Briefings in bioinformatics最新文献

{"title":"Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.","authors":"Syed Sabih Ur Rehman, Muhammad Ibtisam Nasar, Cristina S Mesquita, Souhaila Al Khodor, Richard A Notebaart, Sascha Ott, Sunil Mundra, Ramesh P Arasardanam, Khalid Muhammad, Mohammad Tauqeer Alam","doi":"10.1093/bib/bbaf323","DOIUrl":"10.1093/bib/bbaf323","url":null,"abstract":"<p><p>Microbiomes are crucial for human health and well-being, with microbial dysbiosis being linked to various complex diseases. Therefore, understanding the structural and functional changes in the microbiome, along with the underlying mechanisms in disease conditions, is essential. In this review, we outline the structure and function of different human microbiomes and examine how changes in their composition may contribute to diseases. We highlight critical information associated with microbial dysbiosis and explore various therapeutic strategies for restoring a healthy microbiome, including microbiota transplantation, phage therapy, probiotics, prebiotics, dietary interventions, and drug-based approaches. Further, to better understand microbiome dysbiosis, we discuss multi-omics approaches including metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics, alongside computational modeling approaches such as ecological and metabolic network analysis. We outline key challenges associated with multi-omics techniques and emphasize the importance of integrative systems biology approaches that combine multi-omics data with computational modeling. These approaches are crucial for effectively analyzing microbiome data, providing deeper insights into species interactions and microbiome dynamics. Finally, we offer insights into future research directions in the field of microbiome research. This review makes a unique contribution to microbiome research by presenting a holistic framework that integrates multi-omics data with multi-scale modeling to elucidate microbial interactions, microbiome dysbiosis, and their modulation in disease-associated contexts.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574844","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":"Advancing genetic engineering with active learning: theory, implementations and potential opportunities.","authors":"Qixiu Du, Haochen Wang, Benben Jiang, Xiaowo Wang","doi":"10.1093/bib/bbaf286","DOIUrl":"10.1093/bib/bbaf286","url":null,"abstract":"<p><p>Employing machine learning (ML) models to accelerate experimentation and uncover biological mechanisms has been a rising tendency in genetic engineering. However, effectively collecting data to enhance model accuracy and improve design remains challenging, especially when data quality is poor and validation resources are limited. Active learning (AL) addresses this by iteratively identifying promising candidates, thereby reducing experimental efforts while improving model performance. This review highlights how AL can assist scientists throughout the design-build-test-learn cycle, explore its various practical implementations, and discuss its potential through the integration of cross-domain expertise. In the age of genetic engineering revolutionized by data-driven ML models, AL presents an iterative framework that significantly enhances the functionalities of biomolecules and uncovers their intrinsic mechanisms, all while minimizing expenses and efforts.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590463","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":"Genomic language models (gLMs) decode bacterial genomes for improved gene prediction and translation initiation site identification.","authors":"Genereux Akotenou, Achraf El Allali","doi":"10.1093/bib/bbaf311","DOIUrl":"10.1093/bib/bbaf311","url":null,"abstract":"<p><p>Accurate bacterial gene prediction is essential for understanding microbial functions and advancing biotechnology. Traditional methods based on sequence homology and statistical models often struggle with complex genetic variations and novel sequences due to their limited ability to interpret the \"language of genes.