Briefings in bioinformatics最新文献_第9页

Knowledge-guided multi-level network modeling with experimental characterization identifies PRKCA as a novel biomarker and tumor suppressor triggering ferroptosis in prostate cancer. 知识引导的多层次网络模型与实验表征确定了PRKCA作为一种新的生物标志物和肿瘤抑制因子引发前列腺癌铁下垂。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf220

Yuxin Lin, Zongming Jia, Jixiang Wu, Hubo Yang, Xin Chen, He Wang, Xuedong Wei, Wenying Yan, Xin Qi, Yuhua Huang

{"title":"Knowledge-guided multi-level network modeling with experimental characterization identifies PRKCA as a novel biomarker and tumor suppressor triggering ferroptosis in prostate cancer.","authors":"Yuxin Lin, Zongming Jia, Jixiang Wu, Hubo Yang, Xin Chen, He Wang, Xuedong Wei, Wenying Yan, Xin Qi, Yuhua Huang","doi":"10.1093/bib/bbaf220","DOIUrl":"10.1093/bib/bbaf220","url":null,"abstract":"Prostate cancer (PCa) is observed with high incidence in men worldwide. Ferroptosis, occurred from disorders in a series of gene and pathway regulation, is an emerging target against cancer. However, most of the computational approaches solely treated ferroptosis-related genes (FRGs) as independent variables in model training, and the interactions among FRGs and other candidates were not fully deciphered in a disease-specific content. In this study, a novel network-based and knowledge-guided bioinformatics model was proposed by integrating ferroptosis-related prior knowledge with topological and functional characterization on a protein-protein interaction network for biomarker discovery in PCa development and ferroptosis. The model started at a random walk with restart algorithm for weighting genes close to known FRGs in the PCa-specific network to extract a core subnetwork for robustness and vulnerability analysis. Then key regulatory modules and a candidate gene, i.e. PRKCA, were respectively identified using a multi-level prioritization strategy with hub-bottleneck node filtering, edge-based gene co-expression measuring, community module detecting and a newly defined Ferr.neighbor functional score. The experimental validation using human clinical samples, cell lines, and nude mice convinced the role of PRKCA as a latent biomarker and a tumor suppressor in PCa carcinogenesis with a potential mechanism on triggering GPX4-mediated ferroptosis of PCa cells. This study provides a general-purpose systems biology framework for significant FRG screening, and future translational perspectives of PRKCA as a novel diagnostic and therapeutic signature for PCa management should be explored.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109750","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 promiscuous aggregating inhibitor analysis with intelligent machine learning classification. 基于智能机器学习分类的混杂聚合抑制分析。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf205

Luxuan Wang, Beihong Ji, Jingchen Zhai, Junmei Wang

{"title":"Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.","authors":"Luxuan Wang, Beihong Ji, Jingchen Zhai, Junmei Wang","doi":"10.1093/bib/bbaf205","DOIUrl":"10.1093/bib/bbaf205","url":null,"abstract":"Small molecules have been playing a crucial role in drug discovery; however, some exhibit nonspecific inhibitory effects during hit screening due to the formation of colloidal aggregators. Such false positives often lead to significant research costs and time investment. Therefore, to identify potential aggregating compounds efficiently and accurately at an early stage of drug discovery, we employed several machine learning techniques to develop classification models for identifying promiscuous aggregating inhibitors. Using a training dataset of 10 000 aggregators and 10 000 nonaggregators, models were trained by combining four different molecular representations with various machine learning algorithms. We found that the best-performing model is the one that employs path-based FP2 fingerprints in conjunction with the cubic support vector machine algorithm, which achieved the highest accuracy and area under the receiver operating characteristic curve values for both the validation and test datasets while maintaining high sensitivity and specificity levels (>0.93). Additionally, we have proposed a new model interpretation method, global sensitivity analysis (GSA), to complement the well-recognized SHapley Additive exPlanations analysis. Several comparative studies have shown that GSA is a time-efficient and accurate approach for identifying crucial descriptors that contribute to model prediction, especially in the scenario where the dataset contains a substantial number of data entries with a limited set of descriptors. Our models as well as GSA findings can provide useful guidance on screening library design to minimize false positives.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12056367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961831","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

Network analysis of multivariate time series data in biological systems: methods and applications. 生物系统中多变量时间序列数据的网络分析：方法和应用。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf223

Hao Mei, Zhiyuan Wang, Hang Yang, Xiaoke Li, Yaqing Xu

{"title":"Network analysis of multivariate time series data in biological systems: methods and applications.","authors":"Hao Mei, Zhiyuan Wang, Hang Yang, Xiaoke Li, Yaqing Xu","doi":"10.1093/bib/bbaf223","DOIUrl":"10.1093/bib/bbaf223","url":null,"abstract":"Network analysis has become an essential tool in biological and biomedical research, providing insights into complex biological mechanisms. Since biological systems are inherently time-dependent, incorporating time-varying methods is crucial for capturing temporal changes, adaptive interactions, and evolving dependencies within networks. Our study explores key time-varying methodologies for network structure estimation and network inference based on observed structures. We begin by discussing approaches for estimating network structures from data, focusing on the time-varying Gaussian graphical model, dynamic Bayesian network, and vector autoregression-based causal analysis. Next, we examine analytical techniques that leverage pre-specified or observed networks, including other autoregression-based methods and latent variable models. Furthermore, we explore practical applications and computational tools designed for these methods. By synthesizing these approaches, our study provides a comprehensive evaluation of their strengths and limitations in the context of biological data analysis.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12096012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118818","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

