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Hu W, Li Y, Wu Y, Guan L, Li M. A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding. iScience 2024; 27:110030. [PMID: 38868182 PMCID: PMC11167433 DOI: 10.1016/j.isci.2024.110030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/23/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Enhancers, genomic DNA elements, regulate neighboring gene expression crucial for biological processes like cell differentiation and stress response. However, current machine learning methods for predicting DNA enhancers often underutilize hidden features in gene sequences, limiting model accuracy. Hence, this article proposes the PDCNN model, a deep learning-based enhancer prediction method. PDCNN extracts statistical nucleotide representations from gene sequences, discerning positional distribution information of nucleotides in modifier-like DNA sequences. With a convolutional neural network structure, PDCNN employs dual convolutional and fully connected layers. The cross-entropy loss function iteratively updates using a gradient descent algorithm, enhancing prediction accuracy. Model parameters are fine-tuned to select optimal combinations for training, achieving over 95% accuracy. Comparative analysis with traditional methods and existing models demonstrates PDCNN's robust feature extraction capability. It outperforms advanced machine learning methods in identifying DNA enhancers, presenting an effective method with broad implications for genomics, biology, and medical research.
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Affiliation(s)
- Wenxing Hu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Yelin Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Yan Wu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
| | - Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China
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2
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Tenekeci S, Tekir S. Identifying promoter and enhancer sequences by graph convolutional networks. Comput Biol Chem 2024; 110:108040. [PMID: 38430611 DOI: 10.1016/j.compbiolchem.2024.108040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/09/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Identification of promoters, enhancers, and their interactions helps understand genetic regulation. This study proposes a graph-based semi-supervised learning model (GCN4EPI) for the enhancer-promoter classification problem. We adopt a graph convolutional network (GCN) architecture to integrate interaction information with sequence features. Nodes of the constructed graph hold word embeddings of DNA sequences while edges hold the Enhancer-Promoter Interaction (EPI) information. By means of semi-supervised learning, much less data (16%) and time are needed in model training. Comparisons on a benchmark dataset of six human cell lines show that the proposed approach outperforms the state-of-the-art methods by a large margin (10% higher F1 score) and has the fastest training time (up to 3 times). Moreover, GCN4EPI's performance on cross-cell line data is also better than the baselines (3% higher F1 score). Our qualitative analyses with graph explainability models prove that GCN4EPI learns from both text and graph structure. The results suggest that integrating interaction information with sequence features improves predictive performance and compensates for the number of training instances.
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Affiliation(s)
- Samet Tenekeci
- Department of Computer Engineering, Izmir Institute of Technology, Izmir, 35430, Turkiye
| | - Selma Tekir
- Department of Computer Engineering, Izmir Institute of Technology, Izmir, 35430, Turkiye.
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3
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Zou H. iDPPIV-SI: identifying dipeptidyl peptidase IV inhibitory peptides by using multiple sequence information. J Biomol Struct Dyn 2024; 42:2144-2152. [PMID: 37125813 DOI: 10.1080/07391102.2023.2203257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/10/2023] [Indexed: 05/02/2023]
Abstract
Currently, diabetes has become a great threaten for people's health in the world. Recent study shows that dipeptidyl peptidase IV (DPP-IV) inhibitory peptides may be a potential pharmaceutical agent to treat diabetes. Thus, there is a need to discriminate DPP-IV inhibitory peptides from non-DPP-IV inhibitory peptides. To address this issue, a novel computational model called iDPPIV-SI was developed in this study. In the first, 50 different types of physicochemical (PC) properties were employed to denote the peptide sequences. Three different feature descriptors including the 1-order, 2-order correlation methods and discrete wavelet transform were applied to collect useful information from the PC matrix. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was employed to select these most discriminative features. All of these chosen features were fed into support vector machine (SVM) for identifying DPP-IV inhibitory peptides. The iDPPIV-SI achieved 91.26% and 98.12% classification accuracies on the training and independent dataset, respectively. There is a significantly improvement in the classification performance by the proposed method, as compared with the state-of-the-art predictors. The datasets and MATLAB codes (based on MATLAB2015b) used in current study are available at https://figshare.com/articles/online_resource/iDPPIV-SI/20085878.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hongliang Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China
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4
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Zhang Y, Zhang P, Wu H. Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers. Brief Bioinform 2024; 25:bbae083. [PMID: 38485768 PMCID: PMC10938904 DOI: 10.1093/bib/bbae083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 01/27/2024] [Accepted: 02/07/2024] [Indexed: 03/18/2024] Open
Abstract
Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer-promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.
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Affiliation(s)
- Yao Zhang
- School of Software, Shandong University, Jinan, 250100, Shandong, China
| | - Pengyu Zhang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Hao Wu
- School of Software, Shandong University, Jinan, 250100, Shandong, China
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5
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Mehmood F, Arshad S, Shoaib M. ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction. Brief Bioinform 2024; 25:bbae030. [PMID: 38385876 PMCID: PMC10885011 DOI: 10.1093/bib/bbae030] [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: 09/20/2023] [Revised: 12/30/2023] [Accepted: 01/11/2024] [Indexed: 02/23/2024] Open
Abstract
Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.
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Affiliation(s)
- Faiza Mehmood
- Department of Computer Science, University of Engineering and Technology Lahore, (Faisalabad Campus) Pakistan
| | - Shazia Arshad
- Department of Computer Science, University of Engineering and Technology Lahore, 54890, Pakistan
| | - Muhammad Shoaib
- Department of Computer Science, University of Engineering and Technology Lahore, 54890, Pakistan
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6
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Liu R, Wang Q, Zhang X. Identification of prognostic coagulation-related signatures in clear cell renal cell carcinoma through integrated multi-omics analysis and machine learning. Comput Biol Med 2024; 168:107779. [PMID: 38061153 DOI: 10.1016/j.compbiomed.2023.107779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024]
Abstract
Clear cell renal cell carcinoma is a threat to public health with high morbidity and mortality. Clinical evidence has shown that cancer-associated thrombosis poses significant challenges to treatments, including drug resistance and difficulties in surgical decision-making in ccRCC. However, the coagulation pathway, one of the core mechanisms of cancer-associated thrombosis, recently found closely related to the tumor microenvironment and immune-related pathway, is rarely researched in ccRCC. Therefore, we integrated bulk RNA-seq data, DNA mutation and methylation data, single-cell data, and proteomic data to perform a comprehensive analysis of coagulation-related genes in ccRCC. First, we demonstrated the importance of the coagulation-related gene set by consensus clustering. Based on machine learning, we identified 5 coagulation signature genes and verified their clinical value in TCGA, ICGC, and E-MTAB-1980 databases. It's also demonstrated that the specific expression patterns of coagulation signature genes driven by CNV and methylation were closely correlated with pathways including apoptosis, immune infiltration, angiogenesis, and the construction of extracellular matrix. Moreover, we identified two types of tumor cells in single-cell data by machine learning, and the coagulation signature genes were differentially expressed in two types of tumor cells. Besides, the signature genes were proven to influence immune cells especially the differentiation of T cells. And their protein level was also validated.
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Affiliation(s)
- Ruijie Liu
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, China.
| | - Qi Wang
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, China.
| | - Xiaoping Zhang
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, China.
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7
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Li Z, Jin B, Fang J. MetaAc4C: A multi-module deep learning framework for accurate prediction of N4-acetylcytidine sites based on pre-trained bidirectional encoder representation and generative adversarial networks. Genomics 2024; 116:110749. [PMID: 38008265 DOI: 10.1016/j.ygeno.2023.110749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/05/2023] [Accepted: 11/21/2023] [Indexed: 11/28/2023]
Abstract
MOTIVATION N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in various biological processes. Accurately identifying ac4C sites is of paramount importance for gaining a deeper understanding of their regulatory mechanisms. Nevertheless, the existing experimental techniques for ac4C site identification are characterized by limitations in terms of cost-effectiveness, while the performance of current computational methods in accurately identifying ac4C sites requires further enhancement. RESULTS In this paper, we present MetaAc4C, an advanced deep learning model that leverages pre-trained bidirectional encoder representations from transformers (BERT). The model is based on a bi-directional long short-term memory network (BLSTM) architecture, incorporating attention mechanism and residual connection. To address the issue of data imbalance, we adapt generative adversarial networks to generate synthetic feature samples. On the independent test set, MetaAc4C surpasses the current state-of-the-art ac4C prediction model, exhibiting improvements in terms of ACC, MCC, and AUROC by 2.36%, 4.76%, and 3.11%, respectively, on the unbalanced dataset. When evaluated on the balanced dataset, MetaAc4C achieves improvements in ACC, MCC, and AUROC by 2.6%, 5.11%, and 1.01%, respectively. Notably, our approach of utilizing WGAN-GP augmented training RNA samples demonstrates even superior performance compared to the SMOTE oversampling method.
