Briefings in Functional Genomics最新文献_第9页

Integration of hybrid and self-correction method improves the quality of long-read sequencing data. 混合和自校正方法的整合提高了长读数测序数据的质量。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad026

Tao Tang, Yiping Liu, Binshuang Zheng, Rong Li, Xiaocai Zhang, Yuansheng Liu

引用次数: 0

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information. DeepCMI：基于图的模型，可准确预测具有多种信息的 circRNA-miRNA 相互作用。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad030

Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu, Lei Wang, Lun Hu, Peng-Wei Hu, Yan Qiao, Xin-Fei Wang, Yu-An Huang

{"title":"DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.","authors":"Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu, Lei Wang, Lun Hu, Peng-Wei Hu, Yan Qiao, Xin-Fei Wang, Yu-An Huang","doi":"10.1093/bfgp/elad030","DOIUrl":"10.1093/bfgp/elad030","url":null,"abstract":"Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"276-285"},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10291543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution CUT&Tag data. 基于高分辨率 CUT&Tag 数据预测链特异性和细胞类型特异性 G-四重链。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad024

Yizhi Cui, Hongzhi Liu, Yutong Ming, Zheng Zhang, Li Liu, Ruijun Liu

{"title":"Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution CUT&Tag data.","authors":"Yizhi Cui, Hongzhi Liu, Yutong Ming, Zheng Zhang, Li Liu, Ruijun Liu","doi":"10.1093/bfgp/elad024","DOIUrl":"10.1093/bfgp/elad024","url":null,"abstract":"G-quadruplex (G4), a non-classical deoxyribonucleic acid structure, is widely distributed in the genome and involved in various biological processes. In vivo, high-throughput sequencing has indicated that G4s are significantly enriched at functional regions in a cell-type-specific manner. Therefore, the prediction of G4s based on computational methods is necessary instead of the time-consuming and laborious experimental methods. Recently, G4 CUT&Tag has been developed to generate higher-resolution sequencing data than ChIP-seq, which provides more accurate training samples for model construction. In this paper, we present a new dataset construction method based on G4 CUT&Tag sequencing data and an XGBoost prediction model based on the machine learning boost method. The results show that our model performs well within and across cell types. Furthermore, sequence analysis indicates that the formation of G4 structure is greatly affected by the flanking sequences, and the GC content of the G4 flanking sequences is higher than non-G4. Moreover, we also identified G4 motifs in the high-resolution dataset, among which we found several motifs for known transcription factors (TFs), such as SP2 and BPC. These TFs may directly or indirectly affect the formation of the G4 structure.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"265-275"},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9683854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prognostic signature analysis and survival prediction of esophageal cancer based on N6-methyladenosine associated lncRNAs. 基于N6-甲基腺苷相关lncRNA的食管癌预后特征分析和生存预测

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad028

Ting He, Zhipeng Gao, Ling Lin, Xu Zhang, Quan Zou

{"title":"Prognostic signature analysis and survival prediction of esophageal cancer based on N6-methyladenosine associated lncRNAs.","authors":"Ting He, Zhipeng Gao, Ling Lin, Xu Zhang, Quan Zou","doi":"10.1093/bfgp/elad028","DOIUrl":"10.1093/bfgp/elad028","url":null,"abstract":"Esophageal cancer (ESCA) has a bad prognosis. Long non-coding RNA (lncRNA) impacts on cell proliferation. However, the prognosis function of N6-methyladenosine (m6A)-associated lncRNAs (m6A-lncRNAs) in ESCA remains unknown. Univariate Cox analysis was applied to investigate prognosis related m6A-lncRNAs, based on which the samples were clustered. Wilcoxon rank and Chi-square tests were adopted to compare the clinical traits, survival, pathway activity and immune infiltration in different clusters where overall survival, clinical traits (N stage), tumor-invasive immune cells and pathway activity were found significantly different. Through least absolute shrinkage and selection operator and proportional hazard (Lasso-Cox) model, five m6A-lncRNAs were selected to construct the prognostic signature (m6A-lncSig) and risk score. To investigate the link between risk score and clinical traits or immunological microenvironments, Chi-square test and Spearman correlation analysis were utilized. Risk score was found connected with N stage, tumor stage, different clusters, macrophages M2, B cells naive and T cells CD4 memory resting. Risk score and tumor stage were found as independent prognostic variables. And the constructed nomogram model had high accuracy in predicting prognosis. The obtained m6A-lncSig could be taken as potential prognostic biomarker for ESCA patients. This study offers a theoretical foundation for clinical diagnosis and prognosis of ESCA.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"239-248"},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9886829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism. 基于注意机制的双通道神经网络预测药物与蛋白质的相互作用

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad037

Dayu Tan, Haijun Jiang, Haitao Li, Ying Xie, Yansen Su

{"title":"Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism.","authors":"Dayu Tan, Haijun Jiang, Haitao Li, Ying Xie, Yansen Su","doi":"10.1093/bfgp/elad037","DOIUrl":"10.1093/bfgp/elad037","url":null,"abstract":"The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"286-294"},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10112268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward 通过使用多模态分类器进行乳腺癌预后分析：技术现状与未来方向

