1
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Zhou Y, Cui H, Liu D, Wang W. MSTCRB: Predicting circRNA-RBP interaction by extracting multi-scale features based on transformer and attention mechanism. Int J Biol Macromol 2024; 278:134805. [PMID: 39153682 DOI: 10.1016/j.ijbiomac.2024.134805] [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: 05/30/2024] [Revised: 08/14/2024] [Accepted: 08/14/2024] [Indexed: 08/19/2024]
Abstract
CircRNAs play vital roles in biological system mainly through binding RNA-binding protein (RBP), which is essential for regulating physiological processes in vivo and for identifying causal disease variants. Therefore, predicting interactions between circRNA and RBP is a critical step for the discovery of new therapeutic agents. Application of various deep-learning models in bioinformatics has significantly improved prediction and classification performance. However, most of existing prediction models are only applicable to specific type of RNA or RNA with simple characteristics. In this study, we proposed an attractive deep learning model, MSTCRB, based on transformer and attention mechanism for extracting multi-scale features to predict circRNA-RBP interactions. Therein, K-mer and KNF encoding are employed to capture the global sequence features of circRNA, NCP and DPCP encoding are utilized to extract local sequence features, and the CDPfold method is applied to extract structural features. In order to improve prediction performance, optimized transformer framework and attention mechanism were used to integrate these multi-scale features. We compared our model's performance with other five state-of-the-art methods on 37 circRNA datasets and 31 linear RNA datasets. The results show that the average AUC value of MSTCRB reaches 98.45 %, which is better than other comparative methods. All of above datasets are deposited in https://github.com/chy001228/MSTCRB_database.git and source code are available from https://github.com/chy001228/MSTCRB.git.
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Affiliation(s)
- Yun Zhou
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China; Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.
| | - Haoyu Cui
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Dong Liu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China; Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.
| | - Wei Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China; Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.
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2
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Liu L, Wei Y, Tan Z, Zhang Q, Sun J, Zhao Q. Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network. Interdiscip Sci 2024; 16:635-648. [PMID: 38381315 DOI: 10.1007/s12539-024-00616-z] [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: 11/14/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/22/2024]
Abstract
Circular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, computational methods have been proposed to predict the circRNA-RBP interaction. However, these methods have problems of single feature extraction. Therefore, we propose a novel model called circ-FHN, which utilizes only circRNA sequences to predict circRNA-RBP interactions. The circ-FHN approach involves feature coding and a hybrid deep learning model. Feature coding takes into account the physicochemical properties of circRNA sequences and employs four coding methods to extract sequence features. The hybrid deep structure comprises a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU). The CNN learns high-level abstract features, while the BiGRU captures long-term dependencies in the sequence. To assess the effectiveness of circ-FHN, we compared it to other computational methods on 16 datasets and conducted ablation experiments. Additionally, we conducted motif analysis. The results demonstrate that circ-FHN exhibits exceptional performance and surpasses other methods. circ-FHN is freely available at https://github.com/zhaoqi106/circ-FHN .
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Affiliation(s)
- Liwei Liu
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
- Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou, 571158, China
| | - Yixin Wei
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
| | - Zhebin Tan
- College of Software, Dalian Jiaotong University, Dalian, 116028, China
| | - Qi Zhang
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China.
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
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3
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Wang C, Liang C. CircCNNs, a convolutional neural network framework to better understand the biogenesis of exonic circRNAs. Sci Rep 2024; 14:18982. [PMID: 39152135 PMCID: PMC11329666 DOI: 10.1038/s41598-024-69262-1] [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: 03/03/2024] [Accepted: 08/02/2024] [Indexed: 08/19/2024] Open
Abstract
Circular RNAs (circRNAs) as biomarkers for cancer detection have been extensively explored, however, the biogenesis mechanism is still elusive. In contrast to linear splicing (LS) involved in linear transcript formation, the so-called back splicing (BS) process has been proposed to explain circRNA formation. To investigate the potential mechanism of BS via the machine learning approach, we curated a high-quality BS and LS exon pairs dataset with evidence-based stringent filtering. Two convolutional neural networks (CNN) base models with different structures for processing splicing junction sequences including motif extraction were created and compared after extensive hyperparameter tuning. In contrast to the previous study, we are able to identify motifs corresponding to well-established BS-associated genes such as MBNL1, QKI, and ESPR2. Importantly, despite prevalent high false positive rates in existing circRNA detection pipelines and databases, our base models demonstrated a notable high specificity (greater than 90%). To further improve the model performance, a novo fast numerical method was proposed and implemented to calculate the reverse complementary matches (RCMs) crossing two flanking regions and within each flanking region of exon pairs. Our CircCNNs framework that incorporated RCM information into the optimal base models further reduced the false positive rates leading to 88% prediction accuracy.
