequence data and metadata were obtained from UniProtKB, transformed into a machine-readable format, using frequency chaos game representation and a convolutional neural network model was developed to identify sequence patterns consistent with viruses which infect humans. The model achieves generalized performance of 96.78% accuracy, 0.97 F1 score, and 0.93 MCC (Matthews Correlation Coefficient) on unseen data. The model potentially provides a robust framework for application in early identification of emerging viral threats, supporting public health surveillance and risk mitigation.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322251415123"},"PeriodicalIF":2.0,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12936363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147324630","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":"PCNE: A Tool for Plasmid Copy Number Estimation.","authors":"Riccardo Bollini, Valeria Cento","doi":"10.1177/11779322251410037","DOIUrl":"10.1177/11779322251410037","url":null,"abstract":"<p><p>The identification of plasmids from assembled genomes is well supported by numerous different tools, yet very few incorporate a plasmid copy number estimation step. This limits a comprehensive plasmid analysis, often leaving researchers to perform copy number estimation independently, leading to a lack of standardization. Plasmid Copy Number Estimator (PCNE) addresses this by providing an accessible and versatile command-line tool for estimating plasmid copy numbers directly from short-read sequencing data. Starting from standard input data like raw reads and a genome assembly, PCNE allows to apply a flexible normalization strategy, including an optional GC-bias correction, and is designed to complement existing plasmid detection pipelines. By simplifying and standardizing copy number estimation, PCNE, through the integration of state-of-art methodologies, aims to empower researchers to gain deeper insights into plasmid biology, particularly in studies of antimicrobial resistance and horizontal gene transfer.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"20 ","pages":"11779322251410037"},"PeriodicalIF":2.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12929872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147302160","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}