1
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Zhang Q, Wei Y, Liu L. GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species. Comput Biol Med 2024; 180:108974. [PMID: 39096613 DOI: 10.1016/j.compbiomed.2024.108974] [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: 02/24/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 08/05/2024]
Abstract
Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation mechanisms and developing therapeutic approaches for various diseases. However, experimental techniques for promoter identification are often expensive, time-consuming, and inefficient, necessitating the development of accurate and efficient computational models for this task. Enhancing the model's ability to recognize promoters across multiple species and improving its interpretability pose significant challenges. In this study, we introduce a novel interpretable model based on graph neural networks, named GraphPro, for multi-species promoter identification. Initially, we encode the sequences using k-tuple nucleotide frequency pattern, dinucleotide physicochemical properties, and dna2vec. Subsequently, we construct two feature extraction modules based on convolutional neural networks and graph neural networks. These modules aim to extract specific motifs from the promoters, learn their dependencies, and capture the underlying structural features of the promoters, providing a more comprehensive representation. Finally, a fully connected neural network predicts whether the input sequence is a promoter. We conducted extensive experiments on promoter datasets from eight species, including Human, Mouse, and Escherichia coli. The experimental results show that the average Sn, Sp, Acc and MCC values of GraphPro are 0.9123, 0.9482, 0.8840 and 0.7984, respectively. Compared with previous promoter identification methods, GraphPro not only achieves better recognition accuracy on multiple species, but also outperforms all previous methods in cross-species prediction ability. Furthermore, by visualizing GraphPro's decision process and analyzing the sequences matching the transcription factor binding motifs captured by the model, we validate its significant advantages in biological interpretability. The source code for GraphPro is available at https://github.com/liuliwei1980/GraphPro.
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Affiliation(s)
- Qi Zhang
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
| | - Yuxiao Wei
- College of Software, Dalian Jiaotong University, Dalian, 116028, China
| | - Liwei Liu
- College of Science, Dalian Jiaotong University, Dalian, 116028, China.
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2
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Zhang H, Chen J, Liao B, Wu FX, Bi XA. Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification. Interdiscip Sci 2024; 16:455-468. [PMID: 38573456 DOI: 10.1007/s12539-024-00625-y] [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: 12/07/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 04/05/2024]
Abstract
Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.
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Affiliation(s)
- Huilian Zhang
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Jie Chen
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Bo Liao
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N5A9, Canada
| | - Xia-An Bi
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China.
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China.
- College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China.
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3
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Sun DZ, Sun ZL, Liu M, Yong SH. LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering. Interdiscip Sci 2024; 16:378-391. [PMID: 38206558 DOI: 10.1007/s12539-023-00598-4] [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: 08/19/2023] [Revised: 11/25/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024]
Abstract
Long noncoding RNAs (lncRNAs) have significant regulatory roles in gene expression. Interactions with proteins are one of the ways lncRNAs play their roles. Since experiments to determine lncRNA-protein interactions (LPIs) are expensive and time-consuming, many computational methods for predicting LPIs have been proposed as alternatives. In the LPIs prediction problem, there commonly exists the imbalance in the distribution of positive and negative samples. However, there are few existing methods that give specific consideration to this problem. In this paper, we proposed a new clustering-based LPIs prediction method using segmented k-mer frequencies and multi-space clustering (LPI-SKMSC). It was dedicated to handling the imbalance of positive and negative samples. We constructed segmented k-mer frequencies to obtain global and local features of lncRNA and protein sequences. Then, the multi-space clustering was applied to LPI-SKMSC. The convolutional neural network (CNN)-based encoders were used to map different features of a sample to different spaces. It used multiple spaces to jointly constrain the classification of samples. Finally, the distances between the output features of the encoder and the cluster center in each space were calculated. The sum of distances in all spaces was compared with the cluster radius to predict the LPIs. We performed cross-validation on 3 public datasets and LPI-SKMSC showed the best performance compared to other existing methods. Experimental results showed that LPI-SKMSC could predict LPIs more effectively when faced with imbalanced positive and negative samples. In addition, we illustrated that our model was better at uncovering potential lncRNA-protein interaction pairs.
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Affiliation(s)
- Dian-Zheng Sun
- School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China
| | - Zhan-Li Sun
- School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China.
| | - Mengya Liu
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China
| | - Shuang-Hao Yong
- School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China
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4
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Huang G, Huang X, Jiang J. Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues. Methods 2024; 226:1-8. [PMID: 38485031 DOI: 10.1016/j.ymeth.2024.03.004] [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: 11/06/2023] [Revised: 02/20/2024] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection over the past decades, there were rooms left to improve the predictive accuracy and the efficiency. In this paper, we proposed an improved method for predicting m6A modification sites, which was based on bi-directional gated recurrent unit (Bi-GRU) and convolutional neural networks (CNN), called Deepm6A-MT. The Deepm6A-MT has two input channels. One is to use an embedding layer followed by the Bi-GRU and then by the CNN, and another is to use one-hot encoding, dinucleotide one-hot encoding, and nucleotide chemical property codes. We trained and evaluated the Deepm6A-MT both by the 5-fold cross-validation and the independent test. The empirical tests showed that the Deepm6A-MT achieved the state of the art performance. In addition, we also conducted the cross-species and the cross-tissues tests to further verify the Deepm6A-MT for effectiveness and efficiency. Finally, for the convenience of academic research, we deployed the Deepm6A-MT to the web server, which is accessed at the URL http://www.biolscience.cn/Deepm6A-MT/.