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Affiliation(s)
- Chao Wang
- Department of Biology, Miami University, Oxford, OH, 45056, USA.
| | - Chun Liang
- Department of Biology, Miami University, Oxford, OH, 45056, USA.
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4
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Diao B, Luo J, Guo Y. A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs. Brief Funct Genomics 2024; 23:314-324. [PMID: 38576205 DOI: 10.1093/bfgp/elae010] [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: 12/06/2023] [Revised: 02/25/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.
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Affiliation(s)
- Biyu Diao
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Jin Luo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Yu Guo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
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5
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Yang S, Kim SH, Yang E, Kang M, Joo JY. Molecular insights into regulatory RNAs in the cellular machinery. Exp Mol Med 2024; 56:1235-1249. [PMID: 38871819 PMCID: PMC11263585 DOI: 10.1038/s12276-024-01239-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Eunjeong Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea.
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6
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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024; 56:1293-1321. [PMID: 38871816 PMCID: PMC11263376 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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7
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Digby B, Finn S, Ó Broin P. Computational approaches and challenges in the analysis of circRNA data. BMC Genomics 2024; 25:527. [PMID: 38807085 PMCID: PMC11134749 DOI: 10.1186/s12864-024-10420-0] [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: 02/13/2024] [Accepted: 05/15/2024] [Indexed: 05/30/2024] Open
Abstract
Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.
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Affiliation(s)
- Barry Digby
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland.
| | - Stephen Finn
- Discipline of Histopathology, School of Medicine, Trinity College Dublin and Cancer Molecular Diagnostic Laboratory, Dublin, Ireland
| | - Pilib Ó Broin
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
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8
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Lasantha D, Vidanagamachchi S, Nallaperuma S. CRIECNN: Ensemble convolutional neural network and advanced feature extraction methods for the precise forecasting of circRNA-RBP binding sites. Comput Biol Med 2024; 174:108466. [PMID: 38615462 DOI: 10.1016/j.compbiomed.2024.108466] [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: 11/27/2023] [Revised: 03/29/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024]
Abstract
Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches. In this study, we propose CRIECNN (Circular RNA-RBP Interaction predictor using an Ensemble Convolutional Neural Network), a novel ensemble deep learning model that enhances circRNA-RBP binding site prediction accuracy. CRIECNN employs advanced feature extraction methods and evaluates four distinct sequence datasets and encoding techniques (BERT, Doc2Vec, KNF, EIIP). The model consists of an ensemble convolutional neural network, a BiLSTM, and a self-attention mechanism for feature refinement. Our results demonstrate that CRIECNN outperforms state-of-the-art methods in accuracy and performance, effectively predicting circRNA-RBP interactions from both full-length sequences and fragments. This novel strategy makes an enormous advancement in the prediction of circRNA-RBP interactions, improving our understanding of circRNAs and their regulatory roles.
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Affiliation(s)
- Dilan Lasantha
- Department of Computer Science, University of Ruhuna, Sri Lanka.
| | | | - Sam Nallaperuma
- Department of Engineering, University of Cambridge, United Kingdom.
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9
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Zhou Z, Zhang J, Zheng X, Pan Z, Zhao F, Gao Y. CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308115. [PMID: 38308181 PMCID: PMC11005702 DOI: 10.1002/advs.202308115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep's adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.
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Affiliation(s)
- Zihan Zhou
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Jinyang Zhang
- Beijing Institutes of Life ScienceChinese Academy of SciencesBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Xin Zheng
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Zhicheng Pan
- Center for Computational Biology Flatiron InstituteNew York10010USA
| | - Fangqing Zhao
- Beijing Institutes of Life ScienceChinese Academy of SciencesBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Yuan Gao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
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10
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Niu M, Wang C, Chen Y, Zou Q, Qi R, Xu L. CircRNA identification and feature interpretability analysis. BMC Biol 2024; 22:44. [PMID: 38408987 PMCID: PMC10898045 DOI: 10.1186/s12915-023-01804-x] [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: 07/29/2023] [Accepted: 12/18/2023] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation. RESULTS We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification. CONCLUSIONS CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .