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Affiliation(s)
- Zutan Li
- College of Engineering, Westlake University, Hangzhou, China; College of Sciences, Nanjing Agricultural University, Nanjing, China
| | - Bingbing Jin
- College of Sciences, Nanjing Agricultural University, Nanjing, China
| | - Jingya Fang
- College of Science, China Pharmaceutical University, Nanjing, China.
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8
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Feng T, Hu T, Liu W, Zhang Y. Enhancer Recognition: A Transformer Encoder-Based Method with WGAN-GP for Data Augmentation. Int J Mol Sci 2023; 24:17548. [PMID: 38139375 PMCID: PMC10743946 DOI: 10.3390/ijms242417548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/29/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years, emerging methods, such as deep learning, have provided new insights for enhancing identification. However, these methods also present certain challenges. Deep learning models typically require a large amount of high-quality data, and data acquisition demands considerable time and resources. To address these challenges, in this paper, we propose a data-augmentation method based on generative adversarial networks to solve the problem of small datasets. Moreover, we used regularization methods such as weight decay to improve the generalizability of the model and alleviate overfitting. The Transformer encoder was used as the main component to capture the complex relationships and dependencies in enhancer sequences. The encoding layer was designed based on the principle of k-mers to preserve more information from the original DNA sequence. Compared with existing methods, the proposed approach made significant progress in enhancing the accuracy and strength of enhancer identification and prediction, demonstrating the effectiveness of the proposed method. This paper provides valuable insights for enhancer analysis and is of great significance for understanding gene regulatory mechanisms and studying disease correlations.
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Affiliation(s)
- Tianyu Feng
- College of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China; (T.F.); (T.H.)
| | - Tao Hu
- College of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China; (T.F.); (T.H.)
| | - Wenyu Liu
- College of Ecology, Lanzhou University, Lanzhou 730000, China;
| | - Yang Zhang
- Supercomputer Center, Lanzhou University, Lanzhou 730000, China
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Mir BA, Rehman MU, Tayara H, Chong KT. Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model. J Mol Biol 2023; 435:168314. [PMID: 37852600 DOI: 10.1016/j.jmb.2023.168314] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023]
Abstract
Enhancers are DNA regions that are responsible for controlling the expression of genes. Enhancers are usually found upstream or downstream of a gene, or even inside a gene's intron region, but are normally located at a distant location from the genes they control. By integrating experimental and computational approaches, it is possible to uncover enhancers within DNA sequences, which possess regulatory properties. Experimental techniques such as ChIP-seq and ATAC-seq can identify genomic regions that are associated with transcription factors or accessible to regulatory proteins. On the other hand, computational techniques can predict enhancers based on sequence features and epigenetic modifications. In our study, we have developed a multi-classifier stacked ensemble (MCSE-enhancer) model that can accurately identify enhancers. We utilized feature descriptors from various physiochemical properties as input for our six baseline classifiers and built a stacked classifier, which outperformed previous enhancer classification techniques in terms of accuracy, specificity, sensitivity, and Mathew's correlation coefficient. Our model achieved an accuracy of 81.5%, representing a 2-3% improvement over existing models.
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Affiliation(s)
- Bilal Ahmad Mir
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
| | - Mobeen Ur Rehman
- Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi 127788, United Arab Emirates.
| | - Hilal Tayara
- School of international Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea.
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Wang J, Zhang H, Chen N, Zeng T, Ai X, Wu K. PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks. Animals (Basel) 2023; 13:2935. [PMID: 37760334 PMCID: PMC10526013 DOI: 10.3390/ani13182935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/21/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Understanding the mechanisms of gene expression regulation is crucial in animal breeding. Cis-regulatory DNA sequences, such as enhancers, play a key role in regulating gene expression. Identifying enhancers is challenging, despite the use of experimental techniques and computational methods. Enhancer prediction in the pig genome is particularly significant due to the costliness of high-throughput experimental techniques. The study constructed a high-quality database of pig enhancers by integrating information from multiple sources. A deep learning prediction framework called PorcineAI-enhancer was developed for the prediction of pig enhancers. This framework employs convolutional neural networks for feature extraction and classification. PorcineAI-enhancer showed excellent performance in predicting pig enhancers, validated on an independent test dataset. The model demonstrated reliable prediction capability for unknown enhancer sequences and performed remarkably well on tissue-specific enhancer sequences.The study developed a deep learning prediction framework, PorcineAI-enhancer, for predicting pig enhancers. The model demonstrated significant predictive performance and potential for tissue-specific enhancers. This research provides valuable resources for future studies on gene expression regulation in pigs.
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Affiliation(s)
- Ji Wang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (J.W.); (H.Z.); (T.Z.); (X.A.)
| | - Han Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (J.W.); (H.Z.); (T.Z.); (X.A.)
| | - Nanzhu Chen
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China;
| | - Tong Zeng
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (J.W.); (H.Z.); (T.Z.); (X.A.)
| | - Xiaohua Ai
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (J.W.); (H.Z.); (T.Z.); (X.A.)
| | - Keliang Wu
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (J.W.); (H.Z.); (T.Z.); (X.A.)
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11
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Tang YJ, Yan K, Zhang X, Tian Y, Liu B. Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm. BMC Biol 2023; 21:188. [PMID: 37674132 PMCID: PMC10483879 DOI: 10.1186/s12915-023-01672-5] [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: 02/19/2023] [Accepted: 07/31/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Intrinsically disordered regions (IDRs) are widely distributed in proteins and related to many important biological functions. Accurately identifying IDRs is of great significance for protein structure and function analysis. Because the long disordered regions (LDRs) and short disordered regions (SDRs) share different characteristics, the existing predictors fail to achieve better and more stable performance on datasets with different ratios between LDRs and SDRs. There are two main reasons. First, the existing predictors construct network structures based on their own experiences such as convolutional neural network (CNN) which is used to extract the feature of neighboring residues in protein, and long short-term memory (LSTM) is used to extract the long-distance dependencies feature of protein residues. But these networks cannot capture the hidden feature associated with the length-dependent between residues. Second, many algorithms based on deep learning have been proposed but the complementarity of the existing predictors is not fully explored and used. RESULTS In this study, the neural architecture search (NAS) algorithm was employed to automatically construct the network structures so as to capture the hidden features in protein sequences. In order to stably predict both the LDRs and SDRs, the model constructed by NAS was combined with length-dependent models for capturing the unique features of SDRs or LDRs and general models for capturing the common features between LDRs and SDRs. A new predictor called IDP-Fusion was proposed. CONCLUSIONS Experimental results showed that IDP-Fusion can achieve more stable performance than the other existing predictors on independent test sets with different ratios between SDRs and LDRs.
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Affiliation(s)
- Yi-Jun Tang
- School of Computer Science and Technology, Beijing Institute of Technology, Haidian District, No. 5, South Zhongguancun Street, Beijing, 100081, China
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Haidian District, No. 5, South Zhongguancun Street, Beijing, 100081, China
| | - Xingyi Zhang
- School of Artificial Intelligence, Anhui University, Hefei, 230601, China
| | - Ye Tian
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Haidian District, No. 5, South Zhongguancun Street, Beijing, 100081, China.
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China.
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12
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Wang W, Wu Q, Li C. iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention. BMC Genomics 2023; 24:393. [PMID: 37442977 DOI: 10.1186/s12864-023-09468-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Due to the dynamic nature of enhancers, identifying enhancers and their strength are major bioinformatics challenges. With the development of deep learning, several models have facilitated enhancers detection in recent years. However, existing studies either neglect different length motifs information or treat the features at all spatial locations equally. How to effectively use multi-scale motifs information while ignoring irrelevant information is a question worthy of serious consideration. In this paper, we propose an accurate and stable predictor iEnhancer-DCSA, mainly composed of dual-scale fusion and spatial attention, automatically extracting features of different length motifs and selectively focusing on the important features. RESULTS Our experimental results demonstrate that iEnhancer-DCSA is remarkably superior to existing state-of-the-art methods on the test dataset. Especially, the accuracy and MCC of enhancer identification are improved by 3.45% and 9.41%, respectively. Meanwhile, the accuracy and MCC of enhancer classification are improved by 7.65% and 18.1%, respectively. Furthermore, we conduct ablation studies to demonstrate the effectiveness of dual-scale fusion and spatial attention. CONCLUSIONS iEnhancer-DCSA will be a valuable computational tool in identifying and classifying enhancers, especially for those not included in the training dataset.
