Bioinformatics (Oxford, England)最新文献_第8页

MS1FA: Shiny app for the annotation of redundant features in untargeted metabolomics datasets. MS1FA：闪亮的应用程序，用于注释非目标代谢组学数据集中的冗余特征。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf161

Ruibing Shi, Frank Klawonn, Mark Brönstrup, Raimo Franke

{"title":"MS1FA: Shiny app for the annotation of redundant features in untargeted metabolomics datasets.","authors":"Ruibing Shi, Frank Klawonn, Mark Brönstrup, Raimo Franke","doi":"10.1093/bioinformatics/btaf161","DOIUrl":"10.1093/bioinformatics/btaf161","url":null,"abstract":"Motivation: Untargeted metabolomics, the comprehensive analysis of small molecules in biological systems, has become an invaluable tool for understanding physiology and metabolism. However, the annotation of metabolomic data is often confounded by the presence of redundant features, which can arise from e.g. multimerization, in-source fragments (ISFs), and adducts.Results: MS1FA uniquely integrates all major annotation approaches for redundant features within a single interactive platform. It combines correlation-based grouping with reliable ISF annotation using MS2 data and operates with MS1 data only, MS2 data only, or both. Additionally, it offers a distinctive method for grouping features based on relational criteria. As the only web-based platform with these capabilities, MS1FA provides easy access and allows users to explore and annotate the feature table interactively, with options to download the results.Availability and implementation: MS1FA is freely accessible at https://ms1fa.helmholtz-hzi.de. The source code and data are available at https://github.com/RuibingS/MS1FA_RShiny_dashboard and are archived with the DOI 10.5281/zenodo.15118962.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":"41 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031090","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}

引用次数: 0

Mapping the attractor landscape of Boolean networks with biobalm. 用生物弹绘制布尔网络的吸引子景观。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf280

Van-Giang Trinh, Kyu Hyong Park, Samuel Pastva, Jordan C Rozum

{"title":"Mapping the attractor landscape of Boolean networks with biobalm.","authors":"Van-Giang Trinh, Kyu Hyong Park, Samuel Pastva, Jordan C Rozum","doi":"10.1093/bioinformatics/btaf280","DOIUrl":"10.1093/bioinformatics/btaf280","url":null,"abstract":"Motivation: Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram (SD) describes this interplay in a discrete analog of Waddington's epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. Efficient computational tools for studying SDs are essential for the understanding of Boolean attractor landscapes and connecting them to their biological functions.Results: We present a new approach to SD construction for asynchronously updated Boolean networks, implemented in the biologist's Boolean attractor landscape mapper, biobalm. We compare biobalm to similar tools and find a substantial performance increase in SD construction, attractor identification, and attractor control. We perform the most comprehensive comparative analysis to date of the SD structure in experimentally-validated Boolean models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small SDs, indicating simple decision structures. In contrast, nonrandom models from the literature are enriched in extremely large SDs, indicating an abundance of decision points and suggesting the presence of complex Waddington landscapes in nature.Availability and implementation: The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further data, scripts for testing, analysis, and figure generation are available online at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact at https://doi.org/10.5281/zenodo.13854760.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055076","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}

引用次数: 0

SimSon: simple contrastive learning of SMILES for molecular property prediction. SimSon：用于分子性质预测的smile简单对比学习。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf275

Chae Eun Lee, Jin Sob Kim, Jin Hong Min, Sung Won Han

{"title":"SimSon: simple contrastive learning of SMILES for molecular property prediction.","authors":"Chae Eun Lee, Jin Sob Kim, Jin Hong Min, Sung Won Han","doi":"10.1093/bioinformatics/btaf275","DOIUrl":"10.1093/bioinformatics/btaf275","url":null,"abstract":"Motivation: Molecular property prediction with deep learning has accelerated drug discovery and retrosynthesis. However, the shortage of labeled molecular data and the challenge of generalizing across the vast chemical spaces pose significant hurdles for leveraging deep learning in molecular property prediction. This study proposes a self-supervised framework designed to acquire a Simplified Molecular Input Line Entry System (SMILES) representation, which we have dubbed Simple SMILES contrastive learning (SimSon). SimSon was pre-trained using unlabeled SMILES data through contrastive learning to grasp the SMILES representations.Results: Our findings demonstrate that contrastive learning with randomized SMILES enriches the ability of the model to generalize and its robustness as it captures the global semantic context at the molecular level. In downstream tasks, SimSon performs competitively when compared to graph-based methods and even outperforms them on certain benchmark datasets. These results indicate that SimSon effectively captures structural information from SMILES, exhibiting remarkable generalization and robustness. The potential applications of SimSon extend to bioinformatics and cheminformatics, encompassing areas such as drug discovery and drug-drug interaction prediction.Availability and implementation: The source code is available at https://github.com/lee00206/SimSon.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060843","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}