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Affiliation(s)
- Mengting Niu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China
- Postdoctoral Innovation Practice Base, Shenzhen Polytechnic University, Shenzhen, 518055, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150000, Heilongjiang, China
| | - Yaojia Chen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No.4 Block 2 North Jianshe Road, Chengdu, 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No.4 Block 2 North Jianshe Road, Chengdu, 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Ren Qi
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China.
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11
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Avila Santos AP, de Almeida BLS, Bonidia RP, Stadler PF, Stefanic P, Mandic-Mulec I, Rocha U, Sanches DS, de Carvalho ACPLF. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biol 2024; 21:1-12. [PMID: 38528797 DOI: 10.1080/15476286.2024.2329451] [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] [Accepted: 01/23/2024] [Indexed: 03/27/2024] Open
Abstract
The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.
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Affiliation(s)
- Anderson P Avila Santos
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research - UFZ GmbH, Leipzig, Saxony, Germany
| | - Breno L S de Almeida
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
| | - Robson P Bonidia
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Computer Science, Federal University of Technology - Paraná, UTFPR, Cornélio Procópio, Brazil
| | - Peter F Stadler
- Department of Computer Science and Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Saxony, Germany
| | - Polonca Stefanic
- Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Ines Mandic-Mulec
- Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Ulisses Rocha
- Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research - UFZ GmbH, Leipzig, Saxony, Germany
| | - Danilo S Sanches
- Department of Computer Science, Federal University of Technology - Paraná, UTFPR, Cornélio Procópio, Brazil
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12
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Pudova E, Kobelyatskaya A, Emelyanova M, Snezhkina A, Fedorova M, Pavlov V, Guvatova Z, Dalina A, Kudryavtseva A. Non-Coding RNAs and the Development of Chemoresistance to Docetaxel in Prostate Cancer: Regulatory Interactions and Approaches Based on Machine Learning Methods. Life (Basel) 2023; 13:2304. [PMID: 38137905 PMCID: PMC10744715 DOI: 10.3390/life13122304] [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: 09/18/2023] [Revised: 11/30/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Chemotherapy based on taxane-class drugs is the gold standard for treating advanced stages of various oncological diseases. However, despite the favorable response trends, most patients eventually develop resistance to this therapy. Drug resistance is the result of a combination of different events in the tumor cells under the influence of the drug, a comprehensive understanding of which has yet to be determined. In this review, we examine the role of the major classes of non-coding RNAs in the development of chemoresistance in the case of prostate cancer, one of the most common and socially significant types of cancer in men worldwide. We will focus on recent findings from experimental studies regarding the prognostic potential of the identified non-coding RNAs. Additionally, we will explore novel approaches based on machine learning to study these regulatory molecules, including their role in the development of drug resistance.
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Affiliation(s)
- Elena Pudova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | | | - Marina Emelyanova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Anastasiya Snezhkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Maria Fedorova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Vladislav Pavlov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Zulfiya Guvatova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
- Russian Clinical Research Center for Gerontology, Pirogov Russian National Research Medical University, Ministry of Healthcare of the Russian Federation, 129226 Moscow, Russia
| | - Alexandra Dalina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Anna Kudryavtseva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
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13
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Jia J, Wei Z, Sun M. EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning. BMC Bioinformatics 2023; 24:397. [PMID: 37880673 PMCID: PMC10598967 DOI: 10.1186/s12859-023-05543-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND N6, 2'-O-dimethyladenosine (m6Am) is an abundant RNA methylation modification on vertebrate mRNAs and is present in the transcription initiation region of mRNAs. It has recently been experimentally shown to be associated with several human disorders, including obesity genes, and stomach cancer, among others. As a result, N6,2'-O-dimethyladenosine (m6Am) site will play a crucial part in the regulation of RNA if it can be correctly identified. RESULTS This study proposes a novel deep learning-based m6Am prediction model, EMDL_m6Am, which employs one-hot encoding to expressthe feature map of the RNA sequence and recognizes m6Am sites by integrating different CNN models via stacking. Including DenseNet, Inflated Convolutional Network (DCNN) and Deep Multiscale Residual Network (MSRN), the sensitivity (Sn), specificity (Sp), accuracy (ACC), Mathews correlation coefficient (MCC) and area under the curve (AUC) of our model on the training data set reach 86.62%, 88.94%, 87.78%, 0.7590 and 0.8778, respectively, and the prediction results on the independent test set are as high as 82.25%, 79.72%, 80.98%, 0.6199, and 0.8211. CONCLUSIONS In conclusion, the experimental results demonstrated that EMDL_m6Am greatly improved the predictive performance of the m6Am sites and could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDL-m6Am .