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Affiliation(s)
- Wenjun Wang
- School of Software Engineering, South China University of Technology, Guangzhou, China
- School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China
- Key Laboratory of Big Data and Intelligent Robot, Ministry of Education, Guangzhou, China
| | - Qingyao Wu
- School of Software Engineering, South China University of Technology, Guangzhou, China.
- Pazhou Lab, Guangzhou, China.
- Peng Cheng Laboratory, Shenzhen, China.
| | - Chunshan Li
- Department of Computer Science and Technology, Harbin Institute of Technology, Weihai, China.
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13
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Phan LT, Oh C, He T, Manavalan B. A comprehensive revisit of the machine-learning tools developed for the identification of enhancers in the human genome. Proteomics 2023; 23:e2200409. [PMID: 37021401 DOI: 10.1002/pmic.202200409] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/18/2023] [Accepted: 03/27/2023] [Indexed: 04/07/2023]
Abstract
Enhancers are non-coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time-consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high-throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)-based prediction methods for enhancer identification and related databases has been provided. The existing enhancer-prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML-based predictors.
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Affiliation(s)
- Le Thi Phan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
| | - Changmin Oh
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
| | - Tao He
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
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14
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Grešová K, Martinek V, Čechák D, Šimeček P, Alexiou P. Genomic benchmarks: a collection of datasets for genomic sequence classification. BMC Genom Data 2023; 24:25. [PMID: 37127596 PMCID: PMC10150520 DOI: 10.1186/s12863-023-01123-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 03/31/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition. RESULTS Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package 'genomic-benchmarks', and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks . CONCLUSIONS Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.
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Affiliation(s)
- Katarína Grešová
- Centre for Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czechia
| | - Vlastimil Martinek
- Centre for Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czechia
| | - David Čechák
- Centre for Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czechia
| | - Petr Šimeček
- Centre for Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia.
| | - Panagiotis Alexiou
- Centre for Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
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15
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Ali F, Kumar H, Alghamdi W, Kateb FA, Alarfaj FK. Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-12. [PMID: 37359746 PMCID: PMC10148704 DOI: 10.1007/s11831-023-09933-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.
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Affiliation(s)
- Farman Ali
- Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Khyber Pakhtunkhwa, Pakistan
| | - Harish Kumar
- Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| | - Faris A. Kateb
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| | - Fawaz Khaled Alarfaj
- Department of Management Information Systems, King Faisal University, Hufof, Saudi Arabia
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16
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Wang C, Zou Q, Ju Y, Shi H. Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:967-975. [PMID: 36063523 DOI: 10.1109/tcbb.2022.3204365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Enhancers are crucial for precise regulation of gene expression, while enhancer identification and strength prediction are challenging because of their free distribution and tremendous number of similar fractions in the genome. Although several bioinformatics tools have been developed, shortfalls in these models remain, and their performances need further improvement. In the present study, a two-layer predictor called Enhancer-FRL was proposed for identifying enhancers (enhancers or nonenhancers) and their activities (strong and weak). More specifically, to build an efficient model, the feature representation learning scheme was applied to generate a 50D probabilistic vector based on 10 feature encodings and five machine learning algorithms. Subsequently, the multiview probabilistic features were integrated to construct the final prediction model. Compared with the single feature-based model, Enhancer-FRL showed significant performance improvement and model robustness. Performance assessment on the independent test dataset indicated that the proposed model outperformed state-of-the-art available toolkits. The webserver Enhancer-FRL is freely accessible at http://lab.malab.cn/∼wangchao/softwares/Enhancer-FRL/, The code and datasets can be downloaded at the webserver page or at the Github https://github.com/wangchao-malab/Enhancer-FRL/.
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17
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Zhu D, Yang W, Xu D, Li H, Zhao Y, Li D. A deep learning based two-layer predictor to identify enhancers and their strength. Methods 2023; 211:23-30. [PMID: 36740001 DOI: 10.1016/j.ymeth.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/03/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been developed for 30 years from the first generation to the third generation, and a variety of biological sequence data have increased significantly every year. Due to the importance of enhancer functions, it is very expensive to identify enhancers through biochemical experiments. Therefore, we need to study new methods for the identification and classification of enhancers. Based on the K-mer principle this study proposed a feature extraction method that others have not used in convolutional neural networks. Then, we combined it with one-hot encoding to build an efficient one-dimensional convolutional neural network ensemble model for predicting enhancers and their strengths. Finally, we used five commonly used classification problem evaluation indicators to compare with the models proposed by other researchers. The model proposed in this paper has a better performance by using the same independent test dataset as other models.
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Affiliation(s)
- Di Zhu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
| | - Dali Xu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Hongfei Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
| | - Dan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
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18
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Wu H, Liu M, Zhang P, Zhang H. iEnhancer-SKNN: a stacking ensemble learning-based method for enhancer identification and classification using sequence information. Brief Funct Genomics 2023; 22:302-311. [PMID: 36715222 DOI: 10.1093/bfgp/elac057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 12/01/2022] [Accepted: 12/13/2022] [Indexed: 01/31/2023] Open
Abstract
Enhancers, a class of distal cis-regulatory elements located in the non-coding region of DNA, play a key role in gene regulation. It is difficult to identify enhancers from DNA sequence data because enhancers are freely distributed in the non-coding region, with no specific sequence features, and having a long distance with the targeted promoters. Therefore, this study presents a stacking ensemble learning method to accurately identify enhancers and classify enhancers into strong and weak enhancers. Firstly, we obtain the fusion feature matrix by fusing the four features of Kmer, PseDNC, PCPseDNC and Z-Curve9. Secondly, five K-Nearest Neighbor (KNN) models with different parameters are trained as the base model, and the Logistic Regression algorithm is utilized as the meta-model. Thirdly, the stacking ensemble learning strategy is utilized to construct a two-layer model based on the base model and meta-model to train the preprocessed feature sets. The proposed method, named iEnhancer-SKNN, is a two-layer prediction model, in which the function of the first layer is to predict whether the given DNA sequences are enhancers or non-enhancers, and the function of the second layer is to distinguish whether the predicted enhancers are strong enhancers or weak enhancers. The performance of iEnhancer-SKNN is evaluated on the independent testing dataset and the results show that the proposed method has better performance in predicting enhancers and their strength. In enhancer identification, iEnhancer-SKNN achieves an accuracy of 81.75%, an improvement of 1.35% to 8.75% compared with other predictors, and in enhancer classification, iEnhancer-SKNN achieves an accuracy of 80.50%, an improvement of 5.5% to 25.5% compared with other predictors. Moreover, we identify key transcription factor binding site motifs in the enhancer regions and further explore the biological functions of the enhancers and these key motifs. Source code and data can be downloaded from https://github.com/HaoWuLab-Bioinformatics/iEnhancer-SKNN.
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Affiliation(s)
- Hao Wu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China.,School of Software, Shandong University, Jinan, 250101, Shandong, China
| | - Mengdi Liu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Pengyu Zhang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Hongming Zhang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China
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19
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Wang C, Zou Q. Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE. BMC Biol 2023; 21:12. [PMID: 36694239 PMCID: PMC9875434 DOI: 10.1186/s12915-023-01510-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work. RESULTS In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects. CONCLUSIONS DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .
