Briefings in Functional Genomics最新文献_第10页

High-level RNA editing diversifies the coleoid cephalopod brain proteome. 高水平的RNA编辑使coleoid头足类动物脑蛋白质组多样化

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-17 DOI: 10.1093/bfgp/elad034

Gjendine Voss, Joshua J C Rosenthal

{"title":"High-level RNA editing diversifies the coleoid cephalopod brain proteome.","authors":"Gjendine Voss, Joshua J C Rosenthal","doi":"10.1093/bfgp/elad034","DOIUrl":"10.1093/bfgp/elad034","url":null,"abstract":"Coleoid cephalopods (octopus, squid and cuttlefish) have unusually complex nervous systems. The coleoid nervous system is also the only one currently known to recode the majority of expressed proteins through A-to-I RNA editing. The deamination of adenosine by adenosine deaminase acting on RNA (ADAR) enzymes produces inosine, which is interpreted as guanosine during translation. If this occurs in an open reading frame, which is the case for tens of thousands of editing sites in coleoids, it can recode the encoded protein. Here, we describe recent findings aimed at deciphering the mechanisms underlying high-level recoding and its adaptive potential. We describe the complement of ADAR enzymes in cephalopods, including a recently discovered novel domain in sqADAR1. We further summarize current evidence supporting an adaptive role of high-level RNA recoding in coleoids, and review recent studies showing that a large proportion of recoding sites is temperature-sensitive. Despite these new findings, the mechanisms governing the high level of RNA recoding in coleoid cephalopods remain poorly understood. Recent advances using genome editing in squid may provide useful tools to further study A-to-I RNA editing in these animals.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43557580","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 genomics in Spiralia. 螺旋藻的功能基因组学研究

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-17 DOI: 10.1093/bfgp/elad036

Francisco M Martín-Zamora, Billie E Davies, Rory D Donnellan, Kero Guynes, José M Martín-Durán

{"title":"Functional genomics in Spiralia.","authors":"Francisco M Martín-Zamora, Billie E Davies, Rory D Donnellan, Kero Guynes, José M Martín-Durán","doi":"10.1093/bfgp/elad036","DOIUrl":"10.1093/bfgp/elad036","url":null,"abstract":"Our understanding of the mechanisms that modulate gene expression in animals is strongly biased by studying a handful of model species that mainly belong to three groups: Insecta, Nematoda and Vertebrata. However, over half of the animal phyla belong to Spiralia, a morphologically and ecologically diverse animal clade with many species of economic and biomedical importance. Therefore, investigating genome regulation in this group is central to uncovering ancestral and derived features in genome functioning in animals, which can also be of significant societal impact. Here, we focus on five aspects of gene expression regulation to review our current knowledge of functional genomics in Spiralia. Although some fields, such as single-cell transcriptomics, are becoming more common, the study of chromatin accessibility, DNA methylation, histone post-translational modifications and genome architecture are still in their infancy. Recent efforts to generate chromosome-scale reference genome assemblies for greater species diversity and optimise state-of-the-art approaches for emerging spiralian research systems will address the existing knowledge gaps in functional genomics in this animal group.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41607119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Spiralian genomics and the evolution of animal genome architecture. 螺旋体基因组学和动物基因组结构的进化。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-17 DOI: 10.1093/bfgp/elad029

Isabel Jiah-Yih Liao, Tsai-Ming Lu, Mu-En Chen, Yi-Jyun Luo

引用次数: 0

Emerging trends in functional genomics in Spiralia. 螺旋藻功能基因组学的新趋势。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-17 DOI: 10.1093/bfgp/elad048

José M Martín-Durán

引用次数: 0

Emerging questions on the mechanisms and dynamics of 3D genome evolution in spiralians. 螺旋体三维基因组进化机制和动力学的新问题。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-17 DOI: 10.1093/bfgp/elad043

Thea F Rogers, Oleg Simakov

引用次数: 0

Single-cell transcriptomics refuels the exploration of spiralian biology. 单细胞转录组学促进了对螺旋动物生物学的探索。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-17 DOI: 10.1093/bfgp/elad038

Laura Piovani, Ferdinand Marlétaz

{"title":"Single-cell transcriptomics refuels the exploration of spiralian biology.","authors":"Laura Piovani, Ferdinand Marlétaz","doi":"10.1093/bfgp/elad038","DOIUrl":"10.1093/bfgp/elad038","url":null,"abstract":"Spiralians represent the least studied superclade of bilaterian animals, despite exhibiting the widest diversity of organisms. Although spiralians include iconic organisms, such as octopus, earthworms and clams, a lot remains to be discovered regarding their phylogeny and biology. Here, we review recent attempts to apply single-cell transcriptomics, a new pioneering technology enabling the classification of cell types and the characterisation of their gene expression profiles, to several spiralian taxa. We discuss the methodological challenges and requirements for applying this approach to marine organisms and explore the insights that can be brought by such studies, both from a biomedical and evolutionary perspective. For instance, we show that single-cell sequencing might help solve the riddle of the homology of larval forms across spiralians, but also to better characterise and compare the processes of regeneration across taxa. We highlight the capacity of single-cell to investigate the origin of evolutionary novelties, as the mollusc shell or the cephalopod visual system, but also to interrogate the conservation of the molecular fingerprint of cell types at long evolutionary distances. We hope that single-cell sequencing will open a new window in understanding the biology of spiralians, and help renew the interest for these overlooked but captivating organisms.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10054238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning. Be-1DCNN:基于bagging集成学习的染色质环预测神经网络模型。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-10 DOI: 10.1093/bfgp/elad015

