Briefings in bioinformatics最新文献_第3页

Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics. 基于图的对比学习实现了单细胞和空间多组学之间的统一集成和生态位转移。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag432

Weige Zhou, Xueying Fan, Lanxiang Li, Jianrong Zheng, Xiaodong Liu, Wenfei Jin, Luyi Tian

{"title":"Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics.","authors":"Weige Zhou, Xueying Fan, Lanxiang Li, Jianrong Zheng, Xiaodong Liu, Wenfei Jin, Luyi Tian","doi":"10.1093/bib/bbag432","DOIUrl":"https://doi.org/10.1093/bib/bbag432","url":null,"abstract":"The rapid growth of single-cell and spatial omics has outpaced computational methods capable of unifying these data into a cohesive framework for tissue atlas construction and cross-sample analysis. A critical bottleneck lies in the inability of existing tools to co-embed cells from diverse technologies-spanning transcriptomics, epigenomics, and proteomics-into a shared reference space while preserving spatial architecture and molecular specificity. Here, we present Garfield (Graph-based Contrastive Learning Enables Fast Single-Cell Embedding), a geometric deep-learning framework that addresses these challenges through spatially or molecularly aware cell embedding. Leveraging a graph contrastive learning framework, Garfield learns a shared embedding space for data generated by diverse technologies, enabling seamless construction and querying of spatial reference atlases. Our results show that Garfield consistently outperforms state-of-the-art benchmark models in identifying spatial niches across multiple datasets. We further demonstrate Garfield's versatility by applying it to multimodal spatial data, including gene expression and chromatin accessibility, where it successfully identifies distinct niches in the mouse brain. Notably, Garfield reveals tumor microenvironment heterogeneity in non-small cell lung cancer and breast cancer, uncovered conserved, barrier-like immune niches at tumor margins orchestrating CD80-mediated T cell-B cell-dendritic cell interactions and IFN-$gamma$/B cell activation pathways, forming spatially coordinated immune surveillance hubs. These findings underscore Garfield's potential to advance spatial omics research by offering a robust, scalable solution for integrating and interpreting complex spatial data across diverse tissue types and modalities.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13481149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788444","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}

引用次数: 0

DeSpaST: deconvoluting spatial transcriptomics signals to cell-level resolution using histology images. DeSpaST：利用组织学图像将空间转录组学信号反卷积到细胞水平分辨率。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag415

Qin Zhou, Shidan Wang, Yi Jiang, Yang Liu, Tingyi Wanyan, Kenian Chen, Zhuoyu Wen, Zhikai Chi, Peiran Quan, Kevin Lutz, Ruichen Rong, Lin Xu, Guanghua Xiao, Yang Xie

{"title":"DeSpaST: deconvoluting spatial transcriptomics signals to cell-level resolution using histology images.","authors":"Qin Zhou, Shidan Wang, Yi Jiang, Yang Liu, Tingyi Wanyan, Kenian Chen, Zhuoyu Wen, Zhikai Chi, Peiran Quan, Kevin Lutz, Ruichen Rong, Lin Xu, Guanghua Xiao, Yang Xie","doi":"10.1093/bib/bbag415","DOIUrl":"10.1093/bib/bbag415","url":null,"abstract":"Advances in spatial transcriptomics (ST) technologies enable spatially resolved gene expression profiling, yet most platforms remain limited to spot-level resolution, where each measurement aggregates signals from multiple cells and obscures cell-specific programs and microenvironmental interactions. Existing computational approaches either provide limited sub-spot refinement or rely on matched single-cell RNA sequencing references that are costly and difficult to obtain. Here, we present DeSpaST (Deconvoluting Spatial Transcriptomics Signals to Cell-Level Resolution Using Histology Images), a dynamic edge-conditioned graph convolutional network that deconvolves spot-level ST data into cell-level gene expression profiles using only paired histology images. DeSpaST extracts nucleus-level morphological features, constructs directed cellular interaction graphs, and integrates spatial and transcriptional information through message passing. Validated across four cancer datasets with orthogonal Xenium, immunofluorescence, ablation, and interslice evaluations, DeSpaST enhances spatial resolution, facilitates downstream cellular-level analyses, and provides deeper insights into tissue biology.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13431286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148668413","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}