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Affiliation(s)
- Chao Wang
- grid.411307.00000 0004 1790 5236School of Software Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Quan Zou
- grid.54549.390000 0004 0369 4060Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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20
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Li J, Wu Z, Lin W, Luo J, Zhang J, Chen Q, Chen J. iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models. BIOINFORMATICS ADVANCES 2023; 3:vbad043. [PMID: 37113248 PMCID: PMC10125906 DOI: 10.1093/bioadv/vbad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/04/2023] [Accepted: 03/24/2023] [Indexed: 04/29/2023]
Abstract
Motivation Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences. Results In this article, we propose a novel enhancer identification method (iEnhancer-ELM) based on BERT-like enhancer language models. iEnhancer-ELM tokenizes DNA sequences with multi-scale k-mers and extracts contextual information of different scale k-mers related with their positions via an multi-head attention mechanism. We first evaluate the performance of different scale k-mers, then ensemble them to improve the performance of enhancer identification. The experimental results on two popular benchmark datasets show that our model outperforms state-of-the-art methods. We further illustrate the interpretability of iEnhancer-ELM. For a case study, we discover 30 enhancer motifs via a 3-mer-based model, where 12 of motifs are verified by STREME and JASPAR, demonstrating our model has a potential ability to unveil the biological mechanism of enhancer. Availability and implementation The models and associated code are available at https://github.com/chen-bioinfo/iEnhancer-ELM. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | | | - Wenhao Lin
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Jiawei Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Jun Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Qingcai Chen
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
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21
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Li Y, Kong F, Cui H, Wang F, Li C, Ma J. SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:637-645. [PMID: 35015646 DOI: 10.1109/tcbb.2022.3142019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Identifying enhancers is a critical task in bioinformatics due to their primary role in regulating gene expression. For this reason, various computational algorithms devoted to enhancer identification have been put forward over the years. More features are extracted from the single DNA sequences to boost the performance. Nevertheless, DNA structural information is neglected, which is an essential factor affecting the binding preferences of transcription factors to regulatory elements like enhancers. Here, we propose SENIES, a DNA shape enhanced deep learning predictor, to identify enhancers and their strength. The predictor consists of two layers where the first layer is for enhancer and non-enhancer identification, and the second layer is for predicting the strength of enhancers. Apart from two common sequence-derived features (i.e., one-hot and k-mer), DNA shape is introduced to describe the 3D structures of DNA sequences. Performance comparison with state-of-the-art methods conducted on public datasets demonstrates the effectiveness and robustness of our predictor. The code implementation of SENIES is publicly available at https://github.com/hlju-liye/SENIES.
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22
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Jia J, Lei R, Qin L, Wu G, Wei X. iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module. Front Genet 2023; 14:1132018. [PMID: 36936423 PMCID: PMC10014624 DOI: 10.3389/fgene.2023.1132018] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Enhancers play a crucial role in controlling gene transcription and expression. Therefore, bioinformatics puts many emphases on predicting enhancers and their strength. It is vital to create quick and accurate calculating techniques because conventional biomedical tests take too long time and are too expensive. This paper proposed a new predictor called iEnhancer-DCSV built on a modified densely connected convolutional network (DenseNet) and an improved convolutional block attention module (CBAM). Coding was performed using one-hot and nucleotide chemical property (NCP). DenseNet was used to extract advanced features from raw coding. The channel attention and spatial attention modules were used to evaluate the significance of the advanced features and then input into a fully connected neural network to yield the prediction probabilities. Finally, ensemble learning was employed on the final categorization findings via voting. According to the experimental results on the test set, the first layer of enhancer recognition achieved an accuracy of 78.95%, and the Matthews correlation coefficient value was 0.5809. The second layer of enhancer strength prediction achieved an accuracy of 80.70%, and the Matthews correlation coefficient value was 0.6609. The iEnhancer-DCSV method can be found at https://github.com/leirufeng/iEnhancer-DCSV. It is easy to obtain the desired results without using the complex mathematical formulas involved.
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Affiliation(s)
- Jianhua Jia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
- *Correspondence: Jianhua Jia, ; Rufeng Lei,
| | - Rufeng Lei
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
- *Correspondence: Jianhua Jia, ; Rufeng Lei,
| | - Lulu Qin
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Genqiang Wu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Xin Wei
- Business School, Jiangxi Institute of Fashion Technology, Nanchang, China
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23
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Liang Y, Ma X. iACP-GE: accurate identification of anticancer peptides by using gradient boosting decision tree and extra tree. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:1-19. [PMID: 36562289 DOI: 10.1080/1062936x.2022.2160011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Cancer is one of the main diseases threatening human life, accounting for millions of deaths around the world each year. Traditional physical and chemical methods for cancer treatment are extremely time-consuming, lab-intensive, expensive, inefficient and difficult to be applied in a high-throughput way. Hence, it is an urgent task to develop automated computational methods to enable fast and accurate identification of anticancer peptides (ACPs). In this paper, we develop a novel model named iACP-GE to identify ACPs. Multi-features are extracted by using binary encoding, enhanced grouped amino acid composition and BLOSUM62 encoding based on the N5C5 sequence, as well as detrended forward moving-average auto-cross correlation analysis based on physicochemical properties of 20 natural amino acids. Thus, 835 features are obtained for each sample, in order to avoid information redundancy, gradient boosting decision tree was adopted as the feature selection strategy. Then, the optimal feature subset is input to the extra tree classifier. The accuracies of ACP740 and ACP240 datasets with the 5-fold cross-validation were 90.54% and 91.25%, respectively. Experimental results indicate that iACP-GE significantly outperforms several existing models on ACP740 and ACP240 datasets and can be used as an effective tool for the identification of ACPs. The datasets and source codes for iACP-GE are available at https://github.com/yunyunliang88/iACP-GE.
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Affiliation(s)
- Y Liang
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
| | - X Ma
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
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24
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Yang TH, Yu YH, Wu SH, Zhang FY. CFA: An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes. Comput Biol Med 2023; 152:106375. [PMID: 36502693 DOI: 10.1016/j.compbiomed.2022.106375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/07/2022] [Accepted: 11/27/2022] [Indexed: 11/30/2022]
Abstract
Metazoa gene expression is controlled by modular DNA segments called cis-regulatory modules (CRMs). CRMs can convey promoter/enhancer/insulator roles, generating additional regulation layers in transcription. Experiments for understanding CRM roles are low-throughput and costly. Large-scale CRM function investigation still depends on computational methods. However, existing in silico tools only recognize enhancers or promoters exclusively, thus accumulating errors when considering CRM promoter/enhancer/insulator roles altogether. Currently, no algorithm can concurrently consider these CRM roles. In this research, we developed the CRM Function Annotator (CFA) model. CFA provides complete CRM transcriptional role labeling based on epigenetic profiling interpretation. We demonstrated that CFA achieves high performance (test macro auROC/auPRC = 94.1%/90.3%) and outperforms existing tools in promoter/enhancer/insulator identification. CFA is also inspected to recognize explainable epigenetic codes consistent with previous findings when labeling CRM roles. By considering the higher-order combinations of the epigenetic codes, CFA significantly reduces false-positive rates in CRM transcriptional role annotation. CFA is available at https://github.com/cobisLab/CFA/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan.
| | - Yu-Huai Yu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Sheng-Hang Wu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Fang-Yuan Zhang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
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25
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Aladhadh S, Almatroodi SA, Habib S, Alabdulatif A, Khattak SU, Islam M. An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition. Biomolecules 2022; 13:biom13010070. [PMID: 36671456 PMCID: PMC9855522 DOI: 10.3390/biom13010070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
Enhancers are sequences with short motifs that exhibit high positional variability and free scattering properties. Identification of these noncoding DNA fragments and their strength are extremely important because they play a key role in controlling gene regulation on a cellular basis. The identification of enhancers is more complex than that of other factors in the genome because they are freely scattered, and their location varies widely. In recent years, bioinformatics tools have enabled significant improvement in identifying this biological difficulty. Cell line-specific screening is not possible using these existing computational methods based solely on DNA sequences. DNA segment chromatin accessibility may provide useful information about its potential function in regulation, thereby identifying regulatory elements based on its chromatin accessibility. In chromatin, the entanglement structure allows positions far apart in the sequence to encounter each other, regardless of their proximity to the gene to be acted upon. Thus, identifying enhancers and assessing their strength is difficult and time-consuming. The goal of our work was to overcome these limitations by presenting a convolutional neural network (CNN) with attention-gated recurrent units (AttGRU) based on Deep Learning. It used a CNN and one-hot coding to build models, primarily to identify enhancers and secondarily to classify their strength. To test the performance of the proposed model, parallels were drawn between enhancer-CNNAttGRU and existing state-of-the-art methods to enable comparisons. The proposed model performed the best for predicting stage one and stage two enhancer sequences, as well as their strengths, in a cross-species analysis, achieving best accuracy values of 87.39% and 84.46%, respectively. Overall, the results showed that the proposed model provided comparable results to state-of-the-art models, highlighting its usefulness.