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Affiliation(s)
- Guohua Huang
- School of Information Technology and Administration, Hunan University of Finance and Economics, Changsha, Hunan 410205, China.
| | - Xiaohong Huang
- College of Information Science and Engineering, Shaoyang University, Shaoyang, Hunan 422000, China
| | - Jinyun Jiang
- College of Information Science and Engineering, Shaoyang University, Shaoyang, Hunan 422000, China
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5
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Yuan Y, Tang X, Li H, Lang X, Song Y, Yang Y, Zhou Z. BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs. Molecules 2024; 29:2429. [PMID: 38893304 PMCID: PMC11173551 DOI: 10.3390/molecules29112429] [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: 04/03/2024] [Revised: 05/13/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications, a critical gap remains in the prediction of circRNA m6A modifications. Therefore, accurate identification and prediction of m6A sites are imperative for understanding RNA function and regulation. This study presents a novel hybrid model combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM) for precise m6A methylation site prediction in circular RNAs (circRNAs) based on data from HEK293 cells. This model exploits the synergy between CNN's ability to extract intricate sequence features and BiLSTM's strength in capturing long-range dependencies. Furthermore, the integrated attention mechanism empowers the model to pinpoint critical biological information for studying circRNA m6A methylation. Our model, exhibiting over 78% prediction accuracy on independent datasets, offers not only a valuable tool for scientific research but also a strong foundation for future biomedical applications. This work not only furthers our understanding of gene expression regulation but also opens new avenues for the exploration of circRNA methylation in biological research.
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Affiliation(s)
- Yuqian Yuan
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China; (Y.Y.); (H.L.); (X.L.); (Y.S.)
| | - Xiaozhu Tang
- School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China;
| | - Hongyan Li
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China; (Y.Y.); (H.L.); (X.L.); (Y.S.)
| | - Xufeng Lang
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China; (Y.Y.); (H.L.); (X.L.); (Y.S.)
| | - Yihua Song
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China; (Y.Y.); (H.L.); (X.L.); (Y.S.)
| | - Ye Yang
- School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China;
| | - Zuojian Zhou
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China; (Y.Y.); (H.L.); (X.L.); (Y.S.)
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6
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Chen J, Zhang H, Zou Q, Liao B, Bi XA. Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction. Interdiscip Sci 2024:10.1007/s12539-024-00629-8. [PMID: 38683281 DOI: 10.1007/s12539-024-00629-8] [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: 01/02/2024] [Revised: 03/06/2024] [Accepted: 03/31/2024] [Indexed: 05/01/2024]
Abstract
Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.
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Affiliation(s)
- Jie Chen
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Huilian Zhang
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bo Liao
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Xia-An Bi
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China.
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China.
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7
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Li G, Zhao B, Su X, Yang Y, Hu P, Zhou X, Hu L. Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering. IEEE J Biomed Health Inform 2024; 28:2362-2372. [PMID: 38265898 DOI: 10.1109/jbhi.2024.3357979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.
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8
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Wang M, Ali H, Xu Y, Xie J, Xu S. BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities. J Biol Chem 2024; 300:107140. [PMID: 38447795 PMCID: PMC10997841 DOI: 10.1016/j.jbc.2024.107140] [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: 12/21/2023] [Revised: 02/17/2024] [Accepted: 02/25/2024] [Indexed: 03/08/2024] Open
Abstract
RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.
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Affiliation(s)
- Mingzhao Wang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Haider Ali
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Yandi Xu
- School of Computer Science, Shaanxi Normal University, Xi'an, China; College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an, China.
| | - Shengquan Xu
- College of Life Sciences, Shaanxi Normal University, Xi'an, China.
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9
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Jiang J, Song B, Meng J, Zhou J. Tissue-specific RNA methylation prediction from gene expression data using sparse regression models. Comput Biol Med 2024; 169:107892. [PMID: 38171264 DOI: 10.1016/j.compbiomed.2023.107892] [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: 07/20/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024]
Abstract
N6-methyladenosine (m6A) is a highly prevalent and conserved post-transcriptional modification observed in mRNA and long non-coding RNA (lncRNA). Identifying potential m6A sites within RNA sequences is crucial for unraveling the potential influence of the epitranscriptome on biological processes. In this study, we introduce Exp2RM, a novel approach that formulates single-site-based tissue-specific elastic net models for predicting tissue-specific methylation levels utilizing gene expression data. The resulting ensemble model demonstrates robust predictive performance for tissue-specific methylation levels, with an average R-squared value of 0.496 and a median R-squared value of 0.482 across all 22 human tissues. Since methylation distribution varies among tissues, we trained the model to incorporate similar patterns, significantly improves accuracy with the median R-squared value increasing to 0.728. Additonally, functional analysis reveals Exp2RM's ability to capture coefficient genes in relevant biological processes. This study emphasizes the importance of tissue-specific methylation distribution in enhancing prediction accuracy and provides insights into the functional implications of methylation sites.
