Briefings in bioinformatics最新文献

{"title":"Dynamic changes of synergy relationship between lncRNA and immune checkpoint in cancer progression.","authors":"Chenyu Liu, Qianyi Lu, Jian Li, Di Wang, Zhuoru Wang, Wenli Chen, Yakun Zhang, Caiyu Zhang, Yue Gao, Shangwei Ning","doi":"10.1093/bib/bbaf370","DOIUrl":"10.1093/bib/bbaf370","url":null,"abstract":"<p><p>In the battle between tumors and the immune system, immune evasion based on immune checkpoints (ICPs) is a critical mechanism for tumor progression. Long noncoding RNAs (lncRNAs) are key players in tumorigenesis and immune responses; however, the mechanisms underlying the synergistic relationship between lncRNAs and ICPs in cancer progression remain poorly understood. Manually curated ICPs and high-confidence lncRNA-messenger RNA (mRNA) interactions were integrated via a protein-protein interaction (PPI) network to construct an initial set of lncRNA-ICP pairs. Stage-specific synergy scores were then performed and used to identify stage-specific synergistic pairs for each cancer type. Our findings indicate that several key genes, including MALAT1 and CRNDE, are widely involved in cancer progression and exhibit various patterns in multiple cancers. Genes within the lncRNA-ICP synergy network were associated with the dynamic changes of immune cells during cancer progression, and these relationships remain relatively stable across different cancers and stages. The relationships of the synergistic pairs we identified demonstrate consistency with spatial transcriptomics data in skin cutaneous melanoma. Notably, the overall expression of genes identified in Stage 4 could significantly differentiate patients' survival outcomes. Moreover, the genes we identified could distinguish patients' responses to immunotherapy.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12306437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728041","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":"DrugProtAI: A machine learning-driven approach for predicting protein druggability through feature engineering and robust partition-based ensemble methods.","authors":"Ankit Halder, Sabyasachi Samantaray, Sahil Barbade, Aditya Gupta, Sanjeeva Srivastava","doi":"10.1093/bib/bbaf330","DOIUrl":"10.1093/bib/bbaf330","url":null,"abstract":"<p><p>Drug design and development are central to clinical research, yet 90% of drugs fail to reach the clinic, often due to inappropriate selection of drug targets. Conventional methods for target identification lack precision and sensitivity. While various computational tools have been developed to predict the druggability of proteins, they often focus on limited subsets of the human proteome or rely solely on amino acid properties. Our study presents DrugProtAI, a tool developed by implementing a partitioning-based method and trained on the entire human protein set using both sequence- and non-sequence-derived properties. The partitioned method was evaluated using popular machine learning algorithms, of which Random Forest and XGBoost performed the best. A comprehensive analysis of 183 features, encompassing biophysical, sequence-, and non-sequence-derived properties, achieved a median Area Under Precision-Recall Curve (AUC) of 0.87 in target prediction. The model was further tested on a blinded validation set comprising recently approved drug targets. The key predictors were also identified, which we believe will help users in selecting appropriate drug targets. We believe that these insights are poised to significantly advance drug development. This version of the tool provides the probability of druggability for human proteins. The tool is freely accessible at https://drugprotai.pythonanywhere.com/.</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/PMC12236430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590475","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":"SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation.","authors":"Maribel Pérez-Ribera, Muhammad Faizan-Khan, Roger Giné, Josep M Badia, Alexandra Junza, Oscar Yanes, Marta Sales-Pardo, Roger Guimerà","doi":"10.1093/bib/bbaf333","DOIUrl":"10.1093/bib/bbaf333","url":null,"abstract":"<p><p>Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra of known compounds. This process is hindered by the current lack of comprehensive reference spectral libraries. To address this gap, we need accurate in silico fragmentation tools for predicting MS/MS spectra of compounds for which empirical spectra do not exist. Here, we present SingleFrag, a novel deep learning tool that predicts individual fragments separately, rather than attempting to predict the entire fragmentation spectrum at once. Our results demonstrate that SingleFrag surpasses state-of-the-art in silico fragmentation tools, providing a powerful method for annotating unknown MS/MS spectra of known compounds. As a proof of concept, we successfully annotate three previously unidentified compounds frequently found in human samples.