引用次数: 0

Advancing bioinformatics with language models: components, applications, and perspectives. 用语言模型推进生物信息学：组件、应用和观点。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag367

Jiajia Liu, Mengyuan Yang, Yankai Yu, Haixia Xu, Tiangang Wang, Kang Li, Xiaobo Zhou

{"title":"Advancing bioinformatics with language models: components, applications, and perspectives.","authors":"Jiajia Liu, Mengyuan Yang, Yankai Yu, Haixia Xu, Tiangang Wang, Kang Li, Xiaobo Zhou","doi":"10.1093/bib/bbag367","DOIUrl":"10.1093/bib/bbag367","url":null,"abstract":"Large language models (LLMs) are deep learning-based artificial intelligence models that have achieved remarkable success in natural language processing. Typically composed of neural networks with billions of parameters, they are trained on massive unlabeled datasets using self-supervised or semi-supervised learning. Beyond language, LLMs hold immense potential for addressing complex bioinformatics challenges. This review provides a comprehensive overview of transformer-based model applications in genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. We discuss critical components, including tokenization strategies for diverse biological data, transformer architectures, attention mechanisms, and pretraining approaches. We also survey currently available foundation models and their downstream applications across bioinformatics domains. Finally, we highlight major challenges that remain insufficiently addressed in prior reviews and outline future perspectives and design principles for next-generation biological language models, offering practical guidance for both users and developers.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418975","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}

引用次数: 0

Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models. 以组织病理学为中心的空间组学计算进化：整合、映射和基础模型。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag387

Ninghui Hao, Xinxing Yang, Boshen Yan, Dong Li, Junzhou Huang, Xintao Wu, Emily S Ruiz, Arlene Ruiz de Luzuriaga, Chen Zhao, Guihong Wan

引用次数: 0

Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis. 将三维基因组结构整合到变异集分析中的Hi-C通知核关联检验。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag390

Yueyang Huang, Riddhik Basu, Yuhuan Cheng, Wenbin Lu, Shannon T Holloway, Chang Chen, Yun Li, Jung-Ying Tzeng

{"title":"Hi-C informed kernel association test for integrating 3D genome structure into variant-set analysis.","authors":"Yueyang Huang, Riddhik Basu, Yuhuan Cheng, Wenbin Lu, Shannon T Holloway, Chang Chen, Yun Li, Jung-Ying Tzeng","doi":"10.1093/bib/bbag390","DOIUrl":"10.1093/bib/bbag390","url":null,"abstract":"Variant-set association analysis is a powerful strategy for genetic studies of whole-genome sequence (WGS) data, especially for rare variants. By aggregating variant signals, variant-set analysis can improve statistical power, result interpretability, and study replicability. Motivated by the evidence that 3D genome architecture plays a critical role in regulating gene transcription, several works have incorporated 3D genome architecture into gene-based association tests and demonstrated great promise. In this work, we extend the idea of 3D-genome guided test from gene-centric to gene-agnostic, whole-genome testing by introducing an Hi-C informed kernel association test (i.e. HiC-KAT). We present a principled procedure that converts Hi-C contact confidence into borrowing weights and integrates these weights into genetic similarity kernels so that higher-confidence interacting loci contribute more to the association test of the target variant set. We use a controlling parameter to adaptively determine the appropriate degree of information borrowing from its interacting loci during association testing. We assess the performance of HiC-KAT using simulations and illustrate its advantage in detecting rare-variant sets using WGS data from the ARIC study in the Trans-Omics for Precision Medicine program.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13387501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148547915","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}

引用次数: 0

Correction to: ZeCardioAI: combining zebrafish, AI, and xAI for an in-depth cardiac phenotyping platform. 修正：ZeCardioAI：结合斑马鱼、AI和xAI，建立深度心脏表型平台。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag460