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-01 DOI: 10.1093/bfgp/elae015

Archana Mathur, Nikhilanand Arya, Kitsuchart Pasupa, Sriparna Saha, Sudeepa Roy Dey, Snehanshu Saha

引用次数: 0

Short-homology-mediated PCR-based method for gene introduction in the fission yeast Schizosaccharomyces pombe 基于短同源物介导的 PCR 方法在裂殖酵母中引入基因

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-04-26 DOI: 10.1093/bfgp/elae016

Cai-Xia Zhang, Ying-Chun Hou

{"title":"Short-homology-mediated PCR-based method for gene introduction in the fission yeast Schizosaccharomyces pombe","authors":"Cai-Xia Zhang, Ying-Chun Hou","doi":"10.1093/bfgp/elae016","DOIUrl":"https://doi.org/10.1093/bfgp/elae016","url":null,"abstract":"Schizosaccharomyces pombe is a commonly utilized model organism for studying various aspects of eukaryotic cell physiology. One reason for its widespread use as an experimental system is the ease of genetic manipulations, leveraging the natural homology-targeted repair mechanism to accurately modify the genome. We conducted a study to assess the feasibility and efficiency of directly introducing exogenous genes into the fission yeast S. pombe using Polymerase Chain Reaction (PCR) with short-homology flanking sequences. Specifically, we amplified the NatMX6 gene (which provides resistance to nourseothricin) using PCR with oligonucleotides that had short flanking regions of 20 bp, 40 bp, 60 bp and 80 bp to the target gene. By using this purified PCR product, we successfully introduced the NatMX6 gene at position 171 385 on chromosome III in S. pombe. We have made a simple modification to the transformation procedure, resulting in a significant increase in transformation efficiency by at least 5-fold. The success rate of gene integration at the target position varied between 20% and 50% depending on the length of the flanking regions. Additionally, we discovered that the addition of dimethyl sulfoxide and boiled carrier DNA increased the number of transformants by ~60- and 3-fold, respectively. Furthermore, we found that the removal of the pku70+ gene improved the transformation efficiency to ~5% and reduced the formation of small background colonies. Overall, our results demonstrate that with this modified method, even very short stretches of homologous regions (as short as 20 bp) can be used to effectively target genes at a high frequency in S. pombe. This finding greatly facilitates the introduction of exogenous genes in this organism.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"58 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140798721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation DockingGA：利用变压器神经网络和遗传算法进行对接模拟，提高靶向分子生成能力

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-04-07 DOI: 10.1093/bfgp/elae011

Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, Xun Wang

{"title":"DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation","authors":"Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, Xun Wang","doi":"10.1093/bfgp/elae011","DOIUrl":"https://doi.org/10.1093/bfgp/elae011","url":null,"abstract":"Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"69 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs 基于深度学习的长非编码 RNA 相互作用机制识别与预测综述

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-04-05 DOI: 10.1093/bfgp/elae010

Biyu Diao, Jin Luo, Yu Guo

{"title":"A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs","authors":"Biyu Diao, Jin Luo, Yu Guo","doi":"10.1093/bfgp/elae010","DOIUrl":"https://doi.org/10.1093/bfgp/elae010","url":null,"abstract":"Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body’s normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"69 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved hierarchical variational autoencoder for cell-cell communication estimation using single-cell RNA-seq data. 利用单细胞 RNA-seq 数据估算细胞间通讯的改进型分层变异自动编码器。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elac056

Shuhui Liu, Yupei Zhang, Jiajie Peng, Xuequn Shang

{"title":"An improved hierarchical variational autoencoder for cell-cell communication estimation using single-cell RNA-seq data.","authors":"Shuhui Liu, Yupei Zhang, Jiajie Peng, Xuequn Shang","doi":"10.1093/bfgp/elac056","DOIUrl":"10.1093/bfgp/elac056","url":null,"abstract":"Analysis of cell-cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. There have been many achievements and applications in inferring cell-cell communication based on the known interactions between molecules, such as ligands, receptors and extracellular matrix. However, the prior information is not quite adequate and only involves a fraction of cellular communications, producing many false-positive or false-negative results. To this end, we propose an improved hierarchical variational autoencoder (HiVAE) based model to fully use single-cell RNA-seq data for automatically estimating CCC. Specifically, the HiVAE model is used to learn the potential representation of cells on known ligand-receptor genes and all genes in single-cell RNA-seq data, respectively, which are then utilized for cascade integration. Subsequently, transfer entropy is employed to measure the transmission of information flow between two cells based on the learned representations, which are regarded as directed communication relationships. Experiments are conducted on single-cell RNA-seq data of the human skin disease dataset and the melanoma dataset, respectively. Results show that the HiVAE model is effective in learning cell representations, and transfer entropy could be used to estimate the communication scores between cell types.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"118-127"},"PeriodicalIF":4.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9222533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0