\" To overcome these challenges, we explore genomic language models (gLMs)-inspired by large language models in natural language processing-to enhance bacterial gene prediction. These models learn patterns and contextual dependencies within genetic sequences, similar to how LLMs process human language. We employ transformers, specifically DNABERT, for bacterial gene prediction using a two-stage framework: first, identifying coding sequence (CDS) regions, and then refining predictions by identifying the correct translation initiation sites (TIS). DNABERT is fine-tuned on a curated set of NCBI complete bacterial genomes using a k-mer tokenizer for sequence processing. Our results show that GeneLM significantly improves gene prediction accuracy. Compared with the leading prokaryotic gene finders, Prodigal, GeneMark-HMM, and Glimmer, and other recent deep learning methods, GeneLM reduces missed CDS predictions while increasing matched annotations. More notably, our TIS predictions surpass traditional methods when tested against experimentally verified sites. GeneLM demonstrates the power of gLMs in decoding genetic information, achieving state-of-the-art performance in bacterial genome analysis. This advancement highlights the potential of language models to revolutionize genome annotation, outperforming conventional tools and enabling more precise genetic insights.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12222049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552340","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":"spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration.","authors":"Longyi Li, Liyan Dong, Hao Zhang, Dong Xu, Yongli Li","doi":"10.1093/bib/bbaf304","DOIUrl":"10.1093/bib/bbaf304","url":null,"abstract":"<p><p>Spatial multi-omics technologies provide valuable data on gene expression from various omics in the same tissue section while preserving spatial information. However, deciphering spatial domains within spatial omics data remains challenging due to the sparse gene expression. We propose spaLLM, the first multi-omics spatial domain analysis method that integrates large language models to enhance data representation. Our method combines a pre-trained single-cell language model (scGPT) with graph neural networks and multi-view attention mechanisms to compensate for limited gene expression information in spatial omics while improving sensitivity and resolution within modalities. SpaLLM processes multiple spatial modalities, including RNA, chromatin, and protein data, potentially adapting to emerging technologies and accommodating additional modalities. Benchmarking against eight state-of-the-art methods across four different datasets and platforms demonstrates that our model consistently outperforms other advanced methods across multiple supervised evaluation metrics. The source code for spaLLM is freely available at https://github.com/liiilongyi/spaLLM.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552342","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":"Gene regulatory network integration with multi-omics data enhances survival predictions in cancer.","authors":"Romana T Pop, Ping-Han Hsieh, Tatiana Belova, Anthony Mathelier, Marieke L Kuijjer","doi":"10.1093/bib/bbaf315","DOIUrl":"10.1093/bib/bbaf315","url":null,"abstract":"<p><p>The emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. To fully harness these data, integrative approaches have emerged as essential tools, enabling the combination of multiple omics modalities to uncover disease mechanisms. However, many such approaches overlook gene regulatory mechanisms, which play a central role in the development and progression of cancer. Patient-specific gene regulatory networks (GRNs), representing interactions between regulators (such as transcription factors) and their target genes in each individual tumour, offer a powerful framework to bridge this gap and investigate the regulatory landscape of cancer. In this study, we introduce a novel approach for integrating patient-specific GRNs with multi-omic data and assess whether their inclusion in joint dimensionality reduction models improves survival prediction across multiple cancer types. By applying our method on ten cancer datasets from The Cancer Genome Atlas, we demonstrate that incorporating GRNs enhances associations with patient survival in several cancer types. Focusing on liver cancer, with validation in independent data, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression. These were linked to dysregulated fatty acid metabolism, and identified JUND as a potential novel transcriptional regulator driving these processes. Our findings highlight the value of network-based multi-omics integration for uncovering clinically relevant regulatory mechanisms and improving our understanding of cancer biology at the patient-specific level.