Briefings in Functional Genomics最新文献_第10页

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 2.5 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":2.5,"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 2.5 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":2.5,"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

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. 用于单细胞 RNA-seq 和空间解析转录组研究的差异表达分析的最新进展。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elad011

Xiya Guo, Jin Ning, Yuanze Chen, Guoliang Liu, Liyan Zhao, Yue Fan, Shiquan Sun

引用次数: 0

Single-cell RNA-seq data clustering by deep information fusion. 通过深度信息融合对单细胞 RNA-seq 数据进行聚类。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elad017

Liangrui Ren, Jun Wang, Wei Li, Maozu Guo, Guoxian Yu

{"title":"Single-cell RNA-seq data clustering by deep information fusion.","authors":"Liangrui Ren, Jun Wang, Wei Li, Maozu Guo, Guoxian Yu","doi":"10.1093/bfgp/elad017","DOIUrl":"10.1093/bfgp/elad017","url":null,"abstract":"Determining cell types by single-cell transcriptomics data is fundamental for downstream analysis. However, cell clustering and data imputation still face the computation challenges, due to the high dropout rate, sparsity and dimensionality of single-cell data. Although some deep learning based solutions have been proposed to handle these challenges, they still can not leverage gene attribute information and cell topology in a sensible way to explore the consistent clustering. In this paper, we present scDeepFC, a deep information fusion-based single-cell data clustering method for cell clustering and data imputation. Specifically, scDeepFC uses a deep auto-encoder (DAE) network and a deep graph convolution network to embed high-dimensional gene attribute information and high-order cell-cell topological information into different low-dimensional representations, and then fuses them to generate a more comprehensive and accurate consensus representation via a deep information fusion network. In addition, scDeepFC integrates the zero-inflated negative binomial (ZINB) into DAE to model the dropout events. By jointly optimizing the ZINB loss and cell graph reconstruction loss, scDeepFC generates a salient embedding representation for clustering cells and imputing missing data. Extensive experiments on real single-cell datasets prove that scDeepFC outperforms other popular single-cell analysis methods. Both the gene attribute and cell topology information can improve the cell clustering.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"128-137"},"PeriodicalIF":2.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9489133","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

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease. 整合功能评分和调控数据，预测非编码 SNPs 对一种复杂神经疾病的影响。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elad020

Daniela Felício, Miguel Alves-Ferreira, Mariana Santos, Marlene Quintas, Alexandra M Lopes, Carolina Lemos, Nádia Pinto, Sandra Martins

{"title":"Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease.","authors":"Daniela Felício, Miguel Alves-Ferreira, Mariana Santos, Marlene Quintas, Alexandra M Lopes, Carolina Lemos, Nádia Pinto, Sandra Martins","doi":"10.1093/bfgp/elad020","DOIUrl":"10.1093/bfgp/elad020","url":null,"abstract":"Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3' UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"138-149"},"PeriodicalIF":2.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9918600","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

Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer. 基于长线程测序的胰腺癌 DNA N6-甲基腺嘌呤修饰的功能特征

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elad021

Dianshuang Zhou, Shiwei Guo, Yangyang Wang, Jiyun Zhao, Honghao Liu, Feiyang Zhou, Yan Huang, Yue Gu, Gang Jin, Yan Zhang

{"title":"Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer.","authors":"Dianshuang Zhou, Shiwei Guo, Yangyang Wang, Jiyun Zhao, Honghao Liu, Feiyang Zhou, Yan Huang, Yue Gu, Gang Jin, Yan Zhang","doi":"10.1093/bfgp/elad021","DOIUrl":"10.1093/bfgp/elad021","url":null,"abstract":"Abnormalities of DNA modifications are closely related to the pathogenesis and prognosis of pancreatic cancer. The development of third-generation sequencing technology has brought opportunities for the study of new epigenetic modification in cancer. Here, we screened the N6-methyladenine (6mA) and 5-methylcytosine (5mC) modification in pancreatic cancer based on Oxford Nanopore Technologies sequencing. The 6mA levels were lower compared with 5mC and upregulated in pancreatic cancer. We developed a novel method to define differentially methylated deficient region (DMDR), which overlapped 1319 protein-coding genes in pancreatic cancer. Genes screened by DMDRs were more significantly enriched in the cancer genes compared with the traditional differential methylation method (P < 0.001 versus P = 0.21, hypergeometric test). We then identified a survival-related signature based on DMDRs (DMDRSig) that stratified patients into high- and low-risk groups. Functional enrichment analysis indicated that 891 genes were closely related to alternative splicing. Multi-omics data from the cancer genome atlas showed that these genes were frequently altered in cancer samples. Survival analysis indicated that seven genes with high expression (ADAM9, ADAM10, EPS8, FAM83A, FAM111B, LAMA3 and TES) were significantly associated with poor prognosis. In addition, the distinction for pancreatic cancer subtypes was determined using 46 subtype-specific genes and unsupervised clustering. Overall, our study is the first to explore the molecular characteristics of 6mA modifications in pancreatic cancer, indicating that 6mA has the potential to be a target for future clinical treatment.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"150-162"},"PeriodicalIF":2.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9588453","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