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Affiliation(s)
- Suliman Aladhadh
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
- Correspondence:
| | - Saleh A. Almatroodi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Shabana Habib
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Abdulatif Alabdulatif
- Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Saeed Ullah Khattak
- Centre of Biotechnology and Microbiology, University of Peshawar, Peshawar 25120, Pakistan
| | - Muhammad Islam
- Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia
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26
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Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework. PLoS Comput Biol 2022; 18:e1010779. [PMID: 36520922 PMCID: PMC9836277 DOI: 10.1371/journal.pcbi.1010779] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 01/12/2023] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
Enhancers are short non-coding DNA sequences outside of the target promoter regions that can be bound by specific proteins to increase a gene's transcriptional activity, which has a crucial role in the spatiotemporal and quantitative regulation of gene expression. However, enhancers do not have a specific sequence motifs or structures, and their scattered distribution in the genome makes the identification of enhancers from human cell lines particularly challenging. Here we present a novel, stacked multivariate fusion framework called SMFM, which enables a comprehensive identification and analysis of enhancers from regulatory DNA sequences as well as their interpretation. Specifically, to characterize the hierarchical relationships of enhancer sequences, multi-source biological information and dynamic semantic information are fused to represent regulatory DNA enhancer sequences. Then, we implement a deep learning-based sequence network to learn the feature representation of the enhancer sequences comprehensively and to extract the implicit relationships in the dynamic semantic information. Ultimately, an ensemble machine learning classifier is trained based on the refined multi-source features and dynamic implicit relations obtained from the deep learning-based sequence network. Benchmarking experiments demonstrated that SMFM significantly outperforms other existing methods using several evaluation metrics. In addition, an independent test set was used to validate the generalization performance of SMFM by comparing it to other state-of-the-art enhancer identification methods. Moreover, we performed motif analysis based on the contribution scores of different bases of enhancer sequences to the final identification results. Besides, we conducted interpretability analysis of the identified enhancer sequences based on attention weights of EnhancerBERT, a fine-tuned BERT model that provides new insights into exploring the gene semantic information likely to underlie the discovered enhancers in an interpretable manner. Finally, in a human placenta study with 4,562 active distal gene regulatory enhancers, SMFM successfully exposed tissue-related placental development and the differential mechanism, demonstrating the generalizability and stability of our proposed framework.
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iEnhancer-MRBF: Identifying enhancers and their strength with a multiple Laplacian-regularized radial basis function network. Methods 2022; 208:1-8. [DOI: 10.1016/j.ymeth.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 11/07/2022] Open
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Liao M, Zhao JP, Tian J, Zheng CH. iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework. BMC Bioinformatics 2022; 23:480. [PMCID: PMC9664816 DOI: 10.1186/s12859-022-05033-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractEnhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement structure of chromatin allows the positions far apart in the sequence to have the opportunity to contact each other. Therefore, identifying enhancers and their strength is a complex and challenging task. In this article, a new prediction method based on deep learning is proposed to identify enhancers and enhancer strength, called iEnhancer-DCLA. Firstly, we use word2vec to convert k-mers into number vectors to construct an input matrix. Secondly, we use convolutional neural network and bidirectional long short-term memory network to extract sequence features, and finally use the attention mechanism to extract relatively important features. In the task of predicting enhancers and their strengths, this method has improved to a certain extent in most evaluation indexes. In summary, we believe that this method provides new ideas in the analysis of enhancers.
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Cui Z, Chen ZH, Zhang QH, Gribova V, Filaretov VF, Huang DS. RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3663-3672. [PMID: 34699364 DOI: 10.1109/tcbb.2021.3122183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The abuse of traditional antibiotics has led to an increase in the resistance of bacteria and viruses. Similar to the function of antibacterial peptides, bacteriocins are more common as a kind of peptides produced by bacteria that have bactericidal or bacterial effects. More importantly, the marine environment is one of the most abundant resources for extracting marine microbial bacteriocins (MMBs). Identifying bacteriocins from marine microorganisms is a common goal for the development of new drugs. Effective use of MMBs will greatly alleviate the current antibiotic abuse problem. In this work, deep learning is used to identify meaningful MMBs. We propose a random multi-scale convolutional neural network method. In the scale setting, we set a random model to update the scale value randomly. The scale selection method can reduce the contingency caused by artificial setting under certain conditions, thereby making the method more extensive. The results show that the classification performance of the proposed method is better than the state-of-the-art classification methods. In addition, some potential MMBs are predicted, and some different sequence analyses are performed on these candidates. It is worth mentioning that after sequence analysis, the HNH endonucleases of different marine bacteria are considered as potential bacteriocins.
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Liu S, Xu X, Yang Z, Zhao X, Liu S, Zhang W. EPIHC: Improving Enhancer-Promoter Interaction Prediction by Using Hybrid Features and Communicative Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3435-3443. [PMID: 34473626 DOI: 10.1109/tcbb.2021.3109488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, which help facilitate understanding of gene regulation, cell differentiation and disease mechanisms. EPI identification approaches through wet experiments are often costly and time-consuming, leading to the design of high-efficiency computational methods is in demand. In this paper, we propose a deep neural network-based method named EPIHC to predict Enhancer-Promoter Interactions with Hybrid features and Communicative learning. EPIHC extracts enhancer and promoter sequence-derived features using convolutional neural networks (CNN), and then we design a communicative learning module to capture the communicative information between enhancer and promoter sequences. Besides, EPIHC takes the genomic features of enhancers and promoters into account, incorporating with the sequence-derived features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveals that the communicative learning module can bring explicit information about EPIs, which is ignored by CNN, and provide explainability about EPIs to some degree. Moreover, we consider two strategies to improve the performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on some cell lines can exhibit good performances for other cell lines. The codes and data are available at https://github.com/BioMedicalBigDataMiningLab/EPIHC.
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Butt AH, Alkhalifah T, Alturise F, Khan YD. A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns. Sci Rep 2022; 12:15183. [PMID: 36071071 PMCID: PMC9452539 DOI: 10.1038/s41598-022-19099-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/24/2022] [Indexed: 11/26/2022] Open
Abstract
Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. Given their distance from the target gene, lack of common motifs, and tissue/cell specificity, enhancer regions are thought to be difficult to predict in DNA sequences. Recently, a number of bioinformatics tools were created to distinguish enhancers from other regulatory components and to pinpoint their advantages. However, because the quality of its prediction method needs to be improved, its practical application value must also be improved. Based on nucleotide composition and statistical moment-based features, the current study suggests a novel method for identifying enhancers and non-enhancers and evaluating their strength. The proposed study outperformed state-of-the-art techniques using fivefold and tenfold cross-validation in terms of accuracy. The accuracy from the current study results in 86.5% and 72.3% in enhancer site and its strength prediction respectively. The results of the suggested methodology point to the potential for more efficient and successful outcomes when statistical moment-based features are used. The current study's source code is available to the research community at https://github.com/csbioinfopk/enpred.
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Affiliation(s)
- Ahmad Hassan Butt
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi Arabia.
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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Cross-species enhancer prediction using machine learning. Genomics 2022; 114:110454. [PMID: 36030022 DOI: 10.1016/j.ygeno.2022.110454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/28/2022] [Accepted: 08/16/2022] [Indexed: 11/21/2022]
Abstract
Cis-regulatory elements (CREs) are non-coding parts of the genome that play a critical role in gene expression regulation. Enhancers, as an important example of CREs, interact with genes to influence complex traits like disease, heat tolerance and growth rate. Much of what is known about enhancers come from studies of humans and a few model organisms like mouse, with little known about other mammalian species. Previous studies have attempted to identify enhancers in less studied mammals using comparative genomics but with limited success. Recently, Machine Learning (ML) techniques have shown promising results to predict enhancer regions. Here, we investigated the ability of ML methods to identify enhancers in three non-model mammalian species (cattle, pig and dog) using human and mouse enhancer data from VISTA and publicly available ChIP-seq. We tested nine models, using four different representations of the DNA sequences in cross-species prediction using both the VISTA dataset and species-specific ChIP-seq data. We identified between 809,399 and 877,278 enhancer-like regions (ELRs) in the study species (11.6-13.7% of each genome). These predictions were close to the ~8% proportion of ELRs that covered the human genome. We propose that our ML methods have predictive ability for identifying enhancers in non-model mammalian species. We have provided a list of high confidence enhancers at https://github.com/DaviesCentreInformatics/Cross-species-enhancer-prediction and believe these enhancers will be of great use to the community.