An algorithmic perspective on deciphering cell-cell interactions with spatial omics data. 用空间组学数据解读细胞-细胞相互作用的算法视角。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf236

Mike van Santvoort, Federica Eduati

引用次数: 0

Influenza virus reassortment patterns exhibit preference and continuity while uncovering cross-species transmission events. 流感病毒重组模式在揭示跨物种传播事件时表现出偏好和连续性。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf233

Xiao Ding, Yun Ma, Shicheng Li, Jingze Liu, Luyao Qin, Aiping Wu

{"title":"Influenza virus reassortment patterns exhibit preference and continuity while uncovering cross-species transmission events.","authors":"Xiao Ding, Yun Ma, Shicheng Li, Jingze Liu, Luyao Qin, Aiping Wu","doi":"10.1093/bib/bbaf233","DOIUrl":"10.1093/bib/bbaf233","url":null,"abstract":"Genomic reassortment is a key driver of influenza virus evolution and a major factor in pandemic emergence, as reassorted strains can exhibit significantly altered antigenicity. However, due to technical and ethical constraints, research on reassortment patterns (RPs) has been limited, impeding effective surveillance and control strategies. To address this gap, we developed FluRPId, a framework for identifying RPs based on the genetic diversity of influenza viruses. FluRPId integrates principles of reassortment diversity maximization, dominance, and epidemiological likelihood to assess the credibility of detected reassortment events. Applying FluRPId, we constructed a comprehensive reassortment landscape of influenza viruses, encompassing widespread reassortment events with high credibility, which also include most previously reported reassortment events. Our analysis revealed that the NS gene frequently reassorts with PA and NA, while reassortment involving HA, NA, and NS occurs more frequently than expected. Furthermore, we identified specific loci combinations that exhibit strong linkage during reassortment, providing insights into segment association preferences. Additionally, extensive reassortment chains were observed across all subtypes, underscoring the continuity of reassortment in influenza virus evolution. Notably, we identified significant cross-species reassortment events and characterized host adaptation changes in cross-species-transmitted viruses. Our study provides the most comprehensive reassortment landscape of influenza viruses to date, uncovering key patterns, preferences, and evolutionary continuity. These findings bridge a critical gap in macro-scale reassortment studies and offer insights for future research and control efforts.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12096011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118813","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

Machine learning-augmented m6A-Seq analysis without a reference genome. 没有参考基因组的机器学习增强m6A-Seq分析。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf235

Jing Yang, Minggui Song, Yifan Bu, Haonan Zhao, Chenghui Liu, Ting Zhang, Chujun Zhang, Shutu Xu, Chuang Ma

{"title":"Machine learning-augmented m6A-Seq analysis without a reference genome.","authors":"Jing Yang, Minggui Song, Yifan Bu, Haonan Zhao, Chenghui Liu, Ting Zhang, Chujun Zhang, Shutu Xu, Chuang Ma","doi":"10.1093/bib/bbaf235","DOIUrl":"10.1093/bib/bbaf235","url":null,"abstract":"Methylated RNA m6A immunoprecipitation sequencing (m6A-Seq) is a powerful technique for investigating transcriptome-wide m6A modification. However, most of the existing m6A-Seq protocols rely on reference genomes, limiting their use in species lacking sequenced genomes. Here, we introduce mlPEA, a user-friendly, multi-functional platform specifically tailored to the streamlined processing of m6A-Seq data in a reference genome-free manner. mlPEA provides a comprehensive collection of functions required for performing transcriptome-wide m6A identification and analysis, where the reference-de novo assembled transcriptome-is built solely using m6A-Seq data. By taking advantage of machine learning (ML) algorithms, mlPEA enhances m6A-Seq data analysis by constructing robust computational models for identifying high-quality transcripts and high-confidence m6A-modified regions. These functions and ML models have been integrated into a web-based Galaxy framework. This ensures that mlPEA has powerful data interaction and visualization capabilities, with flexibility, traceability, and reproducibility throughout the analytical process. mlPEA also has high compatibility and portability as it employs advanced packaging technology, dramatically simplifying its large-scale application in various species. Validated through case studies of Arabidopsis, maize, and wheat, mlPEA has demonstrated its utility and robustness regarding reference genome-free m6A-Seq data analysis for plants of various genomic complexities. mlPEA is freely available via GitHub: https://github.com/cma2015/mlPEA.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141508","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

Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2. 利用HighFold2预测含非天然氨基酸环肽的结构。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf202

Cheng Zhu, Sen Cao, Tianfeng Shang, Jingjing Guo, An Su, Chengxi Li, Hongliang Duan

{"title":"Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.","authors":"Cheng Zhu, Sen Cao, Tianfeng Shang, Jingjing Guo, An Su, Chengxi Li, Hongliang Duan","doi":"10.1093/bib/bbaf202","DOIUrl":"10.1093/bib/bbaf202","url":null,"abstract":"Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the 3D structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for these residues. Then, it incorporates an additional neural network to characterize the atom-level features of peptides, allowing for multi-scale modeling of peptide molecules while enabling the distinction between various unnatural amino acids. Besides, HighFold2 constructs a relative position encoding matrix for cyclic peptides based on different cyclization constraints. Except for training using spatial structures with unnatural amino acids, HighFold2 also parameterizes the unnatural amino acids to relax the predicted structure by energy minimization for clash elimination. Extensive empirical experiments demonstrate that HighFold2 can accurately predict the 3D structures of cyclic peptide monomers containing unnatural amino acids and their complexes with proteins, with the median RMSD for Cα reaching 1.891 Å. All these results indicate the effectiveness of HighFold2, representing a significant advancement in cyclic peptide-based drug discovery.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12066415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977203","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

Accounting for the impact of rare variants on causal inference with RARE: a novel multivariable Mendelian randomization method. 用一种新的多变量孟德尔随机化方法来解释罕见变量对因果推理的影响。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf214

Yu Cheng, Xinjia Ruan, Xiaofan Lu, Yuqing Yang, Yuhang Wang, Shangjin Yan, Yuzhe Sun, Fangrong Yan, Liyun Jiang, Tiantian Liu

{"title":"Accounting for the impact of rare variants on causal inference with RARE: a novel multivariable Mendelian randomization method.","authors":"Yu Cheng, Xinjia Ruan, Xiaofan Lu, Yuqing Yang, Yuhang Wang, Shangjin Yan, Yuzhe Sun, Fangrong Yan, Liyun Jiang, Tiantian Liu","doi":"10.1093/bib/bbaf214","DOIUrl":"10.1093/bib/bbaf214","url":null,"abstract":"Mendelian randomization (MR) method utilizes genetic variants as instrumental variables to infer the causal effect of an exposure on an outcome. However, the impact of rare variants on traits is often neglected, and traditional MR assumptions can be violated by correlated horizontal pleiotropy (CHP) and uncorrelated horizontal pleiotropy (UHP). To address these issues, we propose a multivariable MR approach, an extension of the standard MR framework: MVMR incorporating Rare variants Accounting for multiple Risk factors and shared horizontal plEiotropy (RARE). In the simulation studies, we demonstrate that RARE effectively detects the causal effects of exposures on outcome with accounting for the impact of rare variants on causal inference. Additionally, we apply RARE to study the effects of high density lipoprotein and low density lipoprotein on type 2 diabetes and coronary atherosclerosis, respectively, thereby illustrating its robustness and effectiveness in real data analysis.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076068","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

On "Bioinformatics in Russia: history and present-day landscape" by M.A. Nawaz, I.E. Pamirsky, and K.S. Golokhvast. 关于“俄罗斯的生物信息学：历史和当今景观”，作者：M.A. Nawaz， I.E. Pamirsky和K.S. Golokhvast。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf161

Mikhail S Gelfand

引用次数: 0

To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era. 打包还是不打包：重新审视后alphafold时代的蛋白质侧链打包。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf297

Sriniketh Vangaru, Debswapna Bhattacharya

{"title":"To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era.","authors":"Sriniketh Vangaru, Debswapna Bhattacharya","doi":"10.1093/bib/bbaf297","DOIUrl":"10.1093/bib/bbaf297","url":null,"abstract":"Protein side-chain packing (PSCP), the problem of predicting side-chain conformations given a fixed backbone structure, has important implications in the modeling of structures and interactions. However, despite the groundbreaking progress in protein structure prediction pioneered by AlphaFold, the existing PSCP methods still rely on experimental inputs, and do not leverage AlphaFold-predicted backbone coordinates to enable PSCP at scale. Here, we perform a large-scale benchmarking of the predictive performance of various PSCP methods on public datasets from multiple rounds of the Critical Assessment of Structure Prediction challenges using a diverse set of evaluation metrics. Empirical results demonstrate that the PSCP methods perform well in packing the side-chains with experimental inputs, but they fail to generalize in repacking AlphaFold-generated structures. We additionally explore the effectiveness of leveraging the self-assessment confidence scores from AlphaFold by implementing a backbone confidence-aware integrative approach. While such a protocol often leads to performance improvement by attaining modest yet statistically significant accuracy gains over the AlphaFold baseline, it does not yield consistent and pronounced improvements. Our study highlights the recent advances and remaining challenges in PSCP in the post-AlphaFold era.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494701","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