引用次数: 0

Topology-based metrics for finding the optimal sparsity in gene regulatory network inference. 基因调控网络推理中最优稀疏度的拓扑度量。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf120

Nils Lundqvist, Mateusz Garbulowski, Thomas Hillerton, Erik L L Sonnhammer

{"title":"Topology-based metrics for finding the optimal sparsity in gene regulatory network inference.","authors":"Nils Lundqvist, Mateusz Garbulowski, Thomas Hillerton, Erik L L Sonnhammer","doi":"10.1093/bioinformatics/btaf120","DOIUrl":"10.1093/bioinformatics/btaf120","url":null,"abstract":"Motivation: Gene regulatory network (GRN) inference is a complex task aiming to unravel regulatory interactions between genes in a cell. A major shortcoming of most GRN inference methods is that they do not attempt to find the optimal sparsity, i.e. the single best GRN, which is important when applying GRN inference in a real situation. Instead, the sparsity tends to be controlled by an arbitrarily set hyperparameter.Results: In this paper, two new methods for predicting the optimal sparsity of GRNs are formulated and benchmarked on simulated perturbation-based gene expression data using four GRN inference methods: LASSO, Zscore, LSCON, and GENIE3. Both sparsity prediction methods are defined using the hypothesis that the topology of real GRNs is scale-free, and are evaluated based on their ability to predict the sparsity of the true GRN. The results show that the new topology-based approaches reliably predict a sparsity close to the true one. This ability is valuable for real-world applications where a single GRN is inferred from real data. In such situations, it is vital to be able to infer a GRN with the correct sparsity.Availability and implementation: https://bitbucket.org/sonnhammergrni/powerlaw_sparsity/ and https://codeocean.com/capsule/4393635/.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702527","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}

引用次数: 0

EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions. EPIPDLF：用于预测增强子-启动子相互作用的预训练深度学习框架。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btae716

Zhichao Xiao, Yan Li, Yijie Ding, Liang Yu

{"title":"EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.","authors":"Zhichao Xiao, Yan Li, Yijie Ding, Liang Yu","doi":"10.1093/bioinformatics/btae716","DOIUrl":"10.1093/bioinformatics/btae716","url":null,"abstract":"Motivation: Enhancers and promoters, as regulatory DNA elements, play pivotal roles in gene expression, homeostasis, and disease development across various biological processes. With advancing research, it has been uncovered that distal enhancers may engage with nearby promoters to modulate the expression of target genes. This discovery holds significant implications for deepening our comprehension of various biological mechanisms. In recent years, numerous high-throughput wet-lab techniques have been created to detect possible interactions between enhancers and promoters. However, these experimental methods are often time-intensive and costly.Results: To tackle this issue, we have created an innovative deep learning approach, EPIPDLF, which utilizes advanced deep learning techniques to predict EPIs based solely on genomic sequences in an interpretable manner. Comparative evaluations across six benchmark datasets demonstrate that EPIPDLF consistently exhibits superior performance in EPI prediction. Additionally, by incorporating interpretable analysis mechanisms, our model enables the elucidation of learned features, aiding in the identification and biological analysis of important sequences.Availability and implementation: The source code and data are available at: https://github.com/xzc196/EPIPDLF.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560365","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}

引用次数: 0

Automatic biomarker discovery and enrichment with BRAD. 利用BRAD自动发现和富集生物标志物。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf159

Joshua Pickard, Ram Prakash, Marc Andrew Choi, Natalie Oliven, Cooper Stansbury, Jillian Cwycyshyn, Nicholas Galioto, Alex Gorodetsky, Alvaro Velasquez, Indika Rajapakse