</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/PMC12245663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607397","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":"AutoMethyc: an automated methylation analysis for massively parallel sequencing data.","authors":"Fernando Ambriz-Barrera, Miguel Ruiz-De La Cruz, Héctor Martínez-Gregorio, Clara E Díaz-Velásquez, Aldo H De La Cruz-Montoya, Felipe Vaca-Paniagua","doi":"10.1093/bib/bbaf416","DOIUrl":"10.1093/bib/bbaf416","url":null,"abstract":"<p><strong>Motivation: </strong>Bisulfite sequencing (BS-Seq) enables a comprehensive and detailed analysis of DNA methylation patterns at single-nucleotide resolution. While methylation differences can contribute to various diseases, their sincronous occurrence at distinct loci complicates understanding. Therefore, advanced tools are essential to facilitate the identification and analysis of methylation programs and patterns.</p><p><strong>Results: </strong>AutoMethyc provides a comparative approach by integrating different algorithms coordinated and optimized for use on desktop computers and servers. The workflow evaluates the methylation status from different perspectives, facilitating interpretation in an interactive HTML report, incorporating new co-methylation analyses for marker identification, as well as exploratory complex workflows with dimension reduction techniques and identification of unsupervised groups between samples or sites. AutoMethyc was tested in a breast cancer study ($n=389$; 233 cases and 156 controls) using BS-Seq data from the Illumina MiSeq platform, mapping 330 methylation-prone citocine (CpG) sites in 20 genes. The analysis was performed on a desktop with 64 GB RAM, 16 cores (4.673 GHz), and 326 KB/s internet, running Fedora 39 with i3wm. The tool processed the dataset in 48 h, showcasing its efficiency and scalability.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12357496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858925","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":"UnCOT-AD: Unpaired Cross-Omics Translation Enables Multi-Omics Integration for Alzheimer's Disease Prediction.","authors":"Abrar Rahman Abir, Sajib Acharjee Dip, Liqing Zhang","doi":"10.1093/bib/bbaf438","DOIUrl":"https://doi.org/10.1093/bib/bbaf438","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) is a progressive neurodegenerative disorder, posing a growing public health challenge. Traditional machine learning models for AD prediction have relied on single omics data or phenotypic assessments, limiting their ability to capture the disease's molecular complexity and resulting in poor performance. Recent advances in high-throughput multi-omics have provided deeper biological insights. However, due to the scarcity of paired omics datasets, existing multi-omics AD prediction models rely on unpaired omics data, where different omics profiles are combined without being derived from the same biological sample, leading to biologically less meaningful pairings and causing less accurate predictions. To address these issues, we propose UnCOT-AD, a novel deep learning framework for Unpaired Cross-Omics Translation enabling effective multi-omics integration for AD prediction. Our method introduces the first-ever cross-omics translation model trained on unpaired omics datasets, using two coupled Variational Autoencoders and a novel cycle consistency mechanism to ensure accurate bidirectional translation between omics types. We integrate adversarial training to ensure that the generated omics profiles are biologically realistic. Moreover, we employ contrastive learning to capture the disease specific patterns in latent space to make the cross-omics translation more accurate and biologically relevant. We rigorously validate UnCOT-AD on both cross-omics translation and AD prediction tasks. Results show that UnCOT-AD empowers multi-omics based AD prediction by combining real omics profiles with corresponding omics profiles generated by our cross-omics translation module and achieves state-of-the-art performance in accuracy and robustness. Source code is available at https://github.com/abrarrahmanabir/UnCOT-AD.