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Affiliation(s)
- Jie Jiang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
| | - Bowen Song
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China; AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB, Liverpool, United Kingdom
| | - Jingxian Zhou
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University Entrepreneur College (Taicang), Taicang, Suzhou, Jiangsu Province, 215400, China; Department of Computer Science, University of Liverpool, L69 7ZB, Liverpool, United Kingdom.
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10
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Zhang Y, Wang Z, Zhang Y, Li S, Guo Y, Song J, Yu DJ. Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues. Bioinformatics 2023; 39:btad709. [PMID: 37995291 PMCID: PMC10697738 DOI: 10.1093/bioinformatics/btad709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 11/01/2023] [Accepted: 11/22/2023] [Indexed: 11/25/2023] Open
Abstract
MOTIVATION RNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability. RESULTS In this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies. AVAILABILITY AND IMPLEMENTATION The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.
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Affiliation(s)
- Ying Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Zhikang Wang
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Yiwen Zhang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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11
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Le NQK, Xu L. Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites. ACS OMEGA 2023; 8:39420-39426. [PMID: 37901522 PMCID: PMC10600906 DOI: 10.1021/acsomega.3c05074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/28/2023] [Indexed: 10/31/2023]
Abstract
DNA N6-methyladenosine (6 mA) modification carries significant epigenetic information and plays a pivotal role in biological functions, thereby profoundly impacting human development. Precise and reliable detection of 6 mA sites is integral to understanding the mechanisms underpinning DNA modification. The present methods, primarily experimental, used to identify specific molecular sites are often time-intensive and costly. Consequently, the rise of computer-based methods aimed at identifying 6 mA sites provides a welcome alternative. Our research introduces a novel model to discern DNA 6 mA sites in cross-species genomes. This model, developed through machine learning, utilizes extracted sequence information. Hyperparameter tuning was employed to ascertain the most effective feature combination and model implementation, thereby garnering vital information from sequences. Our model demonstrated superior accuracy compared to the existing models when tested using five-fold cross-validation. Thus, our study substantiates the reliability and efficiency of our model as a valuable tool for supplementing experimental research.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional
Master Program in Artificial Intelligence in Medicine, College of
Medicine, Taipei Medical University, Taipei 110, Taiwan
- Research
Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan
- AIBioMed
Research Group, Taipei Medical University, Taipei 110, Taiwan
- Translational
Imaging Research Center, Taipei Medical
University Hospital, Taipei 110, Taiwan
| | - Ling Xu
- NUS-ISS,
National University of Singapore, Singapore, 119615, Singapore
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12
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Song B, Huang D, Zhang Y, Wei Z, Su J, Pedro de Magalhães J, Rigden DJ, Meng J, Chen K. m6A-TSHub: Unveiling the Context-specific m 6A Methylation and m 6A-affecting Mutations in 23 Human Tissues. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:678-694. [PMID: 36096444 PMCID: PMC10787194 DOI: 10.1016/j.gpb.2022.09.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 08/19/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.
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Affiliation(s)
- Bowen Song
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, United Kingdom.
| | - Yuxin Zhang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Zhen Wei
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Institute of Ageing & Chronic Disease, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Jionglong Su
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - João Pedro de Magalhães
- Institute of Ageing & Chronic Disease, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Jia Meng
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Kunqi Chen
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China.
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13
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Hu W, Guan L, Li M. Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network. PLoS Comput Biol 2023; 19:e1011370. [PMID: 37639434 PMCID: PMC10461834 DOI: 10.1371/journal.pcbi.1011370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/18/2023] [Indexed: 08/31/2023] Open
Abstract
DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of chromosomes. DNA methylation modification sites should be identified, which lays a solid basis for gaining more insights into their biological functions. Existing machine learning-based methods of predicting DNA methylation have not fully exploited the hidden multidimensional information in DNA gene sequences, such that the prediction accuracy of models is significantly limited. Besides, most models have been built in terms of a single methylation type. To address the above-mentioned issues, a deep learning-based method was proposed in this study for DNA methylation site prediction, termed the MEDCNN model. The MEDCNN model is capable of extracting feature information from gene sequences in three dimensions (i.e., positional information, biological information, and chemical information). Moreover, the proposed method employs a convolutional neural network model with double convolutional layers and double fully connected layers while iteratively updating the gradient descent algorithm using the cross-entropy loss function to increase the prediction accuracy of the model. Besides, the MEDCNN model can predict different types of DNA methylation sites. As indicated by the experimental results,the deep learning method based on coding from multiple dimensions outperformed single coding methods, and the MEDCNN model was highly applicable and outperformed existing models in predicting DNA methylation between different species. As revealed by the above-described findings, the MEDCNN model can be effective in predicting DNA methylation sites.