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Zeng L, Liu Y, Yu ZG, Liu Y. iEnhancer-DLRA: identification of enhancers and their strengths by a self-attention fusion strategy for local and global features. Brief Funct Genomics 2022; 21:399-407. [PMID: 35942693 DOI: 10.1093/bfgp/elac023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 11/14/2022] Open
Abstract
Identification and classification of enhancers are highly significant because they play crucial roles in controlling gene transcription. Recently, several deep learning-based methods for identifying enhancers and their strengths have been developed. However, existing methods are usually limited because they use only local or only global features. The combination of local and global features is critical to further improve the prediction performance. In this work, we propose a novel deep learning-based method, called iEnhancer-DLRA, to identify enhancers and their strengths. iEnhancer-DLRA extracts local and multi-scale global features of sequences by using a residual convolutional network and two bidirectional long short-term memory networks. Then, a self-attention fusion strategy is proposed to deeply integrate these local and global features. The experimental results on the independent test dataset indicate that iEnhancer-DLRA performs better than nine existing state-of-the-art methods in both identification and classification of enhancers in almost all metrics. iEnhancer-DLRA achieves 13.8% (for identifying enhancers) and 12.6% (for classifying strengths) improvement in accuracy compared with the best existing state-of-the-art method. This is the first time that the accuracy of an enhancer identifier exceeds 0.9 and the accuracy of the enhancer classifier exceeds 0.8 on the independent test set. Moreover, iEnhancer-DLRA achieves superior predictive performance on the rice dataset compared with the state-of-the-art method RiceENN.
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Affiliation(s)
- Li Zeng
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, China
| | - Yang Liu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, China
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
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Njoroge H, van't Hof A, Oruni A, Pipini D, Nagi S, Lynd A, Lucas ER, Tomlinson S, Grau‐Bove X, McDermott D, Wat'senga FT, Manzambi EZ, Agossa FR, Mokuba A, Irish S, Kabula B, Mbogo C, Bargul J, Paine MJI, Weetman D, Donnelly MJ. Identification of a rapidly-spreading triple mutant for high-level metabolic insecticide resistance in Anopheles gambiae provides a real-time molecular diagnostic for antimalarial intervention deployment. Mol Ecol 2022; 31:4307-4318. [PMID: 35775282 PMCID: PMC9424592 DOI: 10.1111/mec.16591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 06/07/2022] [Accepted: 06/27/2022] [Indexed: 12/01/2022]
Abstract
Studies of insecticide resistance provide insights into the capacity of populations to show rapid evolutionary responses to contemporary selection. Malaria control remains heavily dependent on pyrethroid insecticides, primarily in long lasting insecticidal nets (LLINs). Resistance in the major malaria vectors has increased in concert with the expansion of LLIN distributions. Identifying genetic mechanisms underlying high-level resistance is crucial for the development and deployment of resistance-breaking tools. Using the Anopheles gambiae 1000 genomes (Ag1000g) data we identified a very recent selective sweep in mosquitoes from Uganda which localized to a cluster of cytochrome P450 genes. Further interrogation revealed a haplotype involving a trio of mutations, a nonsynonymous point mutation in Cyp6p4 (I236M), an upstream insertion of a partial Zanzibar-like transposable element (TE) and a duplication of the Cyp6aa1 gene. The mutations appear to have originated recently in An. gambiae from the Kenya-Uganda border, with stepwise replacement of the double-mutant (Zanzibar-like TE and Cyp6p4-236 M) with the triple-mutant haplotype (including Cyp6aa1 duplication), which has spread into the Democratic Republic of Congo and Tanzania. The triple-mutant haplotype is strongly associated with increased expression of genes able to metabolize pyrethroids and is strongly predictive of resistance to pyrethroids most notably deltamethrin. Importantly, there was increased mortality in mosquitoes carrying the triple-mutation when exposed to nets cotreated with the synergist piperonyl butoxide (PBO). Frequencies of the triple-mutant haplotype remain spatially variable within countries, suggesting an effective marker system to guide deployment decisions for limited supplies of PBO-pyrethroid cotreated LLINs across African countries.
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Affiliation(s)
- Harun Njoroge
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
- Kenya Medical Research Institute (KEMRI) Centre for Geographic Medicine CoastKEMRI‐Wellcome Trust Research ProgrammeKilifiKenya
| | - Arjen van't Hof
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Ambrose Oruni
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
- College of Veterinary MedicineAnimal Resources and Bio‐securityMakerere UniversityKampalaUganda
| | - Dimitra Pipini
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Sanjay C. Nagi
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Amy Lynd
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Eric R. Lucas
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Sean Tomlinson
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Xavi Grau‐Bove
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Daniel McDermott
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | | | - Emile Z. Manzambi
- Institut National de Recherche BiomédicaleKinshasaDemocratic Republic of Congo
| | - Fiacre R. Agossa
- USAID President's Malaria Initiative, VectorLink Project, Abt AssociatesRockvilleMarylandUSA
| | - Arlette Mokuba
- USAID President's Malaria Initiative, VectorLink Project, Abt AssociatesRockvilleMarylandUSA
| | - Seth Irish
- U.S. President's Malaria Initiative and Centers for Disease Control and PreventionAtlantaGeorgiaUSA
| | - Bilali Kabula
- Amani Research CentreNational Institute for Medical ResearchTanzania
| | - Charles Mbogo
- Population Health UnitKEMRI‐Wellcome Trust Research ProgrammeNairobiKenya
- KEMRI‐Centre for Geographic Medicine Research CoastKilifiKenya
| | - Joel Bargul
- Department of BiochemistryJomo Kenyatta University of Agriculture and TechnologyJujaKenya
- The Animal Health DepartmentInternational Centre of Insect Physiology and EcologyNairobiKenya
| | - Mark J. I. Paine
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - David Weetman
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Martin J. Donnelly
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
- Parasites and Microbes ProgrammeWellcome Sanger InstituteCambridgeUK
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Huang G, Luo W, Zhang G, Zheng P, Yao Y, Lyu J, Liu Y, Wei DQ. Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition. Biomolecules 2022; 12:biom12070995. [PMID: 35883552 PMCID: PMC9313278 DOI: 10.3390/biom12070995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/03/2022] [Accepted: 07/07/2022] [Indexed: 01/27/2023] Open
Abstract
Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers.
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Affiliation(s)
- Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
- Correspondence:
| | - Wei Luo
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Guiyang Zhang
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Peijie Zheng
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China;
| | - Jianyi Lyu
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (W.L.); (G.Z.); (P.Z.); (J.L.)
| | - Yuewu Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410083, China;
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China;
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36
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An Effective Deep Learning-Based Architecture for Prediction of N7-Methylguanosine Sites in Health Systems. ELECTRONICS 2022. [DOI: 10.3390/electronics11121917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
N7-methylguanosine (m7G) is one of the most important epigenetic modifications found in rRNA, mRNA, and tRNA, and performs a promising role in gene expression regulation. Owing to its significance, well-equipped traditional laboratory-based techniques have been performed for the identification of N7-methylguanosine (m7G). Consequently, these approaches were found to be time-consuming and cost-ineffective. To move on from these traditional approaches to predict N7-methylguanosine sites with high precision, the concept of artificial intelligence has been adopted. In this study, an intelligent computational model called N7-methylguanosine-Long short-term memory (m7G-LSTM) is introduced for the prediction of N7-methylguanosine sites. One-hot encoding and word2vec feature schemes are used to express the biological sequences while the LSTM and CNN algorithms have been employed for classification. The proposed “m7G-LSTM” model obtained an accuracy value of 95.95%, a specificity value of 95.94%, a sensitivity value of 95.97%, and Matthew’s correlation coefficient (MCC) value of 0.919. The proposed predictive m7G-LSTM model has significantly achieved better outcomes than previous models in terms of all evaluation parameters. The proposed m7G-LSTM computational system aims to support the drug industry and help researchers in the fields of bioinformatics to enhance innovation for the prediction of the behavior of N7-methylguanosine sites.
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37
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Gao Y, Chen Y, Feng H, Zhang Y, Yue Z. RicENN: Prediction of Rice Enhancers with Neural Network Based on DNA Sequences. Interdiscip Sci 2022; 14:555-565. [PMID: 35190950 DOI: 10.1007/s12539-022-00503-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 01/07/2022] [Accepted: 01/18/2022] [Indexed: 01/22/2023]
Abstract
Enhancers are the primary cis-elements of transcriptional regulation and play a vital role in gene expression at different stages of plant growth and development. Having high locational variation and free scattering in non-encoding genomes, identification of enhancers is a crucial, but challenging work in understanding the biological mechanism of model plants. Recently, applications of neural network models are gaining increasing popularity in predicting the function of genomic elements. Although several computational models have shown great advantages to tackle this challenge, a further study of the identification of rice enhancers from DNA sequences is still lacking. We present RicENN, a novel deep learning framework capable of accurately identifying enhancers of rice, integrating convolution neural networks (CNNs), bi-directional recurrent neural networks (RNNs), and attention mechanisms. A combined-feature representation method was designed to extract the sequence features from original DNA sequences using six types of autocorrelation encodings. Moreover, we verified that the integrated model achieves the best performance by an ablation study. Finally, our deep learning framework realized a reliable prediction of the rice enhancers. The results show RicENN outperforms available alternative approaches in rice species, achieving the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) of 0.960 and 0.960 on cross-validation, and 0.879 and 0.877 during independent tests, respectively. This study develops a hybrid model to combine the merits of different neural network architectures, which shows the potential ability to apply deep learning in bioinformatic sequences and contributes to the acceleration of functional genomic studies of rice. RicENN and its code are freely accessible at http://bioinfor.aielab.cc/RicENN/ .