{"title":"Automatic biomarker discovery and enrichment with BRAD.","authors":"Joshua Pickard, Ram Prakash, Marc Andrew Choi, Natalie Oliven, Cooper Stansbury, Jillian Cwycyshyn, Nicholas Galioto, Alex Gorodetsky, Alvaro Velasquez, Indika Rajapakse","doi":"10.1093/bioinformatics/btaf159","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf159","url":null,"abstract":"Motivation: Integrating Large Language Models (LLMs) with research tools presents technical and reproducibility challenges for biomedical research. While commercial artificial intelligence (AI) systems are easy to adopt, they obscure data provenance, lack transparency, and can generates false information, making them unfit for many research problems. To address these challenges, we developed the Bioinformatics Retrieval Augmented Digital (BRAD) agent software system.Results: Here, we introduce BRAD, an agentic system that integrates LLMs with external tools and data to streamline research workflows. BRAD's modular agents retrieve information from literature, custom software, and online databases while maintaining transparent protocols to increase the reliability of AI generated results. We apply BRAD to a biomarker discovery pipeline, automating both execution and the generation of enrichment reports. This workflow contextualizes user data within the literature, enabling a level of interpretation and automation that surpasses conventional research tools. Beyond the workflow we highlight here, BRAD is a flexible system that has been deployed in other applications including a chatbot, video RAG, and analysis of single cell data.Availability and implementation: The source code for BRAD is available at https://github.com/Jpickard1/BRAD; Information for pip installation, tutorials, documentation, and further information can be found at: ReadTheDocs.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":"41 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045940","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}

引用次数: 0

ProtNote: a multimodal method for protein-function annotation. ProtNote：蛋白质功能注释的多模态方法。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf170

Samir Char, Nathaniel Corley, Sarah Alamdari, Kevin K Yang, Ava P Amini

{"title":"ProtNote: a multimodal method for protein-function annotation.","authors":"Samir Char, Nathaniel Corley, Sarah Alamdari, Kevin K Yang, Ava P Amini","doi":"10.1093/bioinformatics/btaf170","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf170","url":null,"abstract":"Motivation: Understanding the protein sequence-function relationship is essential for advancing protein biology and engineering. However, <1% of known protein sequences have human-verified functions. While deep-learning methods have demonstrated promise for protein-function prediction, current models are limited to predicting only those functions on which they were trained.Results: Here, we introduce ProtNote, a multimodal deep-learning model that leverages free-form text to enable both supervised and zero-shot protein-function prediction. ProtNote not only maintains near state-of-the-art performance for annotations in its training set but also generalizes to unseen and novel functions in zero-shot test settings. ProtNote demonstrates superior performance in the prediction of novel Gene Ontology annotations and Enzyme Commission numbers compared to baseline models by capturing nuanced sequence-function relationships that unlock a range of biological use cases inaccessible to prior models. We envision that ProtNote will enhance protein-function discovery by enabling scientists to use free text inputs without restriction to predefined labels-a necessary capability for navigating the dynamic landscape of protein biology.Availability and implementation: The code is available on GitHub: https://github.com/microsoft/protnote; model weights, datasets, and evaluation metrics are provided via Zenodo: https://zenodo.org/records/13897920.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":"41 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026634","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}

引用次数: 0

Pitfalls of bacterial pan-genome analysis approaches: a case study of Mycobacterium tuberculosis and two less clonal bacterial species. 细菌泛基因组分析方法的陷阱：结核分枝杆菌和两个较少克隆的细菌物种的案例研究。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf219

Maximillian G Marin, Natalia Quinones-Olvera, Christoph Wippel, Mahboobeh Behruznia, Brendan M Jeffrey, Michael Harris, Brendon C Mann, Alex Rosenthal, Karen R Jacobson, Robin M Warren, Heng Li, Conor J Meehan, Maha R Farhat

{"title":"Pitfalls of bacterial pan-genome analysis approaches: a case study of Mycobacterium tuberculosis and two less clonal bacterial species.","authors":"Maximillian G Marin, Natalia Quinones-Olvera, Christoph Wippel, Mahboobeh Behruznia, Brendan M Jeffrey, Michael Harris, Brendon C Mann, Alex Rosenthal, Karen R Jacobson, Robin M Warren, Heng Li, Conor J Meehan, Maha R Farhat","doi":"10.1093/bioinformatics/btaf219","DOIUrl":"10.1093/bioinformatics/btaf219","url":null,"abstract":"Summary: Pan-genome analysis is a fundamental tool for studying bacterial genome evolution; however, the variety in methods used to define and measure the pan-genome poses challenges to the interpretation and reliability of results. Using Mycobacterium tuberculosis, a clonally evolving bacterium with a small accessory genome, as a model system, we systematically evaluated sources of variability in pan-genome estimates. Our analysis revealed that differences in assembly type (short-read versus hybrid), annotation pipeline, and pan-genome software, significantly impact predictions of core and accessory genome size. Extending our analysis to two additional bacterial species, Escherichia coli and Staphylococcus aureus, we observed consistent tool-dependent biases but species-specific patterns in pan-genome variability. Our findings highlight the importance of integrating nucleotide- and protein-level analyses to improve the reliability and reproducibility of pan-genome studies across diverse bacterial populations.Availability and implementation: Panqc is freely available under an MIT license at https://github.com/maxgmarin/panqc.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026501","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}