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Affiliation(s)
- Wenxing Hu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
| | - Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
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14
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M6A-BERT-Stacking: A Tissue-Specific Predictor for Identifying RNA N6-Methyladenosine Sites Based on BERT and Stacking Strategy. Symmetry (Basel) 2023. [DOI: 10.3390/sym15030731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
As the most abundant RNA methylation modification, N6-methyladenosine (m6A) could regulate asymmetric and symmetric division of hematopoietic stem cells and play an important role in various diseases. Therefore, the precise identification of m6A sites around the genomes of different species is a critical step to further revealing their biological functions and influence on these diseases. However, the traditional wet-lab experimental methods for identifying m6A sites are often laborious and expensive. In this study, we proposed an ensemble deep learning model called m6A-BERT-Stacking, a powerful predictor for the detection of m6A sites in various tissues of three species. First, we utilized two encoding methods, i.e., di ribonucleotide index of RNA (DiNUCindex_RNA) and k-mer word segmentation, to extract RNA sequence features. Second, two encoding matrices together with the original sequences were respectively input into three different deep learning models in parallel to train three sub-models, namely residual networks with convolutional block attention module (Resnet-CBAM), bidirectional long short-term memory with attention (BiLSTM-Attention), and pre-trained bidirectional encoder representations from transformers model for DNA-language (DNABERT). Finally, the outputs of all sub-models were ensembled based on the stacking strategy to obtain the final prediction of m6A sites through the fully connected layer. The experimental results demonstrated that m6A-BERT-Stacking outperformed most of the existing methods based on the same independent datasets.
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15
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Rehman MU, Tayara H, Chong KT. DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:904-911. [PMID: 35857733 DOI: 10.1109/tcbb.2022.3192572] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently expensive for transcriptome-wide m6A identification. Some computational strategies for identifying m6A sites have been presented as an effective complement to the experimental procedure. However, their performance still requires improvement. In this study, we have proposed a novel tool called DL-m6A for the identification of m6A sites in mammals using deep learning based on different encoding schemes. The proposed tool uses three encoding schemes which give the required contextual feature representation to the input RNA sequence. Later these contextual feature vectors individually go through several neural network layers for shallow feature extraction after which they are concatenated to a single feature vector. The concatenated feature map is then used by several other layers to extract the deep features so that the insight features of the sequence can be used for the prediction of m6A sites. The proposed tool is firstly evaluated on the tissue-specific dataset and later on a full transcript dataset. To ensure the generalizability of the tool we assessed the proposed model by training it on a full transcript dataset and test on the tissue-specific dataset. The achieved results by the proposed model have outperformed the existing tools. The results demonstrate that the proposed tool can be of great use for the biology experts and therefore a freely accessible web-server is created which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/DL-m6A/.
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16
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Fan Y, Sun G, Pan X. ELMo4m6A: A Contextual Language Embedding-Based Predictor for Detecting RNA N6-Methyladenosine Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:944-954. [PMID: 35536814 DOI: 10.1109/tcbb.2022.3173323] [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
N6-methyladenosine (m6A) is a universal post-transcriptional modification of RNAs, and it is widely involved in various biological processes. Identifying m6A modification sites accurately is indispensable to further investigate m6A-mediated biological functions. How to better represent RNA sequences is crucial for building effective computational methods for detecting m6A modification sites. However, traditional encoding methods require complex biological prior knowledge and are time-consuming. Furthermore, most of the existing m6A sites prediction methods are limited to single species, and few methods are able to predict m6A sites across different species and tissues. Thus, it is necessary to design a more efficient computational method to predict m6A sites across multiple species and tissues. In this paper, we proposed ELMo4m6A, a contextual language embedding-based method for predicting m6A sites from RNA sequences without any prior knowledge. ELMo4m6A first learns embeddings of RNA sequences using a language model ELMo, then uses a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) to identify m6A sites. The results of 5-fold cross-validation and independent testing demonstrate that ELMo4m6A is superior to state-of-the-art methods. Moreover, we applied integrated gradients to find potential sequence patterns contributing to m6A sites.
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17
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Zou J, Liu H, Tan W, Chen YQ, Dong J, Bai SY, Wu ZX, Zeng Y. Dynamic regulation and key roles of ribonucleic acid methylation. Front Cell Neurosci 2022; 16:1058083. [PMID: 36601431 PMCID: PMC9806184 DOI: 10.3389/fncel.2022.1058083] [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: 09/30/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N 6-methyladenosine (m6A), 5-methylcytosine (m5C), N 1-methyladenosine (m1A), 5-hydroxymethylcytosine (hm5C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.