引用次数: 0

Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolution. 在单细胞分辨率下解码子宫腺肌症细胞凋亡、铁下垂和炎症细胞死亡。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag386

Qingjing Sheng, Qiongwei Wu, Jiao Fan, Vinoth Kumar Sangaraju, Balachandran Manavalan, Xiaoying He

{"title":"Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolution.","authors":"Qingjing Sheng, Qiongwei Wu, Jiao Fan, Vinoth Kumar Sangaraju, Balachandran Manavalan, Xiaoying He","doi":"10.1093/bib/bbag386","DOIUrl":"10.1093/bib/bbag386","url":null,"abstract":"Accurate pathway activity inference from single-cell RNA sequencing (scRNA-seq) data is hindered by sparsity, technical noise, and the weak yet coordinated nature of transcriptional programs. Existing methods typically aggregate expression values over predefined gene sets, which can obscure context-dependent regulatory structure. Here, we present Graph-based Pathway Activity Scoring (GraphPAS), a hierarchical graph learning framework for recovering coherent pathway-level structure from scRNA-seq data. Systematic benchmarking across scRNA-seq datasets showed that GraphPAS consistently achieved higher adjusted Rand index, normalized mutual information, and silhouette width than AUCell and scapGNN, while maintaining greater robustness under dropout and Gaussian noise perturbations. Applied to adenomyosis scRNA-seq data, GraphPAS revealed enrichment of programmed cell death programs in macrophages. Pain-associated samples showed elevated apoptosis, ferroptosis, and necroptosis signatures accompanied by inflammatory activation, implicating macrophage-centered cell death remodeling in the adenomyosis microenvironment.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148469164","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}

引用次数: 0

A comparative and exploratory analysis of computational methods for TCR structural prediction and antigen-specific TCR discovery. TCR结构预测和抗原特异性TCR发现计算方法的比较和探索性分析。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag382

Qiang Huang, Yu Liu, Xian Tang, Wei Zhang, Yingyin Cao, JuanJuan Zhao, Furong Qi, Zheng Zhang

{"title":"A comparative and exploratory analysis of computational methods for TCR structural prediction and antigen-specific TCR discovery.","authors":"Qiang Huang, Yu Liu, Xian Tang, Wei Zhang, Yingyin Cao, JuanJuan Zhao, Furong Qi, Zheng Zhang","doi":"10.1093/bib/bbag382","DOIUrl":"10.1093/bib/bbag382","url":null,"abstract":"Reliable structural prediction of T-cell receptors (TCRs) is essential for dissecting antigen recognition and accelerating TCR-based therapeutic development, yet the performance of emerging computational structure prediction models for this task requires further systematic evaluation under practical usage conditions. Here, we assembled a manually curated dataset of TCR crystal structures and compared five state-of-the-art predictors-AlphaFold2 (v2.3.1), TCRmodel2, AlphaFold3, ESMFold, and tFold-TCR-in both single-chain and paired-chain modes. Using pLDDT and model confidence scores (pTM/ipTM), we defined optimized quality thresholds for assessing model reliability. Our analyses revealed a pronounced context dependence in model performance: AlphaFold3 achieved the highest accuracy for paired-chain TCR predictions (excluding the α-FRs domain), while tFold-TCR excelled in single-chain modeling (excluding Vα, CDR1α, CDR1β, and CDR2β domains), indicating that their performance varies significantly depending on the prediction mode and specific structural domains. We further showed that, under our evaluation conditions, structure-guided clustering of predicted CDR3β loops showed improved sensitivity and achieved higher conformational consistency for certain antigen-specific TCR groups compared with conventional sequence-based methods. Applying this framework, we successfully identified two functional SARS-CoV-2-specific TCRs using a TPS (TPSGTWLTY)-reactive TCR template. Our study establishes a practical comparative framework and highlights the translational potential of structure-guided computational workflows for antigen-specific TCR discovery.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13375105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148469060","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}

引用次数: 0

Sparsity is all you need: rethinking biologically informed neural networks. 你所需要的就是稀疏性：重新思考生物信息的神经网络。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag425

