Frontiers in bioinformatics最新文献

{"title":"Entropy-driven machine learning for deciphering mRNA rearrangement by splicing in complex microorganisms.","authors":"Alessio Mancini, Emanuela Merelli, Marco Piangerelli, Sandra Pucciarelli, Leonardo Vito","doi":"10.3389/fbinf.2026.1889469","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1889469","url":null,"abstract":"<p><strong>Introduction: </strong>Alternative splicing allows a single gene to produce multiple messenger RNA (mRNA) variants by differential RNA processing, resulting in the translation of distinct protein isoforms. Intron retention (IR) is a specific type of alternative splicing in which introns remain unspliced in the mature mRNA. The exact regulatory code behind intron splicing remains not fully deciphered, and unraveling these mechanisms is critical to uncovering the foundations of genetic disorders: a substantial fraction of disease-causing mutations disrupt intron splicing.</p><p><strong>Methods: </strong>We applied explainable machine learning (xML) models to intronic sequences from four species of the ciliate genus Tetrahymena (<i>T. thermophila</i>, <i>T. malaccensis</i>, <i>T. borealis</i> and <i>T. elliotti</i>), which allow the intron retention process to be analysed without tissue-specific confounders. Retained introns (RIs) and constitutively spliced introns (CSIs) were extracted through an automated Galaxy pipeline and described by several features of the intronic sequences, including the absence of repetitive nucleotide motifs - quantified as entropy - the GC content, the complexity of the secondary structures as estimated by the Lempel-Ziv (LZ) measure, and the effective distance between the branch point and the 3' splice site. Four classifiers were compared by 10-fold cross-validation within the training set and evaluated on an independent test set.</p><p><strong>Results: </strong>Across 289.6 k analysed introns, the C5.0 decision tree gave the best and most consistent performance, ranking first in three of the four species (accuracy 0.948-0.984). The key distinguishing features of retained introns were a higher entropy (Shannon entropy above 1.67, versus below 1.53 for constitutively spliced introns, against a theoretical maximum of 2) and more complex secondary structures near the 3' splice sites, together with a greater effective branch-point-to-3'-splice-site distance and a relative enrichment of the GC dinucleotide.</p><p><strong>Discussion: </strong>These features converge on weaker, less accessible splicing signals, which may impair splice-site recognition and bias the outcome towards retention. Our work identifies key sequence-level features predictive of intron retention in Tetrahymena and could provide a generalizable, explainable ML framework for investigating splicing regulation in other eukaryotes.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1889469"},"PeriodicalIF":3.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119524","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":"Multi-cohort machine learning identifies a ferroptosis-linked prognostic signature in lung adenocarcinoma.","authors":"Rana Salihoglu","doi":"10.3389/fbinf.2026.1921468","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1921468","url":null,"abstract":"<p><strong>Introduction: </strong>Lung adenocarcinoma (LUAD) shows marked outcome heterogeneity within clinicopathological stage groups. This study developed and externally validated a ferroptosis-linked transcriptomic risk model using a leakage-controlled multi- cohort survival-learning framework and characterized its immune context.</p><p><strong>Methods: </strong>Candidate genes were defined by combining FerrDb V3-annotated genes within a ferroptosis-associated weighted gene co-expression network analysis (WGCNA) module with a filtered WGCNA discovery branch derived from the independent GSE81089 cohort. Model development used TCGA-LUAD, GSE31210, GSE72094, and GSE136961 (1,149 patients; 344 overall-survival events) with bagged cross-cohort Cox screening, leave-one-cohort-out (LOCO) stability locking, and benchmarking of 150 survival-learning configurations. External evaluation used the GSE50081, GSE68465, and GSE30219 cohorts (912 patients; 507 events).</p><p><strong>Results: </strong>The development-selected extra survival trees configuration reached a mean leave-one-cohort-out Uno's C-index of 0.723. The highest cohort-specific C-indices among the prespecified candidate configurations were 0.611, 0.691, and 0.699 and arose from different model configurations. Applying the same development-selected configuration to all three external cohorts yielded Harrell's C-indices of 0.605, 0.675, and 0.682 and 5-year Uno's C-indices of 0.605, 0.678, and 0.696. In TCGA-LUAD, the stored out-of-fold molecular score remained prognostic after TNM stage adjustment (hazard ratio per standard deviation 2.32, 95% confidence interval: 1.39-3.87; p = 0.0013).