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12381766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144943404","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":"Computational methods and data resources for predicting tumor neoantigens.","authors":"Xiaofei Zhao, Lei Wei, Xuegong Zhang","doi":"10.1093/bib/bbaf302","DOIUrl":"10.1093/bib/bbaf302","url":null,"abstract":"<p><p>Neoantigens are tumor-specific antigens presented exclusively by cancer cells. These antigens are recognized as nonself by the host immune system, thereby eliciting an antitumor T-cell response. This response is significantly enhanced through neoantigen-based immunotherapies, such as personalized cancer vaccines. The repertoire of neoantigens is unique to each cancer patient, necessitating neoantigen prediction for designing patient-specific immunotherapies. This review presents the computational methods and data resources used for neoantigen prediction, as well as the prediction-associated challenges. Neoantigen prediction typically uses human leukocyte antigen typing, RNA-seq transcript quantification, somatic variant calling, peptide-major histocompatibility complex (pMHC) presentation prediction, and pMHC recognition prediction as the main computational steps. The immunoinformatics tools used for these steps and for the overall prediction of neoantigens are systematically summarized and detailed in this review.</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/PMC12222050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552339","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":"CAT: a conditional association test for microbiome data using a permutation approach.","authors":"Yushu Shi, Liangliang Zhang, Kim-Anh Do, Robert R Jenq, Christine B Peterson","doi":"10.1093/bib/bbaf326","DOIUrl":"10.1093/bib/bbaf326","url":null,"abstract":"<p><p>In microbiome analysis, researchers often seek to identify taxonomic features associated with an outcome of interest. However, microbiome features are intercorrelated and linked by phylogenetic relationships, making it challenging to assess the association between an individual feature and an outcome. This paper proposes a novel conditional association test, CAT, that can account for other features and phylogenetic relatedness when testing the association between a feature and an outcome. CAT adopts a permutation approach, measuring the importance of a feature in predicting the outcome by permuting operational taxonomic unit/amplicon sequence variant counts belonging to that feature from the data and quantifying how much the association with the outcome is weakened through the change in the coefficient of determination $R^{2}$. Compared with marginal association tests, it focuses on the added value of a feature in explaining outcome variation that is not captured by other features. By leveraging global tests including PERMANOVA and MiRKAT-based methods, CAT allows association testing for continuous, binary, categorical, count, survival, and correlated outcomes. We demonstrate through simulation studies that CAT can provide a direct quantification of feature importance that is distinct from that of marginal association tests, and illustrate CAT with applications to two real-world studies on the microbiome in melanoma patients: one examining the role of the microbiome in shaping immunotherapy response, and one investigating the association between the microbiome and survival outcomes. Our results illustrate the potential of CAT to inform the design of microbiome interventions aimed at improving clinical outcomes.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607395","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":"NASNet-DTI: accurate drug-target interaction prediction using heterogeneous graphs and node adaptation.","authors":"Ningyu Zhong, Zhihua Du","doi":"10.1093/bib/bbaf342","DOIUrl":"10.1093/bib/bbaf342","url":null,"abstract":"<p><p>Drug-target interactions (DTIs) play a key role in drug development, and accurate prediction can significantly improve the efficiency of this process. Traditional experimental methods are reliable but time-consuming and laborious. With the rapid development of deep learning, many DTI prediction methods have emerged. However, most of these methods only focus on the intrinsic features of drugs and targets, while ignoring the relational features between them. In addition, existing graph-based DTI prediction methods often face the challenge of over-smoothing in graph neural networks (GNNs), which limits their prediction accuracy. To address these issues, we propose NASNet-DTI (Drug-target Interactions Based on Node Adaptation and Similarity Networks), a new framework designed to overcome these limitations. NASNet-DTI uses graph convolutional network to extract features from drug molecules and targets separately, and constructs heterogeneous networks to represent two types of nodes: drugs and targets. The edges in the network describe their multiple relationships: drug-drug, target-target, and drug-target. In the feature learning stage, NASNet-DTI adopts a node adaptive learning strategy to dynamically determine the optimal aggregation depth for each node. This ensures that each node can learn the most discriminative features, which effectively alleviates the over-smoothing problem and improves prediction accuracy. Experimental results show that NASNet-DTI significantly outperforms existing methods on multiple datasets, demonstrating its effectiveness and potential as a powerful tool to advance drug discovery and development.