Hao Wu, Bing Zhou, Haoru Zhou, Pengyu Zhang, Meili Wang

{"title":"Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning.","authors":"Hao Wu, Bing Zhou, Haoru Zhou, Pengyu Zhang, Meili Wang","doi":"10.1093/bfgp/elad015","DOIUrl":"10.1093/bfgp/elad015","url":null,"abstract":"The chromatin loops in the three-dimensional (3D) structure of chromosomes are essential for the regulation of gene expression. Despite the fact that high-throughput chromatin capture techniques can identify the 3D structure of chromosomes, chromatin loop detection utilizing biological experiments is arduous and time-consuming. Therefore, a computational method is required to detect chromatin loops. Deep neural networks can form complex representations of Hi-C data and provide the possibility of processing biological datasets. Therefore, we propose a bagging ensemble one-dimensional convolutional neural network (Be-1DCNN) to detect chromatin loops from genome-wide Hi-C maps. First, to obtain accurate and reliable chromatin loops in genome-wide contact maps, the bagging ensemble learning method is utilized to synthesize the prediction results of multiple 1DCNN models. Second, each 1DCNN model consists of three 1D convolutional layers for extracting high-dimensional features from input samples and one dense layer for producing the prediction results. Finally, the prediction results of Be-1DCNN are compared to those of the existing models. The experimental results indicate that Be-1DCNN predicts high-quality chromatin loops and outperforms the state-of-the-art methods using the same evaluation metrics. The source code of Be-1DCNN is available for free at https://github.com/HaoWuLab-Bioinformatics/Be1DCNN.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9753347","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

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification. 基于关注的GCN整合了乳腺癌亚型分类和患者特异性基因标记识别的多组学数据。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-10 DOI: 10.1093/bfgp/elad013

Hui Guo, Xiang Lv, Yizhou Li, Menglong Li

{"title":"Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification.","authors":"Hui Guo, Xiang Lv, Yizhou Li, Menglong Li","doi":"10.1093/bfgp/elad013","DOIUrl":"10.1093/bfgp/elad013","url":null,"abstract":"Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9726805","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

Adaptive deep propagation graph neural network for predicting miRNA-disease associations. 预测mirna与疾病关联的自适应深度传播图神经网络。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-10 DOI: 10.1093/bfgp/elad010

Hua Hu, Huan Zhao, Tangbo Zhong, Xishang Dong, Lei Wang, Pengyong Han, Zhengwei Li

{"title":"Adaptive deep propagation graph neural network for predicting miRNA-disease associations.","authors":"Hua Hu, Huan Zhao, Tangbo Zhong, Xishang Dong, Lei Wang, Pengyong Han, Zhengwei Li","doi":"10.1093/bfgp/elad010","DOIUrl":"10.1093/bfgp/elad010","url":null,"abstract":"Background: A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations.Results: In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs.Conclusion: Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9385707","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: 1

RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features. rblight:一个利用光梯度增强机和进化特征集合发现植物特异性rna结合蛋白的计算工具。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2023-11-10 DOI: 10.1093/bfgp/elad016

Upendra K Pradhan, Prabina K Meher, Sanchita Naha, Soumen Pal, Sagar Gupta, Ajit Gupta, Rajender Parsad

{"title":"RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features.","authors":"Upendra K Pradhan, Prabina K Meher, Sanchita Naha, Soumen Pal, Sagar Gupta, Ajit Gupta, Rajender Parsad","doi":"10.1093/bfgp/elad016","DOIUrl":"10.1093/bfgp/elad016","url":null,"abstract":"RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic species, specifically from mice and humans. Although some models have been tested on Arabidopsis, these techniques fall short of correctly identifying RBPs for other plant species. Therefore, the development of a powerful computational model for identifying plant-specific RBPs is needed. In this study, we presented a novel computational model for locating RBPs in plants. Five deep learning models and ten shallow learning algorithms were utilized for prediction with 20 sequence-derived and 20 evolutionary feature sets. The highest repeated five-fold cross-validation accuracy, 91.24% AU-ROC and 91.91% AU-PRC, was achieved by light gradient boosting machine. While evaluated using an independent dataset, the developed approach achieved 94.00% AU-ROC and 94.50% AU-PRC. The proposed model achieved significantly higher accuracy for predicting plant-specific RBPs as compared to the currently available state-of-art RBP prediction models. Despite the fact that certain models have already been trained and assessed on the model organism Arabidopsis, this is the first comprehensive computer model for the discovery of plant-specific RBPs. The web server RBPLight was also developed, which is publicly accessible at https://iasri-sg.icar.gov.in/rbplight/, for the convenience of researchers to identify RBPs in plants.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9432913","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