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Affiliation(s)
- Jia Zou
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Hui Liu
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Wei Tan
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yi-qi Chen
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Jing Dong
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Shu-yuan Bai
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Zhao-xia Wu
- Community Health Service Center, Wuchang Hospital, Wuhan, China
| | - Yan Zeng
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China,School of Public Health, Wuhan University of Science and Technology, Wuhan, China,*Correspondence: Yan Zeng,
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18
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Luo Z, Lou L, Qiu W, Xu Z, Xiao X. Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning. Int J Mol Sci 2022; 23:ijms232415490. [PMID: 36555143 PMCID: PMC9778682 DOI: 10.3390/ijms232415490] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
N6-methyladenosine (m6A) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA m6A transcriptome-wide at base resolution via experimental approaches, which are generally time-consuming and expensive. Developing computational methods is a good strategy for accurate in silico detection of m6A modification sites from the large amount of RNA sequence data. Unfortunately, the existing computational models are usually only for m6A site prediction in a single species, without considering the tissue level of species, while most of them are constructed based on low-confidence level data generated by an m6A antibody immunoprecipitation (IP)-based sequencing method, thereby restricting reliability and generalizability of proposed models. Here, we review recent advances in computational prediction of m6A sites and construct a new computational approach named im6APred using ensemble deep learning to accurately identify m6A sites based on high-confidence level data in multiple tissues of mammals. Our model im6APred builds upon a comprehensive evaluation of multiple classification methods, including four traditional classification algorithms and three deep learning methods and their ensembles. The optimal base-classifier combinations are then chosen by five-fold cross-validation test to achieve an effective stacked model. Our model im6APred can produce the area under the receiver operating characteristic curve (AUROC) in the range of 0.82-0.91 on independent tests, indicating that our model has the ability to learn general methylation rules on RNA bases and generalize to m6A transcriptome-wide identification. Moreover, AUROCs in the range of 0.77-0.96 were achieved using cross-species/tissues validation on the benchmark dataset, demonstrating differences in predictive performance at the tissue level and the need for constructing tissue-specific models for m6A site prediction.
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19
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Zhang T, Tang Q, Nie F, Zhao Q, Chen W. DeepLncPro: an interpretable convolutional neural network model for identifying long non-coding RNA promoters. Brief Bioinform 2022; 23:6754194. [PMID: 36209437 DOI: 10.1093/bib/bbac447] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/14/2022] [Accepted: 09/17/2022] [Indexed: 12/14/2022] Open
Abstract
Long non-coding RNA (lncRNA) plays important roles in a series of biological processes. The transcription of lncRNA is regulated by its promoter. Hence, accurate identification of lncRNA promoter will be helpful to understand its regulatory mechanisms. Since experimental techniques remain time consuming for gnome-wide promoter identification, developing computational tools to identify promoters are necessary. However, only few computational methods have been proposed for lncRNA promoter prediction and their performances still have room to be improved. In the present work, a convolutional neural network based model, called DeepLncPro, was proposed to identify lncRNA promoters in human and mouse. Comparative results demonstrated that DeepLncPro was superior to both state-of-the-art machine learning methods and existing models for identifying lncRNA promoters. Furthermore, DeepLncPro has the ability to extract and analyze transcription factor binding motifs from lncRNAs, which made it become an interpretable model. These results indicate that the DeepLncPro can server as a powerful tool for identifying lncRNA promoters. An open-source tool for DeepLncPro was provided at https://github.com/zhangtian-yang/DeepLncPro.
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Affiliation(s)
- Tianyang Zhang
- School of Life Sciences, North China University of Science and Technology
| | - Qiang Tang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine
| | - Fulei Nie
- School of Life Sciences, North China University of Science and Technology
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine
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20
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Luan J, Kopp JB, Zhou H. N6-methyladenine RNA Methylation Epigenetic Modification and Kidney Diseases. Kidney Int Rep 2022; 8:36-50. [PMID: 36644366 PMCID: PMC9831943 DOI: 10.1016/j.ekir.2022.10.009] [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: 07/31/2022] [Revised: 10/01/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
RNA methylation modification is a rapidly developing field in epigenetics. N6-methyladensine (m6A) is the most common internal modification in eukaryotic mRNA. m6A group regulates RNA splicing, stability, translocation, and translation. Enzymes catalyzing this process were termed as writers, erasers, and readers. Recent studies have focused on exploring the role of RNA methylation in human diseases. RNA methylation modifications, particularly m6A, play important roles in the pathogenesis of kidney diseases. In this review, we provide a brief description of m6A and summarize the impact of m6A on acute and chronic kidney disease (CKD) and possible future study directions for this research.
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Affiliation(s)
- Junjun Luan
- Department of Nephrology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jeffrey B. Kopp
- Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases/National Institutes of Health, Bethesda, Maryland, USA,Jeffrey B. Kopp, Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases/National Institutes of Health, 10 Center Drive, 3N116, Bethesda, Maryland 20892-1268, USA.
| | - Hua Zhou
- Department of Nephrology, Shengjing Hospital of China Medical University, Shenyang, China,Correspondence: Hua Zhou, Shengjing Hospital of China Medical University, No.36 Sanhao Street, Shenyang, Liaoning 110004, China.