引用次数: 0

OLS4: a new Ontology Lookup Service for a growing interdisciplinary knowledge ecosystem. OLS4：为不断增长的跨学科知识生态系统提供的新的本体查找服务。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf279

James McLaughlin, Josh Lagrimas, Haider Iqbal, Helen Parkinson, Henriette Harmse

{"title":"OLS4: a new Ontology Lookup Service for a growing interdisciplinary knowledge ecosystem.","authors":"James McLaughlin, Josh Lagrimas, Haider Iqbal, Helen Parkinson, Henriette Harmse","doi":"10.1093/bioinformatics/btaf279","DOIUrl":"10.1093/bioinformatics/btaf279","url":null,"abstract":"Summary: The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently, there has been a significant increase in the size and complexity of ontologies due to new scales of biological knowledge, such as spatial transcriptomics, new ontology development methodologies, and curation on an increased scale. Existing Web-based tools for ontology browsing such as BioPortal and OntoBee do not support the full range of definitions used by today's ontologies. In order to support the community going forward, we have developed OLS4, implementing the complete OWL2 specification, internationalization support for multiple languages, and a new user interface with UX enhancements such as links out to external databases. OLS4 has replaced OLS3 in production at EMBL-EBI and has a backward compatible API supporting users of OLS3 to transition.Availability and implementation: The source code of OLS is available at https://github.com/EBISPOT/ols4 and DOI 10.5281/zenodo.14960290 with Apache 2.0 License. A freely available implementation is accessible at https://www.ebi.ac.uk/ols4.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047983","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}

引用次数: 0

ROICellTrack: a deep learning framework for integrating cellular imaging modalities in subcellular spatial transcriptomic profiling of tumor tissues. ROICellTrack：一个深度学习框架，用于整合肿瘤组织亚细胞空间转录组分析的细胞成像模式。

Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI: 10.1093/bioinformatics/btaf152

Xiaofei Song, Xiaoqing Yu, Carlos M Moran-Segura, Hongzhi Xu, Tingyi Li, Joshua T Davis, Aram Vosoughi, G Daniel Grass, Roger Li, Xuefeng Wang

{"title":"ROICellTrack: a deep learning framework for integrating cellular imaging modalities in subcellular spatial transcriptomic profiling of tumor tissues.","authors":"Xiaofei Song, Xiaoqing Yu, Carlos M Moran-Segura, Hongzhi Xu, Tingyi Li, Joshua T Davis, Aram Vosoughi, G Daniel Grass, Roger Li, Xuefeng Wang","doi":"10.1093/bioinformatics/btaf152","DOIUrl":"10.1093/bioinformatics/btaf152","url":null,"abstract":"Motivation: Spatial transcriptomic (ST) technologies, such as GeoMx Digital Spatial Profiler, are increasingly utilized to investigate the role of diverse tumor microenvironment components, particularly in relation to cancer progression, treatment response, and therapeutic resistance. However, in many ST studies, the spatial information obtained from immunofluorescence imaging is primarily used for identifying regions of interest (ROIs) rather than as an integral part of downstream transcriptomic data analysis and interpretation.Results: We developed ROICellTrack, a deep learning-based framework that better integrates cellular imaging with spatial transcriptomic profiling. By analyzing 56 ROIs from urothelial carcinoma of the bladder and upper tract urothelial carcinoma, ROICellTrack identified distinct cancer-immune cell mixtures, characterized by specific transcriptomic and morphological signatures and receptor-ligand interactions linked to tumor content and immune infiltrations. Our findings demonstrate the value of integrating imaging with transcriptomics to analyze spatial omics data, improving our understanding of tumor heterogeneity and its relevance to personalized and targeted therapies.Availability and implementation: ROICellTrack is publicly available at https://github.com/wanglab1/ROICellTrack.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813180","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}

引用次数: 0