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Affiliation(s)
- Jianhua Jia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
| | - Zhangying Wei
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
| | - Mingwei Sun
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China
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14
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Feng XY, Zhu SX, Pu KJ, Huang HJ, Chen YQ, Wang WT. New insight into circRNAs: characterization, strategies, and biomedical applications. Exp Hematol Oncol 2023; 12:91. [PMID: 37828589 PMCID: PMC10568798 DOI: 10.1186/s40164-023-00451-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/23/2023] [Indexed: 10/14/2023] Open
Abstract
Circular RNAs (circRNAs) are a class of covalently closed, endogenous ncRNAs. Most circRNAs are derived from exonic or intronic sequences by precursor RNA back-splicing. Advanced high-throughput RNA sequencing and experimental technologies have enabled the extensive identification and characterization of circRNAs, such as novel types of biogenesis, tissue-specific and cell-specific expression patterns, epigenetic regulation, translation potential, localization and metabolism. Increasing evidence has revealed that circRNAs participate in diverse cellular processes, and their dysregulation is involved in the pathogenesis of various diseases, particularly cancer. In this review, we systematically discuss the characterization of circRNAs, databases, challenges for circRNA discovery, new insight into strategies used in circRNA studies and biomedical applications. Although recent studies have advanced the understanding of circRNAs, advanced knowledge and approaches for circRNA annotation, functional characterization and biomedical applications are continuously needed to provide new insights into circRNAs. The emergence of circRNA-based protein translation strategy will be a promising direction in the field of biomedicine.
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Affiliation(s)
- Xin-Yi Feng
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Shun-Xin Zhu
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Ke-Jia Pu
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Heng-Jing Huang
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Yue-Qin Chen
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
| | - Wen-Tao Wang
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
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15
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Sutanto K, Turcotte M. Assessing Global-Local Secondary Structure Fingerprints to Classify RNA Sequences With Deep Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2736-2747. [PMID: 34633933 DOI: 10.1109/tcbb.2021.3118358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
RNA elements that are transcribed but not translated into proteins are called non-coding RNAs (ncRNAs). They play wide-ranging roles in biological processes and disorders. Just like proteins, their structure is often intimately linked to their function. Many examples have been documented where structure is conserved across taxa despite sequence divergence. Thus, structure is often used to identify function. Specifically, the secondary structure is predicted and ncRNAs with similar structures are assumed to have same or similar functions. However, a strand of RNA can fold into multiple possible structures, and some strands even fold differently in vivo and in vitro. Furthermore, ncRNAs often function as RNA-protein complexes, which can affect structure. Because of these, we hypothesized using one structure per sequence may discard information, possibly resulting in poorer classification accuracy. Therefore, we propose using secondary structure fingerprints, comprising two categories: a higher-level representation derived from RNA-As-Graphs (RAG), and free energy fingerprints based on a curated repertoire of small structural motifs. The fingerprints take into account the difference between global and local structural matches. We also evaluated our deep learning architecture with k-mers. By combining our global-local fingerprints with 6-mer, we achieved an accuracy, precision, and recall of 91.04%, 91.10%, and 91.00%.
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16
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Wang C, Zou Q. Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE. BMC Biol 2023; 21:12. [PMID: 36694239 PMCID: PMC9875434 DOI: 10.1186/s12915-023-01510-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work. RESULTS In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects. CONCLUSIONS DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .
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Affiliation(s)
- Chao Wang
- grid.411307.00000 0004 1790 5236School of Software Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Quan Zou
- grid.54549.390000 0004 0369 4060Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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17
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Liu T, Zou B, He M, Hu Y, Dou Y, Cui T, Tan P, Li S, Rao S, Huang Y, Liu S, Cai K, Wang D. LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism. Brief Bioinform 2023; 24:6961607. [PMID: 36575567 DOI: 10.1093/bib/bbac579] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/11/2022] [Accepted: 11/28/2022] [Indexed: 12/29/2022] Open
Abstract
Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as 'dual functional lncRNAs'. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.