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21
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Huang D, Chen K, Song B, Wei Z, Su J, Coenen F, de Magalhães JP, Rigden DJ, Meng J. Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation. Nucleic Acids Res 2022; 50:10290-10310. [PMID: 36155798 PMCID: PMC9561283 DOI: 10.1093/nar/gkac830] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/26/2022] [Accepted: 09/15/2022] [Indexed: 12/25/2022] Open
Abstract
As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3′UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m6A sites, and improves m6A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m6A but also N1-methyladenosine (m1A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.
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Affiliation(s)
- Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Department of Computer Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - Kunqi Chen
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, PR China
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Zhen Wei
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jionglong Su
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China
| | - Frans Coenen
- Department of Computer Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - João Pedro de Magalhães
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China
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22
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Ma L, He LN, Kang S, Gu B, Gao S, Zuo Z. Advances in detecting N6-methyladenosine modification in circRNAs. Methods 2022; 205:234-246. [PMID: 35878749 DOI: 10.1016/j.ymeth.2022.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 12/14/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of noncoding RNAs with covalently single-stranded closed loop structures derived from back-splicing event of linear precursor mRNAs (pre-mRNAs). N6-methyladenosine (m6A), the most abundant epigenetic modification in eukaryotic RNAs, has been shown to play a crucial role in regulating the fate and biological function of circRNAs, and thus affecting various physiological and pathological processes. Accurate identification of m6A modification in circRNAs is an essential step to fully elucidate the crosstalk between m6A and circRNAs. In recent years, the rapid development of high-throughput sequencing technology and bioinformatic methodology has propelled the establishment of a multitude of approaches to detect circRNAs and m6A modification, including in vitro-based and in silico methods. Based on this, the research community has started on a new journey to develop methods for identification of m6A modification in circRNAs. In this review, we provide a comprehensive review and evaluation of the existing methods responsible for detecting circRNAs, m6A modification, and especially, m6A modification in circRNAs, which mainly focused on those developed based on high-throughput technologies and methodology of bioinformatics. This handy reference can help researchers figure out towards which direction this field will go.
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Affiliation(s)
- Lixia Ma
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital (College of Clinical Medical) of Henan University of Science and Technology, Luoyang, China
| | - Li-Na He
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shiyang Kang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Bianli Gu
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital (College of Clinical Medical) of Henan University of Science and Technology, Luoyang, China
| | - Shegan Gao
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital (College of Clinical Medical) of Henan University of Science and Technology, Luoyang, China.
| | - Zhixiang Zuo
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
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23
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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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24
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Manavalan B, Basith S, Lee G. Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2. Brief Bioinform 2022; 23:bbab412. [PMID: 34595489 PMCID: PMC8500067 DOI: 10.1093/bib/bbab412] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/27/2021] [Accepted: 09/07/2021] [Indexed: 01/08/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.
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Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
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25
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Abbas Z, Tayara H, Zou Q, Chong KT. TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model. Comput Struct Biotechnol J 2021; 19:4619-4625. [PMID: 34471503 PMCID: PMC8383060 DOI: 10.1016/j.csbj.2021.08.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/08/2021] [Accepted: 08/09/2021] [Indexed: 01/30/2023] Open
Abstract
The most communal post-transcriptional modification, N6-methyladenosine (m6A), is associated with a number of crucial biological processes. The precise detection of m6A sites around the genome is critical for revealing its regulatory function and providing new insights into drug design. Although both experimental and computational models for detecting m6A sites have been introduced, but these conventional methods are laborious and expensive. Furthermore, only a handful of these models are capable of detecting m6A sites in various tissues. Therefore, a more generic and optimized computational method for detecting m6A sites in different tissues is required. In this paper, we proposed a universal model using a deep neural network (DNN) and named it TS-m6A-DL, which can classify m6A sites in several tissues of humans (Homo sapiens), mice (Mus musculus), and rats (Rattus norvegicus). To extract RNA sequence features and to convert the input into numerical format for the network, we utilized one-hot-encoding method. The model was tested using fivefold cross-validation and its stability was measured using independent datasets. The proposed model, TS-m6A-DL, achieved accuracies in the range of 75-85% using the fivefold cross-validation method and 72-84% on the independent datasets. Finally, to authenticate the generalization of the model, we performed cross-species testing and proved the generalization ability by achieving state-of-the-art results.
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Affiliation(s)
- Zeeshan Abbas
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
- Institute of Avionics and Aeronautics (IAA), Air University, Islamabad 44000, Pakistan
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - 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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26
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Zulfiqar H, Sun ZJ, Huang QL, Yuan SS, Lv H, Dao FY, Lin H, Li YW. Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli. Methods 2021; 203:558-563. [PMID: 34352373 DOI: 10.1016/j.ymeth.2021.07.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/22/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022] Open
Abstract
N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.
