Briefings in bioinformatics最新文献_第4页

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

GTE-PPIS: a protein-protein interaction site predictor based on graph transformer and equivariant graph neural network. GTE-PPIS：基于图变换和等变图神经网络的蛋白质相互作用位点预测器。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf290

Xun Wang, Tongyu Han, Runqiu Feng, Zhijun Xia, Hanyu Wang, Wenqian Yu, Huanhuan Dai, Haonan Song, Tao Song

引用次数: 0

Correction to: ProtGraph: a tool for the quick and comprehensive exploration and exploitation of the peptide search space derived from protein sequence databases using graphs. protegraph：一个工具，用于快速和全面的探索和利用从蛋白质序列数据库中获得的肽搜索空间。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf222

引用次数: 0

Correction to: DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data. DOMSCNet：一种利用多层组学数据进行胃癌分类的深度学习模型。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf218

引用次数: 0

A multivariable cis-Mendelian randomization method robust to weak instrument bias and horizontal pleiotropy bias. 一种多变量顺式孟德尔随机化方法，对弱仪器偏倚和水平多效性偏倚具有鲁棒性。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf250

Yihe Yang, Noah Lorincz-Comi, Mengxuan Li, Xiaofeng Zhu

{"title":"A multivariable cis-Mendelian randomization method robust to weak instrument bias and horizontal pleiotropy bias.","authors":"Yihe Yang, Noah Lorincz-Comi, Mengxuan Li, Xiaofeng Zhu","doi":"10.1093/bib/bbaf250","DOIUrl":"10.1093/bib/bbaf250","url":null,"abstract":"Multivariable cis-Mendelian randomization (cis-MVMR) has become an effective approach for identifying therapeutic targets that influence disease susceptibility. However, biases from invalid instruments, such as weak instruments and horizontal pleiotropy, remain unsolved. In this paper, we propose a new method called the cis-Mendelian randomization bias correction estimating equation (cis-MRBEE), which mitigates weak instrument bias by leveraging a local sparse genetic architecture: most variants within a genomic region are associated with a trait through linkage disequilibrium with a few causal variants. Cis-MRBEE identifies causal variants or proxies of exposures via fine-mapping, re-estimates genetic associations using the identified variants, and applies a double-penalized minimization to estimate causal exposures and account for horizontal pleiotropic effects. Simulations showed that in the presence of weak instruments and horizontal pleiotropy, directly adapting standard MVMR methods to cis-MVMR was infeasible, and existing cis-MVMR methods failed to control type I errors. In contrast, cis-MRBEE exhibited robustness to these sources of bias. We applied cis-MRBEE to the ANGPTL3 locus and identified a credible set comprising APOA1, APOC1, and PCSK9 as likely causal proteins for LDL-C, HDL-C, and TG. The subsequent analysis revealed a complex protein regulation network that influenced lipid traits. Furthermore, we used cis-MRBEE to discover that the expressions of CR1 in the basal ganglia, hippocampus, and oligodendrocytes were potentially causal for Alzheimer's disease and its biomarkers, A$beta $42 and pTau, in cerebrospinal fluid.","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/PMC12140020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233230","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