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Affiliation(s)
- Yujia Gao
- School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Yiqiong Chen
- School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Haisong Feng
- School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Youhua Zhang
- School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China.
| | - Zhenyu Yue
- School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China.
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38
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Geng Q, Yang R, Zhang L. A deep learning framework for enhancer prediction using word embedding and sequence generation. Biophys Chem 2022; 286:106822. [DOI: 10.1016/j.bpc.2022.106822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/21/2022] [Accepted: 04/29/2022] [Indexed: 11/28/2022]
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Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7518779. [PMID: 35422876 PMCID: PMC9005296 DOI: 10.1155/2022/7518779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/12/2022] [Indexed: 11/17/2022]
Abstract
Enhancers are a class of noncoding DNA elements located near structural genes. In recent years, their identification and classification have been the focus of research in the field of bioinformatics. However, due to their high free scattering and position variability, although the performance of the prediction model has been continuously improved, there is still a lot of room for progress. In this paper, density-based spatial clustering of applications with noise (DBSCAN) was used to screen the physicochemical properties of dinucleotides to extract dinucleotide-based auto-cross covariance (DACC) features; then, the features are reduced by feature selection Python toolkit MRMD 2.0. The reduced features are input into the random forest to identify enhancers. The enhancer classification model was built by word2vec and attention-based Bi-LSTM. Finally, the accuracies of our enhancer identification and classification models were 77.25% and 73.50%, respectively, and the Matthews’ correlation coefficients (MCCs) were 0.5470 and 0.4881, respectively, which were better than the performance of most predictors.
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40
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Amilpur S, Bhukya R. A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature extraction. J Bioinform Comput Biol 2022; 20:2250005. [PMID: 35264081 DOI: 10.1142/s0219720022500056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Enhancers are short regulatory DNA fragments that are bound with proteins called activators. They are free-bound and distant elements, which play a vital role in controlling gene expression. It is challenging to identify enhancers and their strength due to their dynamic nature. Although some machine learning methods exist to accelerate identification process, their prediction accuracy and efficiency will need more improvement. In this regard, we propose a two-layer prediction model with enhanced feature extraction strategy which does feature combination from improved position-specific amino acid propensity (PSTKNC) method along with Enhanced Nucleic Acid Composition (ENAC) and Composition of k-spaced Nucleic Acid Pairs (CKSNAP). The feature sets from all three feature extraction approaches were concatenated and then sent through a simple artificial neural network (ANN) to accurately identify enhancers in the first layer and their strength in the second layer. Experiments are conducted on benchmark chromatin nine cell lines dataset. A 10-fold cross validation method is employed to evaluate model's performance. The results show that the proposed model gives an outstanding performance with 94.50%, 0.8903 of accuracy and Matthew's correlation coefficient (MCC) in predicting enhancers and fairly does well with independent test also when compared with all other existing methods.
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Affiliation(s)
- Santhosh Amilpur
- Computer Science and Engineering, National Institute of Technology Warangal, Warangal Telangana 506004, India
| | - Raju Bhukya
- Computer Science and Engineering, National Institute of Technology Warangal, Warangal Telangana 506004, India
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41
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Shen Z, Zhang Q, Han K, Huang DS. A Deep Learning Model for RNA-Protein Binding Preference Prediction Based on Hierarchical LSTM and Attention Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:753-762. [PMID: 32750884 DOI: 10.1109/tcbb.2020.3007544] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Attention mechanism has the ability to find important information in the sequence. The regions of the RNA sequence that can bind to proteins are more important than those that cannot bind to proteins. Neither conventional methods nor deep learning-based methods, they are not good at learning this information. In this study, LSTM is used to extract the correlation features between different sites in RNA sequence. We also use attention mechanism to evaluate the importance of different sites in RNA sequence. We get the optimal combination of k-mer length, k-mer stride window, k-mer sentence length, k-mer sentence stride window, and optimization function through hyper-parm experiments. The results show that the performance of our method is better than other methods. We tested the effects of changes in k-mer vector length on model performance. We show model performance changes under various k-mer related parameter settings. Furthermore, we investigate the effect of attention mechanism and RNA structure data on model performance.
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iEnhancer-Deep: A Computational Predictor for Enhancer Sites and Their Strength Using Deep Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042120] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Enhancers are short motifs that contain high position variability and free scattering. Identifying these non-coding DNA fragments and their strength is vital because they play an important role in the control of gene regulation. Enhancer identification is more complicated than other genetic factors due to free scattering and their very high amount of locational variation. To classify this biological difficulty, several computational tools in bioinformatics have been created over the last few years as current learning models are still lacking. To overcome these limitations, we introduce iEnhancer-Deep, a deep learning-based framework that uses One-Hot Encoding and a convolutional neural network for model construction, primarily for the identification of enhancers and secondarily for the classification of their strength. Parallels between the iEnhancer-Deep and existing state-of-the-art methodologies were drawn to evaluate the performance of the proposed model. Furthermore, a cross-species test was carried out to assess the generalizability of the proposed model. In general, the results show that the proposed model produced comparable results with the state-of-the-art models.
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Zhu Z, Ge S, Cai Z, Wu Y, Lu C, Zhang Z, Fu P, Mao L, Wu X, Peng Y. Systematic identification and characterization of repeat sequences in African swine fever virus genomes. Vet Res 2022; 53:101. [PMID: 36461107 PMCID: PMC9717548 DOI: 10.1186/s13567-022-01119-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/19/2022] [Indexed: 12/03/2022] Open
Abstract
African swine fever virus (ASFV) is a large DNA virus that infects domestic pigs with high morbidity and mortality rates. Repeat sequences, which are DNA sequence elements that are repeated more than twice in the genome, play an important role in the ASFV genome. The majority of repeat sequences, however, have not been identified and characterized in a systematic manner. In this study, three types of repeat sequences, including microsatellites, minisatellites and short interspersed nuclear elements (SINEs), were identified in the ASFV genome, and their distribution, structure, function, and evolutionary history were investigated. Most repeat sequences were observed in noncoding regions and at the 5' end of the genome. Noncoding repeat sequences tended to form enhancers, whereas coding repeat sequences had a lower ratio of alpha-helix and beta-sheet and a higher ratio of loop structure and surface amino acids than nonrepeat sequences. In addition, the repeat sequences tended to encode penetrating and antimicrobial peptides. Further analysis of the evolution of repeat sequences revealed that the pan-repeat sequences presented an open state, showing the diversity of repeat sequences. Finally, CpG islands were observed to be negatively correlated with repeat sequence occurrences, suggesting that they may affect the generation of repeat sequences. Overall, this study emphasizes the importance of repeat sequences in ASFVs, and these results can aid in understanding the virus's function and evolution.