</p><p><strong>Discussion: </strong>At 3 years and 5 years, the combined TNM-plus-score models showed close calibration and modest gains in discrimination, while decision-curve analysis identified positive incremental net benefit only over restricted threshold ranges. Low-risk tumors were enriched for interferon, complement, and immune-cell programs. These computational findings require further prospective assay-level and experimental validation before clinical implementation.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1921468"},"PeriodicalIF":3.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119195","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":"Rational discovery of peptide inhibitors targeting Keap1: a focus on neurodegenerative diseases.","authors":"Nandhini Senthilkumar, Shanthi Veerappapillai","doi":"10.3389/fbinf.2026.1929781","DOIUrl":"10.3389/fbinf.2026.1929781","url":null,"abstract":"<p><strong>Introduction: </strong>The Kelch-like ECH-associated protein 1 (Keap1)/Nuclear factor erythroid 2-related factor 2 (Nrf2) pathway is a promising therapeutic target for neurodegenerative diseases (NDDs). Nrf2 activation mitigates key pathogenic mechanisms in NDDs by enhancing antioxidant defenses, suppressing neuroinflammation, improving mitochondrial function, inhibiting ferroptosis, and maintaining proteostasis.</p><p><strong>Aim: </strong>To design and identify peptide inhibitors capable of disrupting the Keap1/Nrf2 interaction through a rational computational approach.</p><p><strong>Methods: </strong>A combinatorial peptide library comprising 13,167 peptides was generated from an Nrf2-derived template peptide and subjected to rigorous multi-tier screening based on predicted blood-brain barrier permeability, toxicity, allergenicity, antioxidant and anti-inflammatory potential, and physicochemical properties. Following comprehensive safety assessments, 11 promising peptides were shortlisted and evaluated using molecular docking, MM-GBSA binding free-energy calculations, and per-residue energy decomposition analysis. The top-ranked peptide candidates were subsequently subjected to 200 ns molecular dynamics (MD) simulations to assess complex stability and binding behavior through analyses of structural stability, flexibility, principal component analysis (PCA), hydrogen-bond occupancy, and free-energy landscape analyses.</p><p><strong>Results: </strong>Among the shortlisted peptides, LMEETYEFL (pep4369) and LDYETYEFL (pep6649) exhibited stronger binding affinities toward Keap1 than the template peptide in docking and MM-GBSA analyses. MD simulations confirmed the structural stability and favorable conformational behavior of the peptide-Keap1 complexes throughout the simulation period.</p><p><strong>Conclusion: </strong>These findings suggest that LMEETYEFL and LDYETYEFL are promising candidates for disrupting the Keap1/Nrf2 interaction. However, experimental validation is necessary to confirm their therapeutic potential and facilitate further biological evaluation.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1929781"},"PeriodicalIF":3.6,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083166","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":"Correction: A CSGNN model-based method for essential protein identification.","authors":"Zixuan Li, Zhiguo Yu, Peng Li","doi":"10.3389/fbinf.2026.1986024","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1986024","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fbinf.2026.1731178.].</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1986024"},"PeriodicalIF":3.6,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076848","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":"Comparative functional genomic analysis of table olive-associated <i>Lactiplantibacillus plantarum</i> strains.","authors":"Elio López-García, Antonio Benítez-Cabello, Efstathios Z Panagou, José Luis Ruiz-Barba, Francisco Noé Arroyo-López","doi":"10.3389/fbinf.2026.1936258","DOIUrl":"10.3389/fbinf.2026.1936258","url":null,"abstract":"<p><p>Table olives are among the most important fermented vegetables in the Mediterranean region, with fermentations largely driven by <i>Lactiplantibacillus</i> species. While <i>Lactiplantibacillus pentosus</i> has been extensively studied at the genomic level, genomic information on olive-associated <i>Lactiplantibacillus plantarum</i> remains scarce. To expand current knowledge, three novel <i>L. plantarum</i> strains isolated from table