Frontiers in bioinformatics最新文献

{"title":"How benchmarking of bioinformatics tools is essential for informed workflow selection: a case study on SARS-CoV-2 subgenomic RNA detection.","authors":"Gabriele Leoni, Mauro Petrillo, Man-Hung Eric Tang, Soren Alexandersen","doi":"10.3389/fbinf.2026.1803237","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1803237","url":null,"abstract":"<p><strong>Introduction: </strong>Selecting appropriate bioinformatics tools is critical for accurate and reproducible analysis, particularly in support of genomic surveillance and molecular biomarker monitoring. The importance of these analyses is underscored by the need for effective public health responses to emerging diseases like SARS-CoV-2.</p><p><strong>Methods: </strong>By using the detection of SARS-CoV-2 subgenomic RNAs (sgRNAs) as a case study, we show the importance of systematic benchmarking in selecting optimal workflows. We generated 25 synthetic Illumina datasets simulating both shotgun and amplicon sequencing strategies, along with a real-world wastewater dataset. Using these datasets, we assessed the influence of key variables including mutation profiles, read lengths, aligner choice, and primer design for targeted sequencing.</p><p><strong>Results: </strong>Our results revealed substantial performance variability: common tools developed to identify sgRNAs struggled with shotgun data and were sensitive to mutations depending on the chosen aligner, while amplicon sequencing improved detection sensitivity, with aligners and primer design choices still significantly impacting outcomes.</p><p><strong>Discussion: </strong>Our results highlight the need for benchmarking steps and analyses to inform workflow selection. Without such evaluations, researchers risk drawing inaccurate conclusions from suboptimal workflows. This case study underscores the value of context-aware tool selection and encourages standardised benchmarking practices to ensure reproducibility and reliability in bioinformatics analysis, particularly in evidence-based decision-making environments such as public health and policymaking.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1803237"},"PeriodicalIF":3.9,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13144104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846937","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":"A machine learning-derived genomic dataset from bacteria frequently reported as probiotics.","authors":"Diego Lucas Neres Rodrigues, Pedro Alexandre Sodrzeieski, Sandrine Auger, Jean-Marc Chatel, Ana Maria Benko-Iseppon, Vasco Azevedo, Siomar de Castro Soares, Flávia Figueira Aburjaile","doi":"10.3389/fbinf.2026.1810235","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1810235","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1810235"},"PeriodicalIF":3.9,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13144117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846914","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":"An automated cell-tracking pipeline for the analysis of neutrophil dynamics.","authors":"Chen Li, Wilson W C Yiu, Wanbin Hu, Herman P Spaink, Lu Cao, Fons J Verbeek","doi":"10.3389/fbinf.2026.1748364","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1748364","url":null,"abstract":"<p><p>Neutrophils play a key role in the innate immune system. They act as the primary line of defense when bacteria, viruses, or other harmful foreign particles invade the immune system. Accurate movement measurement of neutrophils, including velocity, direction, and displacement, is crucial to studying the regulation of cell migration behavior. Cell tracking is a key technology to realize the quantification of these measurements. In this article, we developed a pipeline, including cell segmentation, cell motion tracking between two frames, and trajectory linkage, to realize cell tracking. Our starting point was to collect time-lapse sequences of neutrophils using a confocal microscope. We pre-processed each frame in the time-lapse sequence to improve the image quality by denoising, smoothing, and contrast enhancement. Subsequently, a deep learning model, that is, U-Net, was used to segment cells in each image frame. U-Net was used again to track the cells between two adjacent frames by calculating the score matrices representing the posterior probability of linkage. Moreover, an extended Viterbi algorithm was applied to find optimal trajectories based on score matrices generated by the U-Net. Results demonstrate that our pipeline outperforms other representative linkage methods used in cell tracking. It provides a robust, practical solution for a challenging and highly motile <i>in vivo</i> regime.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1748364"},"PeriodicalIF":3.9,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13139118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846894","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":"Feature representation for explainable CRISPR off-target prediction and base editing efficiency.","authors":"Faiza Hasin, Michele Minervini, Corrado Mencar, Giuseppe Ventrella, Arianna Consiglio, Alessandro Orro, Tommaso Selmi","doi":"10.3389/fbinf.2026.1800237","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1800237","url":null,"abstract":"<p><strong>Introduction: </strong>The interaction between guide RNAs (gRNAs) and target DNA sequences is a critical factor in the effectiveness of CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) gene editing. Predicting these interactions accurately necessitates models that offer biological knowledge in addition to high accuracy. This study analyzes the impact of feature representation on accuracy and interpretability in off-target prediction.