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Affiliation(s)
- Zhaozhong Zhu
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
| | - Shengqiang Ge
- grid.414245.20000 0004 6063 681XChina Animal Health and Epidemiology Center, Qingdao, 266032 China ,grid.418524.e0000 0004 0369 6250Key Laboratory of Animal Biosafety Risk Prevention and Control (South), Ministry of Agriculture and Rural Affairs, Qingdao, China
| | - Zena Cai
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
| | - Yifan Wu
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
| | - Congyu Lu
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
| | - Zheng Zhang
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
| | - Ping Fu
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
| | - Longfei Mao
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
| | - Xiaodong Wu
- grid.414245.20000 0004 6063 681XChina Animal Health and Epidemiology Center, Qingdao, 266032 China ,grid.418524.e0000 0004 0369 6250Key Laboratory of Animal Biosafety Risk Prevention and Control (South), Ministry of Agriculture and Rural Affairs, Qingdao, China
| | - Yousong Peng
- grid.67293.39Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082 China
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Wang C, Ju Y, Zou Q, Lin C. DeepAc4C: a convolutional neural network model with hybrid features composed of physicochemical patterns and distributed representation information for identification of N4-acetylcytidine in mRNA. Bioinformatics 2021; 38:52-57. [PMID: 34427581 DOI: 10.1093/bioinformatics/btab611] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/17/2021] [Accepted: 08/20/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION N4-acetylcytidine (ac4C) is the only acetylation modification that has been characterized in eukaryotic RNA, and is correlated with various human diseases. Laboratory identification of ac4C is complicated by factors, such as sample hydrolysis and high cost. Unfortunately, existing computational methods to identify ac4C do not achieve satisfactory performance. RESULTS We developed a novel tool, DeepAc4C, which identifies ac4C using convolutional neural networks (CNNs) using hybrid features composed of physicochemical patterns and a distributed representation of nucleic acids. Our results show that the proposed model achieved better and more balanced performance than existing predictors. Furthermore, we evaluated the effect that specific features had on the model predictions and their interaction effects. Several interesting sequence motifs specific to ac4C were identified. AVAILABILITY AND IMPLEMENTATION The webserver is freely accessible at https://ac4c.webmalab.cn/, the source code and datasets are accessible at Zenodo with URL https://doi.org/10.5281/zenodo.5138047 and Github with URL https://github.com/wangchao-malab/DeepAc4C. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Chen Lin
- School of Informatics, Xiamen University, Xiamen 361005, China
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iDHS-DT: Identifying DNase I hypersensitive sites by integrating DNA dinucleotide and trinucleotide information. Biophys Chem 2021; 281:106717. [PMID: 34798459 DOI: 10.1016/j.bpc.2021.106717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 01/02/2023]
Abstract
DNase I hypersensitive sites (DHSs) is important for identifying the location of gene regulatory elements, such as promoters, enhancers, silencers, and so on. Thus, it is crucial for discriminating DHSs from non-DHSs. Although some traditional methods, such as Southern blots and DNase-seq technique, have the ability to identify DHSs, these approaches are time-consuming, laborious, and expensive. To address these issues, researchers paid their attention on computational approaches. Therefore, in this study, we developed a novel predictor called iDHS-DT to identify DHSs. In this predictor, the DNA sequences were firstly denoted by physicochemical properties (PC) of DNA dinucleotide and trinucleotide. Then, three different descriptors, including auto-covariance, cross-covariance, and discrete wavelet transform were used to collect related features from the PC matrix. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was employed to remove these irrelevant and redundant features. Finally, these selected features were fed into support vector machine (SVM) for distinguishing DHSs from non-DHSs. The proposed method achieved 97.64% and 98.22% classification accuracy on dataset S1 and S2, respectively. Compared with the existing predictors, our proposed model has significantly improvement in classification performance. Experimental results demonstrated that the proposed method is powerful in identifying DHSs.
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Lyu Y, Zhang Z, Li J, He W, Ding Y, Guo F. iEnhancer-KL: A Novel Two-Layer Predictor for Identifying Enhancers by Position Specific of Nucleotide Composition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2809-2815. [PMID: 33481715 DOI: 10.1109/tcbb.2021.3053608] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
An enhancer is a short region of DNA with the ability to recruit transcription factors and their complexes, increasing the likelihood of the transcription of a particular gene. Considering the importance of enhancers, enhancer identification is a prevailing problem in computational biology. In this paper, we propose a novel two-layer enhancer predictor called iEnhancer-KL, using computational biology algorithms to identify enhancers and then classify these enhancers into strong or weak types. Kullback-Leibler (KL) divergence is creatively taken into consideration to improve the feature extraction method PSTNPss. Then, LASSO is used to reduce the dimension of features and finally helps to get better prediction performance. Furthermore, the selected features are tested on several machine learning models, and the SVM algorithm achieves the best performance. The rigorous cross-validation indicates that our predictor is remarkably superior to the existing state-of-the-art methods with an Acc of 84.23 percent and the MCC of 0.6849 for identifying enhancers. Our code and results can be freely downloaded from https://github.com/Not-so-middle/iEnhancer-KL.git.
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Kang Q, Meng J, Su C, Luan Y. Mining plant endogenous target mimics from miRNA-lncRNA interactions based on dual-path parallel ensemble pruning method. Brief Bioinform 2021; 23:6399881. [PMID: 34662389 DOI: 10.1093/bib/bbab440] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/07/2021] [Accepted: 09/24/2021] [Indexed: 12/14/2022] Open
Abstract
The interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) play important roles in biological activities. Specially, lncRNAs as endogenous target mimics (eTMs) can bind miRNAs to regulate the expressions of target messenger RNAs (mRNAs). A growing number of studies focus on animals, but the studies on plants are scarce and many functions of plant eTMs are unknown. This study proposes a novel ensemble pruning protocol for predicting plant miRNA-lncRNA interactions at first. It adaptively prunes the base models based on dual-path parallel ensemble method to meet the challenge of cross-species prediction. Then potential eTMs are mined from predicted results. The expression levels of RNAs are identified through biological experiment to construct the lncRNA-miRNA-mRNA regulatory network, and the functions of potential eTMs are inferred through enrichment analysis. Experiment results show that the proposed protocol outperforms existing methods and state-of-the-art predictors on various plant species. A total of 17 potential eTMs are verified by biological experiment to involve in 22 regulations, and 14 potential eTMs are inferred by Gene Ontology enrichment analysis to involve in 63 functions, which is significant for further research.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Chenglin Su
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024 China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024 China
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Liang Y, Zhang S, Qiao H, Cheng Y. iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:8797-8814. [PMID: 34814323 DOI: 10.3934/mbe.2021434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Enhancer is a non-coding DNA fragment that can be bound with proteins to activate transcription of a gene, hence play an important role in regulating gene expression. Enhancer identification is very challenging and more complicated than other genetic factors due to their position variation and free scattering. In addition, it has been proved that genetic variation in enhancers is related to human diseases. Therefore, identification of enhancers and their strength has important biological meaning. In this paper, a novel model named iEnhancer-MFGBDT is developed to identify enhancer and their strength by fusing multiple features and gradient boosting decision tree (GBDT). Multiple features include k-mer and reverse complement k-mer nucleotide composition based on DNA sequence, and second-order moving average, normalized Moreau-Broto auto-cross correlation and Moran auto-cross correlation based on dinucleotide physical structural property matrix. Then we use GBDT to select features and perform classification successively. The accuracies reach 78.67% and 66.04% for identifying enhancers and their strength on the benchmark dataset, respectively. Compared with other models, the results show that our model is useful and effective intelligent tool to identify enhancers and their strength, of which the datasets and source codes are available at https://github.com/shengli0201/iEnhancer-MFGBDT1.
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Affiliation(s)
- Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an 710048, China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Huijuan Qiao
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| | - Yinan Cheng
- Department of Statistics, University of California at Davis, Davis, CA 95616, USA
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Wang S, He Y, Chen Z, Zhang Q. FCNGRU: Locating Transcription Factor Binding Sites by combing Fully Convolutional Neural Network with Gated Recurrent Unit. IEEE J Biomed Health Inform 2021; 26:1883-1890. [PMID: 34613923 DOI: 10.1109/jbhi.2021.3117616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Deciphering the relationship between transcription factors (TFs) and DNA sequences is very helpful for computational inference of gene regulation and a comprehensive understanding of gene regulation mechanisms. Transcription factor binding sites (TFBSs) are specific DNA short sequences that play a pivotal role in controlling gene expression through interaction with TF proteins. Although recently many computational and deep learning methods have been proposed to predict TFBSs aiming to predict sequence specificity of TF-DNA binding, there is still a lack of effective methods to directly locate TFBSs. In order to address this problem, we propose FCNGRU combing a fully convolutional neural network (FCN) with the gated recurrent unit (GRU) to directly locate TFBSs in this paper. Furthermore, we present a two-task framework (FCNGRU-double): one is a classification task at nucleotide level which predicts the probability of each nucleotide and locates TFBSs, and the other is a regression task at sequence level which predicts the intensity of each sequence. A series of experiments are conducted on 45 in-vitro datasets collected from the UniPROBE database derived from universal protein binding microarrays (uPBMs). Compared with competing methods, FCNGRU-double achieves much better results on these datasets. Moreover, FCNGRU-double has an advantage over a single-task framework, FCNGRU-single, which only contains the branch of locating TFBSs. In additionwe combine with in vivo datasets to make a further analysis and discussion. The source codes are avaiable at https://github.com/wangguoguoa/FCNGRU.
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Identifying Dipeptidyl Peptidase-IV Inhibitory Peptides Based on Correlation Information of Physicochemical Properties. Int J Pept Res Ther 2021. [DOI: 10.1007/s10989-021-10280-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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