olive fermentations in Spain (Lpl15 and LPT703) and Greece (B282) were subjected to whole-genome sequencing and comparative genomic analysis. Genome assemblies ranged from 3.25 to 3.50 Mb and contained 3,060-3,328 predicted protein-coding genes. While Illumina assemblies remained fragmented, hybrid sequencing produced a complete genome assembly for strain <i>L. plantarum</i> Lpl15, including a complete chromosome and three plasmids. Comparative analyses incorporating <i>L. plantarum</i> genomes from diverse ecological origins, including vegetable fermentations, animal-derived foods, and human-associated environments, were performed using Average Nucleotide Identity (ANI), pangenome analysis, and core-genome phylogeny. Neither ANI nor phylogenomic analyses supported the existence of a distinct lineage associated with table olive fermentations. Functional and metabolic comparisons revealed two major genomic groups linked to ecological origin. Genomes predominantly associated with vegetable fermentations lacked complete nitrogen metabolism pathways, whereas strains of animal origin generally retained these functions. In addition, vegetable-associated strains showed an enrichment of genes involved in exopolysaccharide biosynthesis. Analyses of protein families, domains, and mobilome-related elements further revealed niche-associated genomic patterns. Overall, these results indicate that <i>L. plantarum</i> from table olive fermentations does not constitute a distinct evolutionary lineage. Instead, adaptation appears to be reflected in functional and accessory genomic traits associated with ecological origin, providing new insights into the diversity and adaptive potential of this species across different environments.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1936258"},"PeriodicalIF":3.6,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076834","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":"The illusion of meritocracy: addressing systemic gender gaps in bioinformatics.","authors":"Aldana A Cepeda Dean, Natalia Rego, Luisa Berná","doi":"10.3389/fbinf.2026.1891103","DOIUrl":"10.3389/fbinf.2026.1891103","url":null,"abstract":"<p><p>Despite the widespread perception of computational sciences as objective and merit-based fields, bioinformatics continues to exhibit persistent gender inequalities. This opinion article examines how structural disparities manifest through phenomena such as the citation gap, the Matilda Effect, and the \"leaky pipeline,\" as well as through sociocultural barriers that shape career progression and scientific recognition. We also discuss the risk of gender bias being reproduced through computational tools, datasets, and algorithmic frameworks developed within the field itself. We argue that sustained institutional and community-level intervention, rather than individual effort, is the only viable path toward a truly equitable scientific community.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1891103"},"PeriodicalIF":3.6,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066486","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":"Ten key considerations for developing interpretable risk prediction models.","authors":"Xi Zhang, Wenjie Xu, David R Smith","doi":"10.3389/fbinf.2026.1878681","DOIUrl":"10.3389/fbinf.2026.1878681","url":null,"abstract":"","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1878681"},"PeriodicalIF":3.6,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066544","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":"Evaluation of functional annotation-informed and ancestry-specific polygenic risk scores for ischemic stroke.","authors":"Nicole D Armstrong, Vinodh Srinivasasainagendra, Amit Patki, Lavanya Pilla, Chad Aldridge, Alana C Jones, Ulrich Broeckel, Ethan M Lange, Leslie A Lange, Pankaj Arora, Nita A Limdi, Hirotaka Iwaki, Lana Sargent, Joohyun Kim, Maggie C Y Ng, Josep M Mercader, Keith L Keene, Bradford B Worrall, Hemant K Tiwari, Marguerite R Irvin","doi":"10.3389/fbinf.2026.1875388","DOIUrl":"10.3389/fbinf.2026.1875388","url":null,"abstract":"<p><strong>Introduction: </strong>Ischemic stroke (IS) is a leading cause of morbidity and mortality, and predicting events remains challenging. Polygenic risk scores (PRSs) aggregate genetic variants, but performance varies across ancestries and remains modest, particularly in non-European populations.