</p><p><strong>Methods: </strong>We address two CRISPR applications: gene knockout (KO) and base editing (BE) using distinct benchmark datasets. For the KO problem, we utilized CHANGE-seq and GUIDE-seq to evaluate paired sequence representations, while the Hanna screening dataset has been used for BE. We approached the prediction problem both as a classification and regression task using XGBoost models.</p><p><strong>Results: </strong>In the case of KO, there is not a single universally optimal encoding. For both classification and regression, One-Hot and its variants (OH, OH5C) achieve the best results on GUIDE-seq (AUPR = 0.661, Pearson = 0.756), while the Bulges representation performs best on CHANGE-seq (AUPR = 0.612, Pearson = 0.602). In the case of BE, One-hot encoding consistently outperforms K-mer representation for predictive accuracy both as regression and classification (AUPR = 0.723, Pearson = 0.746).</p><p><strong>Discussion: </strong>Our analysis demonstrates comparable predictive performance across both gene knockout and base editing tasks, confirming the robustness of the framework in distinct editing domains. Interpretability analysis using SHapley Additive exPlanations (SHAP) reveals that despite different mechanisms, the Protospacer Adjacent Motif (PAM)-proximal region remains a critical feature for prediction for both editing mechanisms.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1800237"},"PeriodicalIF":3.9,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846874","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":"Geometric multidimensional representation of omic signatures.","authors":"Higor Almeida Cordeiro Nogueira, Enrique Medina-Acosta","doi":"10.3389/fbinf.2026.1806975","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1806975","url":null,"abstract":"<p><strong>Introduction: </strong>Multi-omic signatures are widely used in biomarker discovery, precision oncology, and systems biology, yet they are typically treated as vectors or composite scores that collapse intrinsically multidimensional biological organization into one-dimensional summaries. As a result, their internal structure, contextual dependencies, and functional coherence remain largely inaccessible.</p><p><strong>Methods: </strong>Here, we introduce a geometric framework that reconceptualizes omic signatures as multidimensional informational entities whose biological meaning arises from structural organization rather than molecular membership alone. Each signature is embedded in a shared latent space integrating regulatory, phenotypic, microenvironmental, immune, and clinical constraints, and represented as a convex polytope. This representation preserves internal organization and enables intrinsic geometric measurements-including barycenter distance, volume, anisotropy, and asymmetry-that quantify concordance, divergence, and latent complexity. We applied this framework to 24,796 metabolic regulatory circuitries reconstructed across 32 TCGA cancer types, encoded as paired regulatory and metabolic signatures in an 18-dimensional latent space.</p><p><strong>Results: </strong>Geometric analysis shows that discordance predominates: most circuitries occupy strong or extreme discordance regimes and display high-dimensional, frequently asymmetric geometries, whereas fully concordant circuitries are rare and structurally constrained. These geometric phenotypes stratify metabolic pathways and superfamilies in reproducible, non-uniform patterns that are not readily captured by conventional vector- or network-based representations.</p><p><strong>Discussion: </strong>By transforming omic signatures into measurable geometric objects, this framework provides a principled approach for the comparison and de-redundancy of multi-omic biomarkers, providing a scalable method for analyzing complex regulatory systems across cancer and beyond. All geometric representations and derived descriptors are available through the SigPolytope Shiny application (https://sigpolytope.shinyapps.io/geometricatlas/).</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1806975"},"PeriodicalIF":3.9,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13133092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823847","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":"Diversity and evolution of quorum-sensing systems in <i>Rhizobium</i>.","authors":"Ivana Blancas-Nava, Erick Cruz-Santiago, Gabriela Guerrero, Rosa-Maria Gutierrez-Rios, Miguel A Cevallos","doi":"10.3389/fbinf.2026.1767204","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1767204","url":null,"abstract":"<p><p>Quorum-sensing (QS) systems based on acyl-homoserine lactones (AHLs) regulate gene expression in response to cell density in many bacteria, including <i>Rhizobium</i>. 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 <i>Rhizobium</i> remain poorly understood. We analyzed 142 complete <i>Rhizobium</i> 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 <i>Rhizobium</i> 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 <i>Rhizobium</i> 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.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1767204"},"PeriodicalIF":3.9,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13133039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824412","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: Protein embeddings reveal a continuous molecular landscape of host adaptation in waterfowl parvoviruses.","authors":"Nihui Shao, Yunfei Guo","doi":"10.3389/fbinf.2026.1839097","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1839097","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fbinf.2025.1738737.].