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Affiliation(s)
- Tianyuan Liu
- Department of Hematology and Oncology, Shenzhen Children's Hospital, Shenzhen 518038, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Bohao Zou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Department of Statistics, University of California Davis, Davis, California, USA
| | - Manman He
- State Key Laboratory of Medical Molecular Biology, Key Laboratorytar of RNA Regulation and Hematopoiesis, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, CAMS and Peking Union Medical College, Beijing 100005, China
| | - Yongfei Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Dermatology Hospital, Southern Medical University, Guangzhou, 510091, China
| | - Yiying Dou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Puwen Tan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Shaobin Li
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Shuan Rao
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Yan Huang
- Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Sixi Liu
- Department of Hematology and Oncology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Kaican Cai
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Dermatology Hospital, Southern Medical University, Guangzhou, 510091, China.,Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350122, China
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18
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Shen Z, Shao YL, Liu W, Zhang Q, Yuan L. Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks. BMC Genomics 2022; 23:581. [PMID: 35962324 PMCID: PMC9373444 DOI: 10.1186/s12864-022-08820-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/03/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Circular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism of CircRNAs formation can help reveal the role of CircRNAs in various biological processes mentioned above. Back-splicing is important for CircRNAs formation. Back-splicing sites prediction helps uncover the mysteries of CircRNAs formation. Several methods were proposed for back-splicing sites prediction or circRNA-realted prediction tasks. Model performance was constrained by poor feature learning and using ability. RESULTS In this study, CircCNN was proposed to predict pre-mRNA back-splicing sites. Convolution neural network and batch normalization are the main parts of CircCNN. Experimental results on three datasets show that CircCNN outperforms other baseline models. Moreover, PPM (Position Probability Matrix) features extract by CircCNN were converted as motifs. Further analysis reveals that some of motifs found by CircCNN match known motifs involved in gene expression regulation, the distribution of motif and special short sequence is important for pre-mRNA back-splicing. CONCLUSIONS In general, the findings in this study provide a new direction for exploring CircRNA-related gene expression regulatory mechanism and identifying potential targets for complex malignant diseases. The datasets and source code of this study are freely available at: https://github.com/szhh521/CircCNN .
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Affiliation(s)
- Zhen Shen
- School of Computer and Software, Nanyang Institute of Technology, Changjiang Road 80, Nanyang, 473004, Henan, China
| | - Yan Ling Shao
- School of Computer and Software, Nanyang Institute of Technology, Changjiang Road 80, Nanyang, 473004, Henan, China
| | - Wei Liu
- School of Computer and Software, Nanyang Institute of Technology, Changjiang Road 80, Nanyang, 473004, Henan, China
| | - Qinhu Zhang
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Siping Road 1239, Shanghai, 200092, China
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai, 201804, China
| | - Lin Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Daxue Road 3501, Jinan, 250353, Shandong, China.
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19
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Xu D, Yuan W, Fan C, Liu B, Lu MZ, Zhang J. Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants. FRONTIERS IN PLANT SCIENCE 2022; 13:890663. [PMID: 35498708 PMCID: PMC9048598 DOI: 10.3389/fpls.2022.890663] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/28/2022] [Indexed: 06/01/2023]
Affiliation(s)
- Dong Xu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenya Yuan
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Chunjie Fan
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, China
| | - Bobin Liu
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, School of Wetlands, Yancheng Teachers University, Yancheng, China
| | - Meng-Zhu Lu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Jin Zhang
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
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20
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Sharma AR, Banerjee S, Bhattacharya M, Saha A, Lee SS, Chakraborty C. Recent progress of circular RNAs in different types of human cancer: Technological landscape, clinical opportunities and challenges (Review). Int J Oncol 2022; 60:56. [PMID: 35362541 DOI: 10.3892/ijo.2022.5346] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/10/2022] [Indexed: 11/11/2022] Open
Abstract
Circular RNAs (circRNAs) are a novel class of endogenous non‑coding RNAs that have been recently regarded as functionally active. CircRNAs are remarkably stable and known to possess several biological functions such as microRNA sponging, regulating transcription and splicing and occasionally acting as polypeptide‑producing templates. CircRNAs show tissue‑specific expression and have been reported to be associated with the progression of several types of malignancies. Given the recent progress in genome sequencing and bioinformatics techniques, a rapid increment in the biological role of circRNAs has been observed. Concurrently, the patent search from different patent databases shows that the patent number of circRNA is increasing very quickly. These phenomena reveal a rapid development of the technological landscape. In the present review, the recent progress on circRNAs in various kinds of cancer has been investigated and their function as biomarkers or therapeutic targets and their technological landscape have been appreciated. A new insight into circRNAs structure and functional capabilities in cancer has been reviewed. Continually increasing knowledge on their critical role during cancer progression is projecting them as biomarkers or therapeutic targets for various kinds of cancer. Thus, recent updates on the functional role of circRNAs in terms of the technological landscape, clinical opportunities (biomarkers and therapeutic targets), and challenges in cancer have been illustrated.