Isabella Caranzano, Corrado Pancotti, Cesare Rollo, Flavio Sartori, Pietro Liò, Piero Fariselli, Tiziana Sanavia

{"title":"Sparsity is all you need: rethinking biologically informed neural networks.","authors":"Isabella Caranzano, Corrado Pancotti, Cesare Rollo, Flavio Sartori, Pietro Liò, Piero Fariselli, Tiziana Sanavia","doi":"10.1093/bib/bbag425","DOIUrl":"10.1093/bib/bbag425","url":null,"abstract":"Biologically informed neural networks are increasingly adopted in bioinformatics under the premise that embedding biological knowledge into model architectures yields more accurate and interpretable predictions. This approach has driven a growing literature of pathway-informed models aiming to move beyond black-box learning by explicitly encoding biological structure. However, it remains unclear whether these models exploit biological knowledge or instead benefit from a different inductive bias. Here, we systematically investigate this question across 29 state-of-the-art pathway-informed neural networks by explicitly decoupling biological annotations from network architecture. For each evaluable model, we implement a structure-matched randomization protocol, in which pathway annotations are replaced with random associations while preserving sparsity and architectural constraints, allowing for a direct comparison under controlled conditions. Across multiple prediction tasks, datasets, and evaluation metrics, the randomized models consistently match or outperform their biologically informed counterparts. Moreover, pathway-informed models show no systematic advantage in interpretability: randomized models recover disease-associated biomarkers with comparable accuracy and yield highly correlated feature rankings. Our results reveal that the performance gains commonly attributed to biological pathway integration arise predominantly from sparsity-induced regularization rather than from biological knowledge itself. We provide a general evaluation workflow to test whether biological priors contribute predictive information beyond sparsity, offering practical guidance for the development of biology-aware neural networks. The code implementing the proposed methodology is available on GitHub at https://github.com/compbiomed-unito/Pathway_Randomization.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13446513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148683379","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}

引用次数: 0

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction. 桥接局部-全局跨膜蛋白背景与对比预训练，用于无对齐的致病性预测。

IF 7.3 2区生物学

Briefings in bioinformatics Pub Date : 2026-07-03 DOI: 10.1093/bib/bbag352

Yihang Bao, Zhe Liu, Fangyi Zhao, Wenhao Li, Hui Jin, Guan Ning Lin

{"title":"Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.","authors":"Yihang Bao, Zhe Liu, Fangyi Zhao, Wenhao Li, Hui Jin, Guan Ning Lin","doi":"10.1093/bib/bbag352","DOIUrl":"10.1093/bib/bbag352","url":null,"abstract":"Predicting the pathogenic consequences of protein mutations is a cornerstone of precision medicine, yet it remains a formidable challenge for transmembrane proteins (TMPs), a clinically vital class of drug targets. Existing computational methods are often hampered by their reliance on evolutionary data and fail to model TMP-specific biophysical constraints. Here, we introduce Memo-Patho, a deep learning framework for robust, alignment-free pathogenicity prediction of TMP variants. The core innovation is a within-protein, label-informed supervised contrastive pretraining strategy that learns sequence-encoded biophysical signatures distinguishing pathogenic and benign variants by directly comparing them within the same protein context. By fusing sequence-level representations from protein language models with local structural proxies derived from sequence, Memo-Patho achieves accurate predictions without multiple sequence alignments or experimental structures. Across diverse TMP benchmarks and under protein-level group splits, Memo-Patho consistently outperforms leading predictors, achieving up to 0.93 accuracy, and it transfers to an independent KCNQ1 ion-channel cohort without re-training. Its resource-efficient, alignment-free design enables routine large-scale screening when evolutionary or structural data are sparse. Conceptually, Memo-Patho addresses a key gap by directly learning discriminative, sequence-anchored signatures pertinent to TMP-specific constraints, offering a principled and generalizable foundation for research-use clinical variant triage and proteome-wide mutation-effect modeling.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381498","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}

引用次数: 0