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Affiliation(s)
- Hasan Zulfiqar
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Jie Sun
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qin-Lai Huang
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Shi Yuan
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yan-Wen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
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27
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Song Z, Huang D, Song B, Chen K, Song Y, Liu G, Su J, Magalhães JPD, Rigden DJ, Meng J. Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications. Nat Commun 2021; 12:4011. [PMID: 34188054 PMCID: PMC8242015 DOI: 10.1038/s41467-021-24313-3] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 06/07/2021] [Indexed: 02/08/2023] Open
Abstract
Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms. RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.
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Affiliation(s)
- Zitao Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | - Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China. .,Department of Computer Sciences, University of Liverpool, Liverpool, United Kingdom.
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Kunqi Chen
- Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, PR China
| | - Yiyou Song
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | - Gang Liu
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | | | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China. .,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom. .,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, PR China.
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28
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Pan J, Huang Z, Xu Y. m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma. Front Cell Dev Biol 2021; 9:671821. [PMID: 34268304 PMCID: PMC8277384 DOI: 10.3389/fcell.2021.671821] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/01/2021] [Indexed: 12/24/2022] Open
Abstract
Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs–mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.
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Affiliation(s)
- Junfan Pan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Zhidong Huang
- Quanzhou First Hospital, Fujian Medical University, Quanzhou, China
| | - Yiquan Xu
- Department of Thoracic Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
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29
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Wang M, Xie J, Xu S. M6A-BiNP: predicting N 6-methyladenosine sites based on bidirectional position-specific propensities of polynucleotides and pointwise joint mutual information. RNA Biol 2021; 18:2498-2512. [PMID: 34161188 PMCID: PMC8632114 DOI: 10.1080/15476286.2021.1930729] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
N6-methyladenosine (m6A) plays an important role in various biological processes. Identifying m6A site is a key step in exploring its biological functions. One of the biggest challenges in identifying m6A sites is how to extract features comprising rich categorical information to distinguish m6A and non-m6A sites. To address this challenge, we propose bidirectional dinucleotide and trinucleotide position-specific propensities, respectively, in this paper. Based on this, we propose two feature-encoding algorithms: Position-Specific Propensities and Pointwise Mutual Information (PSP-PMI) and Position-Specific Propensities and Pointwise Joint Mutual Information (PSP-PJMI). PSP-PMI is based on the bidirectional dinucleotide propensity and the pointwise mutual information, while PSP-PJMI is based on the bidirectional trinucleotide position-specific propensity and the proposed pointwise joint mutual information in this paper. We introduce parameters α and β in PSP-PMI and PSP-PJMI, respectively, to represent the distance from the nucleotide to its forward or backward adjacent nucleotide or dinucleotide, so as to extract features containing local and global classification information. Finally, we propose the M6A-BiNP predictor based on PSP-PMI or PSP-PJMI and SVM classifier. The 10-fold cross-validation experimental results on the benchmark datasets of non-single-base resolution and single-base resolution demonstrate that PSP-PMI and PSP-PJMI can extract features with strong capabilities to identify m6A and non-m6A sites. The M6A-BiNP predictor based on our proposed feature encoding algorithm PSP-PJMI is better than the state-of-the-art predictors, and it is so far the best model to identify m6A and non-m6A sites.
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Affiliation(s)
- Mingzhao Wang
- College of Life Sciences, Shaanxi Normal University, Xi'an, China.,School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Shengquan Xu
- College of Life Sciences, Shaanxi Normal University, Xi'an, China
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Wang Y, Guo R, Huang L, Yang S, Hu X, He K. m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information. Front Genet 2021; 12:670852. [PMID: 34122525 PMCID: PMC8191635 DOI: 10.3389/fgene.2021.670852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 04/29/2021] [Indexed: 11/30/2022] Open
Abstract
N6-methyladenosine (m6A) is one of the most prevalent RNA post-transcriptional modifications and is involved in various vital biological processes such as mRNA splicing, exporting, stability, and so on. Identifying m6A sites contributes to understanding the functional mechanism and biological significance of m6A. The existing biological experimental methods for identifying m6A sites are time-consuming and costly. Thus, developing a high confidence computational method is significant to explore m6A intrinsic characters. In this study, we propose a predictor called m6AGE which utilizes sequence-derived and graph embedding features. To the best of our knowledge, our predictor is the first to combine sequence-derived features and graph embeddings for m6A site prediction. Comparison results show that our proposed predictor achieved the best performance compared with other predictors on four public datasets across three species. On the A101 dataset, our predictor outperformed 1.34% (accuracy), 0.0227 (Matthew's correlation coefficient), 5.63% (specificity), and 0.0081 (AUC) than comparing predictors, which indicates that m6AGE is a useful tool for m6A site prediction. The source code of m6AGE is available at https://github.com/bokunoBike/m6AGE.
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Affiliation(s)
- Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun, China
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Rui Guo
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun, China
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun, China
| | - Sen Yang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun, China
| | - Xuemei Hu
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun, China
| | - Kai He
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun, China
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31
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Zhang Z, Cui F, Lin C, Zhao L, Wang C, Zou Q. Critical downstream analysis steps for single-cell RNA sequencing data. Brief Bioinform 2021; 22:6210064. [PMID: 33822873 DOI: 10.1093/bib/bbab105] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/20/2021] [Accepted: 03/09/2021] [Indexed: 12/13/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.