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574841","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":"Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.","authors":"Ke Wu, Changming Sun, Jie Geng, Ping Wang, Qi Dai, Leyi Wei, Ran Su","doi":"10.1093/bib/bbaf288","DOIUrl":"10.1093/bib/bbaf288","url":null,"abstract":"<p><p>Single-cell sequencing technology has profoundly revolutionized the field of cancer genomics, enabling researchers to explore gene expression profiles at the resolution of individual cells. Despite its extensive applications in the study of cancer gene states, pan-cancer analyses remain relatively underexplored. In this study, we propose the G-DESC-E algorithm, which effectively distinguishes dimensionality-reduced data through a grid-based approach, filters out outliers during the preprocessing phase, and employs the Louvain algorithm for prescreening cluster centroids as initial clusters. We construct an objective function by integrating label entropy with the Kullback-Leibler divergence formula, achieving final clustering results through iterative optimization. Our findings demonstrate the effectiveness of the G-DESC-E algorithm in enhancing clustering accuracy. By applying our methodology to real-world datasets, we illustrate its capability to identify critical transcriptional features associated with distinct cancer subtypes. Coupled with clustering visualization and gene ontology analysis, we identify over thirty genes potentially related to cancer occurrence and progression. The algorithm and research framework presented in this study pave the way for new directions in clinical research by applying single-cell sequencing technology to the analysis of key genes within the realm of pan-cancer analysis for the first time. This approach offers valuable insights that can inform further clinical investigations.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552341","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":"Assessing Random Forest self-reproducibility for optimal short biomarker signature discovery.","authors":"Ahmed Debit, Christophe Poulet, Claire Josse, Guy Jerusalem, Chloe-Agathe Azencott, Vincent Bours, Kristel Van Steen","doi":"10.1093/bib/bbaf318","DOIUrl":"10.1093/bib/bbaf318","url":null,"abstract":"<p><p>Biomarker signature discovery remains the main path to developing clinical diagnostic tools when the biological knowledge on pathology is weak. Shortest signatures are often preferred to reduce the cost of the diagnostic. The ability to find the best and shortest signature relies on the robustness of the models that can be built on such a set of molecules. The classification algorithm that will be used is often selected based on the average Area Under the Curve (AUC) performance of its models. However, it is not guaranteed that an algorithm with a large AUC distribution will keep a stable performance when facing data. Here, we propose two AUC-derived hyper-stability scores, the Hyper-stability Resampling Sensitive (HRS) and the Hyper-stability Signature Sensitive (HSS), as complementary metrics to the average AUC that should bring confidence in the choice for the best classification algorithm. To emphasize the importance of these scores, we compared 15 different Random Forest implementations. Our findings show that the Random Forest implementation should be chosen according to the data at hand and the classification question being evaluated. No Random Forest implementation can be used universally for any classification and on any dataset. Each of them should be tested for their average AUC performance and AUC-derived stability, prior to analysis.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607394","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":"A 3D pocket-aware lead optimization model with knowledge guidance and its application for discovery of new glutaminyl cyclase inhibitors.","authors":"Anjie Qiao, Yuting Chen, Junjie Xie, Weifeng Huang, Hao Zhang, Qirui Deng, Jiahua Rao, Ji Deng, Fanbo Meng, Zhen Wang, Mingyuan Xu, Hongming Chen, Jiancong Xie, Shuangjia Zheng, Yuedong Yang, Guo-Bo Li, Jinping Lei","doi":"10.1093/bib/bbaf345","DOIUrl":"https://doi.org/10.1093/bib/bbaf345","url":null,"abstract":"<p><p>Lead optimization, aimed at improving binding affinity or other properties of hit compounds, is a crucial task in drug discovery. Though deep learning-based 3D generative models showed promise in enhancing the efficiency of de novo drug design recently, less research and attention has garnered for structure-based lead optimization. Herein, we propose a 3D pocket-aware diffusion model named Diffleop, which explicitly incorporates the knowledge of protein-ligand binding affinity and information on covalent bonds to guide the denoising sampling process for lead optimization with enhanced binding affinity and rational properties. Specifically, the bond constraint is achieved through diffusion on fully connected molecular graphs, and the determination of atom positions, atom and bond types in each sampling step is guided by the gradient of the binding affinity that is predicted through fitting with an E(3)-equivariant expert network. The comprehensive evaluations indicated that Diffleop outperforms baseline models on lead optimization with higher affinity and more binding interactions, and can generate more drug-like molecules with more rational structures. Diffleop was further applied to optimize 5-methyl-1H-imidazole, our newly discovered lead compound targeting human glutaminyl cyclases (QCs). Three synthesized compounds exhibit substantially improved inhibitory activities against QCs, with the most effective one showing an IC50 value of 8 nM and 3.5-fold better than clinical candidate PQ912.