</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1839097"},"PeriodicalIF":3.9,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13130047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824347","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":"SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial transcriptomics.","authors":"Jonathan Xu, Michelle Jiang, Shunsuke Koga, Nancy Zhang, Zhi Huang","doi":"10.3389/fbinf.2026.1746714","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1746714","url":null,"abstract":"<p><p>Sequencing an entire spatial transcriptomics slide can cost thousands of dollars per assay, making routine use impractical. Focusing on smaller regions of interest (ROIs) based on adjacent H&E slides offers a practical alternative, but there is (i) no reliable way to identify the most informative areas from standard H&E images alone; and (ii) limited solutions for clinicians to prioritize the microenvironment of their own interests. Here we introduce SpatialFinder, a framework that combines a biomedical vision-language model (VLM) with a human-in-the-loop optimization pipeline to predict gene expression heterogeneity and rank high-value ROIs across routine H&E tissue slides. Evaluated across four Visium HD tissue types, SpatialFinder consistently outperforms VLM-only baselines for both diversity- and tumor-targeted ROI ranking, achieving Spearman's <math><mrow><mi>ρ</mi></mrow> </math> up to 0.89 and Overlap@10% up to 78.8%, an absolute 24.9 percentage-point gain over the strongest VLM. These results demonstrate the potential of human-AI collaboration to make spatial transcriptomics more cost-effective and clinically actionable.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1746714"},"PeriodicalIF":3.9,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13124733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823859","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":"Self-organizing maps for allele specific expression data reconstruction and identification of anomalous genomic regions.","authors":"Roberto Pagliarini, Francesco Nascimben, Alberto Policriti","doi":"10.3389/fbinf.2026.1810835","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1810835","url":null,"abstract":"<p><p>Allele Specific Expression data quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites. Current methodologies of genome-wide sequencing produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Machine learning tools could be employed to explore the data and to estimate missing signatures. We present a two-phase procedure based on Self-Organizing Maps (SOMs), an unsupervised clustering technique, to recover missing allele specific expression data from RNA-seq experiments. Specifically, a SOM trained on a complete population <math><mrow><mi>P</mi></mrow> </math> is used to assign a so-called corrupted individual <math> <mrow> <mover><mrow><mi>p</mi></mrow> <mo>^</mo></mover> </mrow> </math> to its most fitting cluster <math><mrow><mi>c</mi></mrow> </math> ; then, a completion rule based on allele frequencies within the subpopulation of <math> <mrow> <msub><mrow><mi>P</mi></mrow> <mrow><mi>c</mi></mrow> </msub> <mo>⊆</mo> <mi>P</mi></mrow> </math> defined by <math><mrow><mi>c</mi></mrow> </math> is employed to reconstruct <math> <mrow> <mover><mrow><mi>p</mi></mrow> <mo>^</mo></mover> </mrow> </math> . To evaluate our approach, we first apply it to purely artificial datasets, in order to have full control over all experimental conditions. After that, we consider a real population of <i>Vitis vinifera</i>, which we also extend by applying a computational framework to generate synthetic individuals from allele expression data. We then introduce two local feature relevance indices in order to assess the influence of specific alleles on the topological placement of corrupted individuals in the SOM structure. Our results, showing promising accuracy in the prediction of missing alleles, suggest that the developed approach could be very useful for recovering incomplete samples in a dataset instead of discarding them, mainly in situations where experiments are challenging.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1810835"},"PeriodicalIF":3.9,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13121152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790942","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":"Resolving heterogeneity in Lymph Node Stromal Cells using high-dimensional analysis of non-optimized flow cytometry data.","authors":"Mikala E Heon, Eduardo Rosa-Molinar","doi":"10.3389/fbinf.2026.1657030","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1657030","url":null,"abstract":"<p><p>Lymph Node Stromal Cells (LNSCs) are a diverse population of cells responsible for maintaining the lymph node environment and regulating the immune response. Given these roles, they have the potential to help replicate lymph node functions <i>invitro</i>. However, LNSCs are challenging to work with due to their high heterogeneity. Here, we demonstrate the challenges of working with heterogeneous cell populations, where ratios between populations can change over time. We show how similar marker expression profiles between populations, along with non-optimized controls due to experimental limitations, can make flow cytometry analysis difficult. To better assess this heterogeneous population, we demonstrate how to use machine learning algorithms to identify changing populations while overcoming the limitations of any single algorithm. This approach reduces the effects of user bias when placing gates while also increasing confidence in population identification. This analysis method is robust, utilizes existing tools, and provides information that can inform various directions of future studies.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1657030"},"PeriodicalIF":3.9,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13121901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790936","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}