</p><p><strong>Methods: </strong>We compared Bayesian PRS methods incorporating functional annotations and expanded linkage disequilibrium reference panels for IS prediction. PRSs were generated using PRS-CS with HapMap3 and TagIt panels, and SBayesRC integrating functional priors. Scores were optimized in 1,403 European ancestry (EA) and 6,342 African ancestry (AA) participants from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study and independently validated in All of Us participants (78,155 EA and 29,594 AA participants), a REGARDS holdout cohort (11,459 EA participants), and the Genetics of Hypertension Associated Treatments (GenHAT) study (6,908 AA participants). Predictive performance was evaluated using logistic regression adjusted for age, sex, the top 10 genetic principal components, and antihypertensive treatment assignment where applicable. Performance was assessed using odds ratios per standard deviation (OR/SD), liability-scale R², and area under the curve (AUC).</p><p><strong>Results: </strong>Among EA participants, SBayesRC demonstrated the largest effect size and liability R² in the REGARDS holdout validation cohort (OR/SD = 1.24, 95% CI 1.12-1.36, R² = 2.42%). PRS-CS using the expanded TagIt panel (OR/SD = 1.11, 95% CI 1.00-1.22) showed numerically stronger performance than the HapMap3 panel (OR/SD = 1.09, 95% CI 0.98-1.20). Performance was consistent across the All of Us EA cohort, with OR/SD ranging from 1.10 to 1.16 across methods. Among AA participants, associations and predictive metrics were attenuated, with no significant associations observed in All of Us. A modest association was observed for PRS-CS-TagIt in GenHAT (for an all-stroke outcome). PRSs modestly improved discrimination beyond covariate models among EA participants (AUCs up to 66.6%), but showed little to no improvement among AA participants (AUCs 62.7%-66.7%). High PRS percentile groups (e.g., top 2% versus 98%) showed modest stroke enrichment with high specificity and limited sensitivity.</p><p><strong>Conclusion: </strong>Functional annotation-informed PRSs and expanded reference panels modestly improved prediction, particularly among EA participants. Limited performance among AA participants underscores the need for larger, more diverse genomic datasets and integrative models incorporating genetic, clinical, lifestyle, and social determinants of health.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1875388"},"PeriodicalIF":3.6,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13601841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042372","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":"Datamonkey 3: browser-native molecular evolution analysis.","authors":"Steven Weaver, Ben Murrell, Anton Nekrutenko, Sergei L Kosakovsky Pond","doi":"10.3389/fbinf.2026.1876305","DOIUrl":"10.3389/fbinf.2026.1876305","url":null,"abstract":"<p><p>We present Datamonkey 3, a browser-native implementation of the Datamonkey web platform for molecular evolutionary analysis. By utilizing WebAssembly to execute the HyPhy analysis engine on the client side, Datamonkey 3 removes the dependency on remote computational clusters for standard analyses. This architecture ensures data sovereignty by processing sequences locally and enables an interactive workflow with immediate feedback. The platform provides a comprehensive suite of statistical methods for detecting natural selection and recombination, supported by a data curation system that identifies and corrects common formatting errors. Additionally, Datamonkey 3 incorporates data-driven runtime estimation and interactive results visualization. By shifting the computational burden to the client, Datamonkey 3 establishes a sustainable, privacy-preserving infrastructure model that scales with the user base, demonstrating the viability of client-side genomic inference. While tailored for evolutionary analysis of selection and recombination, this browser-native architecture offers a generalizable blueprint for deploying complex bioinformatic tools without server-side dependencies. Datamonkey 3 is freely available at https://v3.datamonkey.org, and all source code is available from https://github.com/veg/datamonkey3.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1876305"},"PeriodicalIF":3.6,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042355","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":"Correction: Network-based integrative analysis of multi-level regulatory mechanisms associated with glyphosate exposure.","authors":"Claudia Consuegra-Mayor, Barbara Arroyo-Salgado, Jesus Olivero-Verbel","doi":"10.3389/fbinf.2026.1979796","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1979796","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fbinf.2026.1890219.].</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1979796"},"PeriodicalIF":3.6,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13599721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042390","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}