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144648530","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":"Deciphering the MHC immunopeptidome of human cancers with Ligand.MHC atlas.","authors":"Zhi Ran, Meilin Mu, Shaofeng Lin, Tao Wang, Jing Zeng, Lan Kuang, Kunqi Chen, Shengbao Suo, Kai Yuan, Haodong Xu","doi":"10.1093/bib/bbaf314","DOIUrl":"10.1093/bib/bbaf314","url":null,"abstract":"<p><p>A fundamental principle of immunotherapy is that T cells are capable of detecting tumor epitopes presented on cancer cell surfaces. Immunopeptidomic strategies empowered by liquid chromatography-tandem mass spectrometry have transformed tumor epitopes identification and provided novel insights into tumor immunology. It enables in-depth profiling of major histocompatibility complex (MHC) presented ligands, thereby offering valuable perspectives on the molecular dialog among tumor and T cells. Here, we developed an immune-ligand identification and analysis pipeline from large-scale immunopeptidomics data. Through an extensive collection and processing of 5821 immunopeptidomic samples, which amounted to 305.7 million MS2 spectra, we identified 24 380 595 peptide-spectrum matches from these samples and further detected a total of 1 017 731 unique MHC immune ligands. These ligands were deconvolved and classified to specific HLA alleles. In total, we detected 582 852 HLA-I peptides and 434 879 HLA-II peptides that can bind to 292 HLA alleles, thereby greatly expanding the cancer immunopeptidome. Additionally, we identified and annotated 372 720 tumor-associated post-translational modification (PTM) peptides, revealing the comprehensive landscape of PTM antigens. All ligands and annotations were aggregated into Ligand.MHC Atlas, a comprehensive repository dedicated to tumor-derived HLA-presented ligands across 26 major human cancers (54 subtypes). Overall, our study uniquely integrates batch-effect correction, leverages the optimized software with novel deconvolution approach for immunopeptidomics analysis and ligand identification, and provides a public web portal with a comprehensive HLA ligand repository. Ligand.MHC Atlas functions as an invaluable resource, offering crucial understandings into immunology investigations. It will accelerate the advancement of cancer vaccines and immunotherapies. Ligand.MHC Atlas is available at http://modinfor.com/Ligand.MHC-Atlas/.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590474","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":"TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification.","authors":"Fenglan Pang, Guangfu Xue, Wenyi Yang, Yideng Cai, Jinhao Que, Haoxiu Sun, Pingping Wang, Shuaiyu Su, Xiyun Jin, Qian Ding, Zuxiang Wang, Meng Luo, Yuexin Yang, Yi Lin, Renjie Tan, Yusong Liu, Zhaochun Xu, Qinghua Jiang","doi":"10.1093/bib/bbaf316","DOIUrl":"10.1093/bib/bbaf316","url":null,"abstract":"<p><p>Spatial transcriptomics (ST) encompasses rich multi-modal information related to cell state and organization. Precisely identifying spatial domains with consistent gene expression patterns and histological features is a critical task in ST analysis, which requires comprehensive integration of multi-modal information. Here, we propose TriCLFF, a contrastive learning-based multi-modal feature fusion framework, to effectively integrate spatial associations, gene expression levels, and histological features in a unified manner. Leveraging an advanced feature fusion mechanism, our proposed TriCLFF framework outperforms existing state-of-the-art methods in terms of accuracy and robustness across four datasets (mouse brain anterior, mouse olfactory bulb, human dorsolateral prefrontal cortex, and human breast cancer) from different platforms (10x Visium and Stereo-seq) for spatial domain identification. TriCLFF also facilitates the identification of finer-grained structures in breast cancer tissues and detects previously unknown gene expression patterns in the human dorsolateral prefrontal cortex, providing novel insights for understanding tissue functions. Overall, TriCLFF establishes an effective paradigm for integrating spatial multi-modal data, demonstrating its potential for advancing ST research. The source code of TriCLFF is available online at https://github.com/HBZZ168/TriCLFF.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607398","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}