</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/PMC12265895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641767","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":"PREDAC-FluB: predicting antigenic clusters of seasonal influenza B viruses with protein language model embedding based convolutional neural network.","authors":"Wenping Xie, Jingze Liu, Chuan Wang, Jiangyuan Wang, Wenjie Han, Yousong Peng, Xiangjun Du, Jing Meng, Kang Ning, Taijiao Jiang","doi":"10.1093/bib/bbaf308","DOIUrl":"10.1093/bib/bbaf308","url":null,"abstract":"<p><p>Influenza poses a significant global public health threat, with vaccination being the most effective and economical preventive measure. However, these punctuated antigenic changes, particularly in HA, result in escape from the immunity that was induced by prior infection or vaccination. Accurately predicting antigenic variation and understanding the antigenic dynamics of influenza viruses are crucial for selecting appropriate vaccine strains, but no established methods exist for influenza B viruses. Therefore, we present PREDAC-FluB, a hybrid deep learning framework that integrates spatial feature extraction via CNN to model interactions in HA1 sequences, multimodal sequence representation combining ESM-2 embeddings with six physicochemical descriptors and continuous encoding (ESM2-7-features), and UMAP-guided clustering for antigenic cluster identification. Using data from 9036 B/Victoria-lineage and 4520 B/Yamagata-lineage influenza virus pair. PREDAC-FluB demonstrates superior performance over traditional machine learning methods in predicting antigenic variation in influenza viruses, successfully identifying major antigenic clusters. Specifically, PREDAC-FluB classified the B/Victoria lineage into nine antigenic clusters and the B/Yamagata lineage into three antigenic clusters. In five-fold cross-validation for B/Victoria viruses, PREDAC-FluB with ESM2-7-features encoding achieved AUROC values of 0.9961 on the validation set and 0.9856 on the independent test set. In retrospective testing for B/Victoria viruses, PREDAC-FluB achieved AUROC values ranging from 0.83 to 0.97, demonstrating high prediction accuracy and effectively capturing antigenic variation information. In conclusion, PREDAC-FluB is a robust tool for antigenic computation, capable of accurately predicting antigenic variation in influenza B viruses. Its high prediction accuracy makes it a promising auxiliary method for recommending future influenza vaccine strains.</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/PMC12264208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641769","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 survey on deep learning for polygenic risk scores.","authors":"Max Schuran, Benjamin Goudey, Gillian S Dite, Enes Makalic","doi":"10.1093/bib/bbaf373","DOIUrl":"10.1093/bib/bbaf373","url":null,"abstract":"<p><p>Polygenic risk scores (PRS) combine the effects of multiple genetic variants to predict an individual's genetic predisposition to a disease. PRS typically rely on linear models, which assume that all genetic variants act independently. They often fall short in predictive accuracy and are not able to explain the genetic variability of a trait to the full extent. There is growing interest in applying deep learning neural networks to model PRS given their ability to model non-linear relationships and strong performance in other domains. We conducted a survey of the literature to investigate how neural networks model PRS. We categorize deep learning-based approaches by their underlying architecture, highlighting their modeling assumptions, likely strengths and potential weaknesses of the architectures. Several categories of neural network architectures exhibited promising signs for the improvement of PRS' predictive power, namely sequence-based architectures, graph neural networks and those that incorporated biological knowledge. Additionally, the use of latent representations in autoencoders has improved predictive performance across diverse ancestries. However, a lack of existing model benchmarks on consistent datasets and phenotypes makes it challenging to understand the extent to which different architectures improve performance. Interpretability of deep learning-based PRS is also challenging with great care required when inferring causation. To address these challenges, we suggest the establishment and adherence to reporting standards and benchmarks to aid the development of deep learning-based PRS to find quantifiable trends in neural network architectures.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844499","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}