Briefings in bioinformatics最新文献

{"title":"Ribo-seq rRNA reads reveal co-predicted signatures of ribosome heterogeneity and collisions.","authors":"Edwin S Kyei-Baffour, William J Faller, Ferhat Alkan","doi":"10.1093/bib/bbag575","DOIUrl":"10.1093/bib/bbag575","url":null,"abstract":"<p><p>Ribosome heterogeneity is increasingly recognized as a key factor in various biological contexts, including development, cancer, and immune responses. To enable systematic exploration of this diversity, we previously developed a bioinformatics method that utilized rRNA reads from Ribo-seq data, detecting changes in ribosomal protein composition (dripARF) and ribosome collisions (dricARF) between conditions simply and efficiently. The method works by projecting differences in rRNA read abundance onto the 3D-structure of the ribosome, following which we perform enrichment tests on predefined heterogeneity-specific rRNA position sets. In this study, we apply this tool to publicly available Ribo-seq datasets to understand the impact of experimental protocol variations and to identify shared biological signals across studies. Our analyses show that the rRNA regions assigned to our prediction sets are consistently enriched in rRNA reads, supporting the core assumptions of our approach. We further examine how various protocol factors influence rRNA read distribution and implement a single-sample rRNA position set enrichment analysis-based weight to limit false positives. Finally, we highlight consistent ribosomal changes across these studies, including prominent co-predictors such as ribosome collisions, eS25, and uS19.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in computational biology approaches for digital modeling of tumor microenvironment.","authors":"Wenlong Ming, Huimin Lu, Tianhao Zeng, Kun Luo, Jun Xu, Hongde Liu","doi":"10.1093/bib/bbag595","DOIUrl":"https://doi.org/10.1093/bib/bbag595","url":null,"abstract":"<p><p>Tumor progression arises from tightly coupled cellular, vascular, metabolic, immune, and biomechanical processes within the tumor microenvironment (TME), forming a heterogeneous multiscale system that cannot be fully characterized through experimental observation alone. Computational modeling provides a quantitative framework for integrating these mechanisms, but the diversity of available methods complicates model selection, parameterization, validation, and clinical translation. This review presents a problem-driven synthesis of computational approaches for TME modeling. Continuous, discrete, and discrete-continuous hybrid paradigms are compared in terms of biological resolution, data requirements, interpretability, and computational cost, and are linked to five mechanistic domains: tumor-cell dynamics, vascular transport, extracellular matrix and mechanics, metabolism and hypoxia, and immune interactions. Model credibility is examined through parameter identifiability, uncertainty quantification, numerical validity, biological fidelity, and clinical relevance, with emphasis on interpreting performance according to intended use and validation context. Machine learning is analyzed by function within mechanistic-ML integration: surrogate models accelerate forward simulation, inverse methods estimate latent parameters and states, patient-specific workflows combine multimodal clinical data with mechanistic models, and physics-informed neural networks embed governing equations into learning. We further discuss longitudinal personalization, physics-aware multiscale integration, clinically deployable digital twins, external generalization, and synthetic-data-to-clinical transfer. A translation-readiness pathway is proposed from mechanistic hypothesis generation to adaptive digital-twin decision support. Credible TME modeling should prioritize the minimum sufficient model, explicit intended use, transparent uncertainty, and evidence matched to the biological or clinical question.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149285264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene-Chronos: parameter-efficient developmental time inference using a pretrained single-cell foundation model.","authors":"Yinbo Liu, Handi Gao, Tian Tian","doi":"10.1093/bib/bbag469","DOIUrl":"10.1093/bib/bbag469","url":null,"abstract":"<p><p>Large-scale single-cell and spatial transcriptomic atlases enable the study of developmental processes at high resolution. However, most datasets capture only static snapshots of cells, making it difficult to infer continuous biological time from transcriptomic profiles. Existing temporal inference methods often show limited robustness across heterogeneous datasets, and recent single-cell foundation models, although powerful for representation learning, are not designed to capture continuous temporal relationships. We present Gene-Chronos, a parameter-efficient framework for developmental time inference built on a frozen pretrained Geneformer backbone. The model introduces learnable temporal prompt tokens and a temporal contrastive objective to extract time-informative signals and encourage temporally coherent organization of cell representations. Across multiple benchmark datasets spanning diverse species and developmental stages, Gene-Chronos outperforms existing approaches and demonstrates strong generalization to previously unseen samples. Attention-based analyses further identify genes associated with developmental progression, providing interpretable insights into temporal gene expression dynamics.