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Affiliation(s)
- Zilong Zhang
- University of Electronic Science and Technology of China
| | | | | | | | | | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
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32
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Ahmed S, Rahman A, Hasan MAM, Islam MKB, Rahman J, Ahmad S. predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance. PLoS One 2021; 16:e0249396. [PMID: 33793659 PMCID: PMC8016359 DOI: 10.1371/journal.pone.0249396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately. In this study, a novel computational tool, referred to as predPhogly-Site, has been developed to predict phosphoglycerylation sites in the protein. It has effectively utilized the probabilistic sequence-coupling information among the nearby amino acid residues of phosphoglycerylation sites along with a variable cost adjustment for the skewed training dataset to enhance the prediction characteristics. It has achieved around 99% accuracy with more than 0.96 MCC and 0.97 AUC in both 10-fold cross-validation and independent test. Even, the standard deviation in 10-fold cross-validation is almost negligible. This performance indicates that predPhogly-Site remarkably outperformed the existing prediction tools and can be used as a promising predictor, preferably with its web interface at http://103.99.176.239/predPhogly-Site.
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Affiliation(s)
- Sabit Ahmed
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
- * E-mail:
| | - Afrida Rahman
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md. Al Mehedi Hasan
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md Khaled Ben Islam
- Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
| | - Julia Rahman
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
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Charoenkwan P, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides. Bioinformatics 2021; 37:2556-2562. [PMID: 33638635 DOI: 10.1093/bioinformatics/btab133] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/08/2021] [Accepted: 02/24/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desira-ble. RESULTS In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of > 8% accuracy and >16% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research. AVAILABILITY The user-friendly web server of the proposed BERT4Bitter is freely accessible at: http://pmlab.pythonanywhere.com/BERT4Bitter. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | | | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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34
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Tang B, Wang Y, Chen Y, Li M, Tao Y. A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression. Front Cell Dev Biol 2021; 8:620746. [PMID: 33585460 PMCID: PMC7874010 DOI: 10.3389/fcell.2020.620746] [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: 10/23/2020] [Accepted: 11/17/2020] [Indexed: 11/13/2022] Open
Abstract
Carcinoma diagnosis and prognosis are still hindered by the lack of effective prediction model and integration methodology. We proposed a novel feature selection with orthogonal regression (FSOR) method to resolve predictor selection and performance optimization. Functional enrichment and clinical outcome analyses with multi-omics information validated the method's robustness in the early-stage prognosis of lung adenocarcinoma. Furthermore, compared with the classic least absolute shrinkage and selection operator (LASSO) regression method [the averaged 1- to 4-years predictive area under the receiver operating characteristic curve (AUC) measure, 0.6998], the proposed one outperforms more accurately by 0.7208 with fewer predictors, particularly its averaged 1- to 3-years AUC reaches 0.723, vs. classic 0.6917 on The Cancer Genome Atlas (TCGA). In sum, the proposed method can deliver better prediction performance for early-stage prognosis and improve therapy strategy but with less predictor consideration and computation burden. The self-composed running scripts, together with the processed results, are available at https://github.com/gladex/PM-FSOR.
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Affiliation(s)
- Binhua Tang
- Epigenetics & Function Group, Hohai University, Nanjing, China
| | - Yuqi Wang
- Epigenetics & Function Group, Hohai University, Nanjing, China
| | - Yu Chen
- Epigenetics & Function Group, Hohai University, Nanjing, China
| | - Ming Li
- Epigenetics & Function Group, Hohai University, Nanjing, China
| | - Yongfeng Tao
- Epigenetics & Function Group, Hohai University, Nanjing, China
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35
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Feng P, Liu W, Huang C, Tang Z. Classifying the superfamily of small heat shock proteins by using g-gap dipeptide compositions. Int J Biol Macromol 2020; 167:1575-1578. [PMID: 33212104 DOI: 10.1016/j.ijbiomac.2020.11.111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/02/2020] [Accepted: 11/13/2020] [Indexed: 01/16/2023]
Abstract
Small heat shock protein (sHSP) is a superfamily of molecular chaperone and is found from archaea to human. Recent researches have demonstrated that sHSPs participate in a series of biological processes and are even closely associated with serious diseases. Since sHSP is a very large superfamily and members from different superfamilies exhibit distinct functions, accurate classification of the subfamily of sHSP will be helpful for unrevealing its functions. In the present work, a support vector machine-based method was proposed to classify the subfamily of sHSPs. In the 10-fold cross validation test, an overall accuracy of 93.25% was obtained for classifying the subfamily of sHSPs. The superiority of the proposed method was also demonstrated by comparing it with the other methods. It is anticipated that the proposed method will become a useful tool for classifying the subfamily of sHSPs.
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Affiliation(s)
- Pengmian Feng
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China.
| | - Weiwei Liu
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Cong Huang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Zhaohui Tang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
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