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Affiliation(s)
- Ashish Ranjan Sharma
- Institute for Skeletal Aging and Orthopedic Surgery, Hallym University‑Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon 24252, Republic of Korea
| | - Shreya Banerjee
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, India
| | - Abinit Saha
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging and Orthopedic Surgery, Hallym University‑Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon 24252, Republic of Korea
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
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21
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Raad J, Bugnon LA, Milone DH, Stegmayer G. miRe2e: a full end-to-end deep model based on transformers for prediction of pre-miRNAs. Bioinformatics 2022; 38:1191-1197. [PMID: 34875006 DOI: 10.1093/bioinformatics/btab823] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/29/2021] [Accepted: 12/01/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION MicroRNAs (miRNAs) are small RNA sequences with key roles in the regulation of gene expression at post-transcriptional level in different species. Accurate prediction of novel miRNAs is needed due to their importance in many biological processes and their associations with complicated diseases in humans. Many machine learning approaches were proposed in the last decade for this purpose, but requiring handcrafted features extraction to identify possible de novo miRNAs. More recently, the emergence of deep learning (DL) has allowed the automatic feature extraction, learning relevant representations by themselves. However, the state-of-art deep models require complex pre-processing of the input sequences and prediction of their secondary structure to reach an acceptable performance. RESULTS In this work, we present miRe2e, the first full end-to-end DL model for pre-miRNA prediction. This model is based on Transformers, a neural architecture that uses attention mechanisms to infer global dependencies between inputs and outputs. It is capable of receiving the raw genome-wide data as input, without any pre-processing nor feature engineering. After a training stage with known pre-miRNAs, hairpin and non-harpin sequences, it can identify all the pre-miRNA sequences within a genome. The model has been validated through several experimental setups using the human genome, and it was compared with state-of-the-art algorithms obtaining 10 times better performance. AVAILABILITY AND IMPLEMENTATION Webdemo available at https://sinc.unl.edu.ar/web-demo/miRe2e/ and source code available for download at https://github.com/sinc-lab/miRe2e. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jonathan Raad
- Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
| | - Leandro A Bugnon
- Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
| | - Diego H Milone
- Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
| | - Georgina Stegmayer
- Informatics Department, Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
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22
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Niu M, Zou Q, Lin C. CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach. PLoS Comput Biol 2022; 18:e1009798. [PMID: 35051187 PMCID: PMC8806072 DOI: 10.1371/journal.pcbi.1009798] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 02/01/2022] [Accepted: 01/02/2022] [Indexed: 02/06/2023] Open
Abstract
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git. More and more evidences show that circular RNA can directly bind to proteins and participate in countless different biological processes. The calculation method can quickly and accurately predict the binding site of circular RNA and RBP. In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL. It can effectively learn high-level feature representations. The performance of the model was verified through comparative experiments of different feature extraction algorithms, different deep learning models and classifier models. Moreover, the CRBPDL model was applied to 31 linear RNAs, and the effectiveness of our method was proved by comparison with the results of current excellent algorithms. It is expected that the CRBPDL model can effectively predict the binding site of circular RNA-RBP and provide reliable candidates for further biological experiments.