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13544629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148890928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoupling topological and molecular features for interpretable biomolecular interaction prediction.","authors":"Qi Wu, Yinbo Liu, Feng Yang, Weihong Huang, Xiaolei Zhu, Juan Liu","doi":"10.1093/bib/bbag471","DOIUrl":"10.1093/bib/bbag471","url":null,"abstract":"<p><p>Predicting biomolecular interactions is fundamental to understanding cellular mechanisms and advancing drug discovery. However, biomolecular interactions exhibit immense diversity across multiple dimensions. Most existing computational methods are designed to handle one specific task or data modality, which limits their applicability and generalization capability in broader scenarios. To address this methodological rigidity, we propose a flexible framework for multi-modal feature fusion in biomolecular interaction prediction (FlexBIP). The core of FlexBIP lies in its modular architecture, which decouples intrinsic molecular features from complex graph topologies, enabling the adaptive integration of node attributes, edge properties, and auxiliary graph information. The flexible fusion methodology breaks through the limitations of task-specific models. This design enables FlexBIP to adaptively process and integrate biological data of different types and from various sources, including homogeneous interactions between molecules of the same type, heterogeneous interactions between different molecular classes, as well as qualitative binary, multi-class, and quantitative regression prediction tasks. Our research has yielded exciting results. In extensive testing across 15 benchmark datasets, covering 8 major categories of biomolecular associations, FlexBIP's performance comprehensively surpasses that of 25 state-of-the-art specialized models. Crucially, in data-scarce \"cold-start\" scenarios that simulate the discovery of new molecules, FlexBIP continues to demonstrate remarkable robustness and predictive accuracy. Furthermore, FlexBIP provides robust and reliable interpretability for various downstream analysis tasks.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13544631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148890939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models.","authors":"Lei Chen, Zhitong Jin, Ying Shao, Bo Zhou","doi":"10.1093/bib/bbag489","DOIUrl":"10.1093/bib/bbag489","url":null,"abstract":"<p><p>Metabolite-protein interactions (MPIs) play pivotal roles in regulating cellular processes and metabolic pathways and have important implications for systems biology and drug discovery. Although several computational models have been proposed, alternative approaches are needed to broaden the methodological toolkit for MPI prediction. In this study, we developed a new deep-learning model for predicting MPIs. Chemical and protein large language models were used to generate informative representations of metabolites and proteins. For each entity type, we constructed two K-nearest-neighbor hypergraphs using different values of K, thereby capturing higher-order relationships at different scales and expanding the feature-learning space. A dual hypergraph convolutional network (HGCN) learned from these relationships. The resulting high-level features were further processed using a channel-wise attention mechanism and a one-dimensional convolutional neural network (1D-CNN) to obtain integrated metabolite and protein representations. Finally, a fully connected layer generated the predictions. The model was evaluated on four MPI datasets using global five-fold cross-validation; AUC and AUPR exceeded 0.9 on most datasets. Local five-fold cross-validation and an independent test further indicated that the model could predict MPIs involving previously unobserved metabolites or proteins. The model outperformed several existing MPI prediction models and related models adapted to this task. Ablation studies supported the rationale for its design. Short abstract In this study, we focused on the metabolite-protein interactions (MPIs), which play pivotal roles in regulating cellular processes and metabolic pathways, and designed an effective deep-learning model for predicting MPIs. The model received the chemical and protein features yielded by large language models and refined them using a dual hypergraph convolutional network with two K-nearest-neighbor hypergraphs using different values of K. The resulting high-level features were further processed by a channel-wise attention mechanism and a one-dimensional convolutional neural network. A fully connected layer finally generated the predictions. The model shown competitive performance on four MPI datasets and outperformed several existing MPI prediction models.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.","authors":"Mingyuan Xu, Zhixin Dong, Bisheng Xia","doi":"10.1093/bib/bbag480","DOIUrl":"10.1093/bib/bbag480","url":null,"abstract":"<p><p>Paired spatial multi-omics provides a supervised basis for learning RNA-protein correspondence in situ, but predicting protein abundance from spatial transcriptomic data alone remains challenging across tissue contexts and protein panels. Here, we present DPAS-Graph, an adaptive relation-learning framework for spatial RNA-to-protein prediction. Rather than directly merging spatial proximity and transcriptomic similarity as fixed graph priors, DPAS-Graph represents them as two relation channels on a shared edge support and updates their contributions during representation learning for protein prediction. Its Niche-Coupled Field Encoder combines layer-wise edge-relation modeling, intra-branch relation refinement, and cross-branch residual correction to learn spot representations for protein abundance prediction. In a leave-one-dataset-out benchmark across seven paired spatial multi-omics datasets, DPAS-Graph achieved lower aggregate prediction errors and improved spot-level agreement of protein expression profiles, with gains mainly reflected in error-based metrics and PCC-Spot. Spatial autocorrelation and protein-derived domain agreement analyses were further used to characterize the spatial behavior of the predicted protein maps. When applied to external RNA-only spatial sections, DPAS-Graph generated qualitatively interpretable marker-level virtual protein maps, illustrating its use as a complementary tool for protein-level interpretation of transcriptomics-only spatial data.