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Guo Y, Zhou D, Li P, Li C, Cao J. Context-Aware Poly(A) Signal Prediction Model via Deep Spatial-Temporal Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:8241-8253. [PMID: 37015693 DOI: 10.1109/tnnls.2022.3226301] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Polyadenylation [Poly(A)] is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial-temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.
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Liu X, Zhang H, Zeng Y, Zhu X, Zhu L, Fu J. DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks. Genes (Basel) 2024; 15:404. [PMID: 38674339 PMCID: PMC11048956 DOI: 10.3390/genes15040404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
The precise identification of splice sites is essential for unraveling the structure and function of genes, constituting a pivotal step in the gene annotation process. In this study, we developed a novel deep learning model, DRANetSplicer, that integrates residual learning and attention mechanisms for enhanced accuracy in capturing the intricate features of splice sites. We constructed multiple datasets using the most recent versions of genomic data from three different organisms, Oryza sativa japonica, Arabidopsis thaliana and Homo sapiens. This approach allows us to train models with a richer set of high-quality data. DRANetSplicer outperformed benchmark methods on donor and acceptor splice site datasets, achieving an average accuracy of (96.57%, 95.82%) across the three organisms. Comparative analyses with benchmark methods, including SpliceFinder, Splice2Deep, Deep Splicer, EnsembleSplice, and DNABERT, revealed DRANetSplicer's superior predictive performance, resulting in at least a (4.2%, 11.6%) relative reduction in average error rate. We utilized the DRANetSplicer model trained on O. sativa japonica data to predict splice sites in A. thaliana, achieving accuracies for donor and acceptor sites of (94.89%, 94.25%). These results indicate that DRANetSplicer possesses excellent cross-organism predictive capabilities, with its performance in cross-organism predictions even surpassing that of benchmark methods in non-cross-organism predictions. Cross-organism validation showcased DRANetSplicer's excellence in predicting splice sites across similar organisms, supporting its applicability in gene annotation for understudied organisms. We employed multiple methods to visualize the decision-making process of the model. The visualization results indicate that DRANetSplicer can learn and interpret well-known biological features, further validating its overall performance. Our study systematically examined and confirmed the predictive ability of DRANetSplicer from various levels and perspectives, indicating that its practical application in gene annotation is justified.
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Affiliation(s)
- Xueyan Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Hongyan Zhang
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Ying Zeng
- School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China;
| | - Xinghui Zhu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Lei Zhu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Jiahui Fu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
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Zou Y, Guo Q, Chang Y, Zhong Y, Cheng L, Wei W. Alternative splicing affects synapses in the hippocampus of offspring after maternal fructose exposure during gestation and lactation. Chem Biol Interact 2023; 379:110518. [PMID: 37121297 DOI: 10.1016/j.cbi.2023.110518] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/15/2023] [Accepted: 04/27/2023] [Indexed: 05/02/2023]
Abstract
Increased fructose over-intake is a global issue. Maternal fructose exposure during gestation and lactation can impair brain development in offspring. However, the effect on synapses is still unknown. For the diversification of RNA and biological functions, alternative splicing (AS) and alternative polyadenylation (APA) are essential. We constructed a maternal high-fructose diet model by administering 13% and 40% fructose water. The student's t-test analyzed the results of RT-qPCR. All other results were analyzed by one-way analysis of variance. The animal behavior experiment results revealed that conditioning and associative memory had been damaged. The proteins that form synapses were consistently low-expressed. In addition, compared with the control group, the Oxford Nanopore Technologies platform's full-length RNA-sequencing identified 298 different spliced genes (DSGs) and 51 differentially expressed alternative splicing (DEAS) genes in the 13% fructose group. 313 DSGs and 74 DEAS genes were in the 40% fructose group. Enrichment analysis based on these altered genes revealed some enlightening items and pathways. Our findings demonstrated the transcriptome mechanism underlying maternal fructose exposure during gestation and lactation and impaired synapse function during the transcripts' editing.
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Affiliation(s)
- Yuchen Zou
- Child and Adolescent Health, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, PR China
| | - Qing Guo
- Child and Adolescent Health, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, PR China
| | - Yidan Chang
- Child and Adolescent Health, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, PR China
| | - Yongyong Zhong
- Child and Adolescent Health, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, PR China
| | - Lin Cheng
- Child and Adolescent Health, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, PR China
| | - Wei Wei
- Child and Adolescent Health, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, PR China.