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148910736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.","authors":"Lei Hou, Xiao-Hua Zhou, Fuzhong Xue, Hao Chen","doi":"10.1093/bib/bbag473","DOIUrl":"10.1093/bib/bbag473","url":null,"abstract":"<p><p>The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures-HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments-to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR's superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning methodology using a masked neural network for robust genetic risk score calculation from noisy and missing data.","authors":"Steven Squires, Michael N Weedon, Richard A Oram","doi":"10.1093/bib/bbag538","DOIUrl":"https://doi.org/10.1093/bib/bbag538","url":null,"abstract":"<p><strong>Purpose: </strong>Genetic risk scores (GRSs) are summaries of genetic data that can improve prediction of disease risk and progression. GRSs are increasing available but rely on high-quality input data to produce good output results; with noisy or missing inputs the GRS may be inaccurate. We aimed to develop a method to produce a robust estimate of the GRS when input data are missing, noisy or both.</p><p><strong>Approach: </strong>We developed a neural network approach, named masked-multi-layer perceptron (MLP), for robust GRS calculation trained on a set of GRS scores calculated on clean data. The masked-MLP includes additional input data and has noise inserted during training, both which make the model more robust.</p><p><strong>Results: </strong>A GRS for type 1 diabetes (T1D) calculated on input data with 10% of the data corrupted had a Spearman rank correlation to the clean GRS of 0.669 (0.665-0.674) while the equivalent for the masked-MLP was 0.951 (0.950-0.952). For the same data, the area under the receiver operating characteristic curve for separation of T1D from population samples fell from 0.919 (0.904-0.932) to 0.808 (0.787-0.827) for the GRS while the masked-MLP fell to 0.910 (0.895-0.924).</p><p><strong>Conclusions: </strong>The masked-MLP was more robust to noise when calculating a GRS than using standard approaches. Our approach has the potential to enable GRS calculations which are more robust to noise.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OmniExtract: an automatic data extraction tool based on large language model and prompt engineering.","authors":"Yibo Wang, Bixia Tang, Sicheng Wu, Shaoqi Bei, Xiaojing Gao, Yueyue Wu, Shuying Liu, Huangxia Hu, Yuyan Meng, Demian Kong, Wenming Zhao","doi":"10.1093/bib/bbag502","DOIUrl":"10.1093/bib/bbag502","url":null,"abstract":"<p><p>Extracting structured information from documents or scientific papers is crucial for data sharing and retrieval. Recent advances in large language models (LLMs) have demonstrated strong capabilities in language understanding, and a number of LLM-based tools have been developed for extraction-oriented tasks. However, it's still difficult to find a universal and user-friendly tool for various practical extraction tasks. To address this challenge, we propose OmniExtract, an automatic data extraction tool with user-friendly configuration files that can adapt to various data extraction tasks. OmniExtract employs a prompt optimization method to refine task-specific prompts and achieve high extraction performance. It also supports comprehensive data extraction from both documents and tables, making it applicable to a broad range of data sources. Evaluation results show that OmniExtract obtains a high accuracy ~90% for three datasets. Furthermore, two additional data extraction applications of OmniExtract in real-world scenarios have been presented, achieving an accuracy of 92.21% and ~90% precision and recall, respectively. Specifically, OmniExtract can handle tabular files of various sizes and formats, and achieve over 99% precision and recall on table information extraction tasks. The data reliability performance shows that OmniExtract is a valuable tool for database updating. An online testing service is available at https://ngdc.cncb.ac.cn/omniextract/. The service can be deployed locally with the code in https://github.com/wyb39/OmniExtract.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13577667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surf2Spot: a surface-informed geometry-aware model for predicting partner-independent binder and nanobody design hotspots.","authors":"Anwen Zhao, Yu Li, Zixuan Wang, Tianhao Wu, Ran Fu, Yuxuan Lou, Yanfen Xu, Xuliang Han, Wenying Wang, Jun Yan, Xiangfeng Wang","doi":"10.1093/bib/bbag542","DOIUrl":"10.1093/bib/bbag542","url":null,"abstract":"<p><p>Protein-protein interactions (PPIs) and nanobody-antigen interactions (NAIs) play essential roles in cellular function, yet potential hotspot prediction remains challenging. We present Surf2Spot, a deep learning framework that integrates sequence embeddings, structural features, and protein surface properties to predicts surface regions with a high propensity to contain PPI hotspot residues. Unlike complex-prediction approaches that require predefined binding partners, Surf2Spot identifies interaction-prone regions directly from target sequences or predicted monomeric structures, enabling partner-independent hotspot prediction. By jointly modeling structural and physicochemical determinants, Surf2Spot achieves strong hotspot prediction performance on curated PPI and NAI datasets, outperforming existing methods in terms of F1-scores and the area under the precision-recall curve (AUPRC). Case studies on NbPDS1 and VdPDA1 demonstrate that Surf2Spot can identify putative hotspot residues within functional domains that are enriched in experimentally validated binder designs. For the tested targets, Surf2Spot-guided designs (with RFdiffusion and BindCraft) yielded a four-fold increase in successful design throughput and enhanced binding affinities compared to baseline strategies in a target-dependent manner. These results establish Surf2Spot as a powerful tool for hotspot discovery and rational protein engineering.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}