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Guo Y, Shen H, Li W, Li C, Jin C. Deep Effective k-mer representation learning for polyadenylation signal prediction via co-occurrence embedding. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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5
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Ho L, Goethals P. Machine learning applications in river research: Trends, opportunities and challenges. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Long Ho
- Department of Animal Sciences and Aquatic Ecology Ghent University Ghent Belgium
| | - Peter Goethals
- Department of Animal Sciences and Aquatic Ecology Ghent University Ghent Belgium
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Ye W, Lian Q, Ye C, Wu X. A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00121-8. [PMID: 36167284 PMCID: PMC10372920 DOI: 10.1016/j.gpb.2022.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/17/2022] [Accepted: 09/19/2022] [Indexed: 05/08/2023]
Abstract
Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3' untranslated region, tissue-specific, cross-species, and single-cell pA prediction.
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Affiliation(s)
- Wenbin Ye
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
| | - Qiwei Lian
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China; Department of Automation, Xiamen University, Xiamen 361005, China
| | - Congting Ye
- Key Laboratory of the Coastal and Wetland Ecosystems, Ministry of Education, College of the Environment and Ecology, Xiamen University, Xiamen 361005, China
| | - Xiaohui Wu
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China.
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Context-aware dynamic neural computational models for accurate Poly(A) signal prediction. Neural Netw 2022; 152:287-299. [DOI: 10.1016/j.neunet.2022.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/03/2022] [Accepted: 04/22/2022] [Indexed: 11/21/2022]
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Yu H, Dai Z. SANPolyA: a deep learning method for identifying Poly(A) signals. Bioinformatics 2020; 36:2393-2400. [PMID: 31904817 DOI: 10.1093/bioinformatics/btz970] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 12/05/2019] [Accepted: 01/01/2020] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Polyadenylation plays a regulatory role in transcription. The recognition of polyadenylation signal (PAS) motif sequence is an important step in polyadenylation. In the past few years, some statistical machine learning-based and deep learning-based methods have been proposed for PAS identification. Although these methods predict PAS with success, there is room for their improvement on PAS identification. RESULTS In this study, we proposed a deep neural network-based computational method, called SANPolyA, for identifying PAS in human and mouse genomes. SANPolyA requires no manually crafted sequence features. We compared our method SANPolyA with several previous PAS identification methods on several PAS benchmark datasets. Our results showed that SANPolyA outperforms the state-of-art methods. SANPolyA also showed good performance on leave-one-motif-out evaluation. AVAILABILITY AND IMPLEMENTATION https://github.com/yuht4/SANPolyA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Zhiming Dai
- School of Data and Computer Science.,Guangdong Province Key Laboratory of Big Data Analysis and Processing, Sun Yat-Sen University, Guangzhou 510006, China
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Albaradei S, Magana-Mora A, Thafar M, Uludag M, Bajic VB, Gojobori T, Essack M, Jankovic BR. Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA. Gene 2020; 763S:100035. [PMID: 32550561 PMCID: PMC7285987 DOI: 10.1016/j.gene.2020.100035] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 05/06/2020] [Indexed: 12/21/2022]
Abstract
Background The accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes. Results With this in mind, we developed the Splice2Deep models for SS detection. Each model is an ensemble of deep convolutional neural networks. We evaluated the performance of the models based on the ability to detect SS in Homo sapiens, Oryza sativa japonica, Arabidopsis thaliana, Drosophila melanogaster, and Caenorhabditis elegans. Results demonstrate that the models efficiently detect SS in other organisms not considered during the training of the models. Compared to the state-of-the-art tools, Splice2Deep models achieved significantly reduced average error rates of 41.97% and 28.51% for acceptor and donor SS, respectively. Moreover, the Splice2Deep cross-organism validation demonstrates that models correctly identify conserved genomic elements enabling annotation of SS in new genomes by choosing the taxonomically closest model. Conclusions The results of our study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes. Splice2Deep models are implemented in Python using Keras API; the models and the data are available at https://github.com/SomayahAlbaradei/Splice_Deep.git.
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Key Words
- AUC, area under curve
- AcSS, acceptor splice site
- Acc, accuracy
- Bioinformatics
- CNN, convolutional neural network
- CONV, convolutional layers
- DL, deep learning
- DNA, deoxyribonucleic acid
- DT, decision trees
- Deep-learning
- DoSS, donor splice site
- FC, fully connected layer
- ML, machine learning
- NB, naive Bayes
- NN, neural network
- POOL, pooling layer
- Prediction
- RF, random forest
- RNA, ribonucleic acid
- ReLU, rectified linear unit layer
- SS, splice site
- SVM, support vector machine
- Sn, sensitivity
- Sp, specificity
- Splice sites
- Splicing
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Affiliation(s)
- Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia
| | - Arturo Magana-Mora
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Saudi Aramco, EXPEC-ARC, Drilling Technology Team, Dhahran 31311, Saudi Arabia
| | - Maha Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Faculty of Computers and Information Systems, Taif University, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Boris R Jankovic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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