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Affiliation(s)
- Mengting Niu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Chen Lin
- School of Informatics, Xiamen University, Xiamen, China
- * E-mail:
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23
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Wang C, Ju Y, Zou Q, Lin C. DeepAc4C: a convolutional neural network model with hybrid features composed of physicochemical patterns and distributed representation information for identification of N4-acetylcytidine in mRNA. Bioinformatics 2021; 38:52-57. [PMID: 34427581 DOI: 10.1093/bioinformatics/btab611] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/17/2021] [Accepted: 08/20/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION N4-acetylcytidine (ac4C) is the only acetylation modification that has been characterized in eukaryotic RNA, and is correlated with various human diseases. Laboratory identification of ac4C is complicated by factors, such as sample hydrolysis and high cost. Unfortunately, existing computational methods to identify ac4C do not achieve satisfactory performance. RESULTS We developed a novel tool, DeepAc4C, which identifies ac4C using convolutional neural networks (CNNs) using hybrid features composed of physicochemical patterns and a distributed representation of nucleic acids. Our results show that the proposed model achieved better and more balanced performance than existing predictors. Furthermore, we evaluated the effect that specific features had on the model predictions and their interaction effects. Several interesting sequence motifs specific to ac4C were identified. AVAILABILITY AND IMPLEMENTATION The webserver is freely accessible at https://ac4c.webmalab.cn/, the source code and datasets are accessible at Zenodo with URL https://doi.org/10.5281/zenodo.5138047 and Github with URL https://github.com/wangchao-malab/DeepAc4C. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Chen Lin
- School of Informatics, Xiamen University, Xiamen 361005, China
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24
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Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA. Genes (Basel) 2021; 12:genes12122018. [PMID: 34946967 PMCID: PMC8701965 DOI: 10.3390/genes12122018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/14/2021] [Accepted: 12/17/2021] [Indexed: 12/23/2022] Open
Abstract
Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture fed with a raw sequence, to learn the sparse features of RNA sequences and to accomplish the circRNAs identification task. The model outperformed the state-of-art models. Moreover, following the validation of the attention mechanism effectiveness by the handwritten digit dataset, the key sequence loci underlying circRNA’s recognition were obtained based on the corresponding attention score. Then, motif enrichment analysis identified some of the key motifs for circRNA formation. In conclusion, we designed deep learning network architecture suitable for learning gene sequences with sparse features and implemented it for the circRNA identification task, and the model has strong representation capability in the indication of some key loci.
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25
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Micheel J, Safrastyan A, Wollny D. Advances in Non-Coding RNA Sequencing. Noncoding RNA 2021; 7:70. [PMID: 34842804 PMCID: PMC8628893 DOI: 10.3390/ncrna7040070] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022] Open
Abstract
Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.
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Affiliation(s)
| | | | - Damian Wollny
- RNA Bioinformatics/High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University, 07743 Jena, Germany; (J.M.); (A.S.)
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26
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Niu M, Ju Y, Lin C, Zou Q. Characterizing viral circRNAs and their application in identifying circRNAs in viruses. Brief Bioinform 2021; 23:6377516. [PMID: 34585234 DOI: 10.1093/bib/bbab404] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/23/2021] [Accepted: 09/02/2021] [Indexed: 01/19/2023] Open
Abstract
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism, which play an important role in a variety of biological activities. Viruses can encode circRNA, and viral circRNAs have been found in multiple single-stranded and double-stranded viruses. However, the characteristics and functions of viral circRNAs remain unknown. Sequence alignment showed that viral circRNAs are less conserved than circRNAs in animal, indicating that the viral circRNAs may evolve rapidly. Through the analysis of the sequence characteristics of viral circRNAs and circRNAs in animal, it was found that viral circRNAs and animals circRNAs are similar in nucleic acid composition, but have obvious differences in secondary structure and autocorrelation characteristics. Based on these characteristics of viral circRNAs, machine learning algorithms were employed to construct a prediction model to identify viral circRNA. Additionally, analysis of the interaction between viral circRNA and miRNAs showed that viral circRNA is expected to interact with 518 human miRNAs, and preliminary analysis of the role of viral circRNA. And it has been also found that viral circRNAs may be involved in many KEGG pathways related to nervous system and cancer. We curated an online server, and the data and code are available: http://server.malab.cn/viral-CircRNA/.
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Affiliation(s)
- Mengting Niu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China
| | - Chen Lin
- School of Informatics, Xiamen University, Xiamen, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
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27
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Asim MN, Ibrahim MA, Imran Malik M, Dengel A, Ahmed S. Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs. Int J Mol Sci 2021; 22:8719. [PMID: 34445436 PMCID: PMC8395733 DOI: 10.3390/ijms22168719] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 02/06/2023] Open
Abstract
Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.
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Affiliation(s)
- Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Muhammad Ali Ibrahim
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Muhammad Imran Malik
- National Center for Artificial Intelligence (NCAI), National University of Sciences and Technology, Islamabad 44000, Pakistan;
- School of Electrical Engineering & Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Andreas Dengel
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- DeepReader GmbH, Trippstadter Str. 122, 67663 Kaiserslautern, Germany
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28
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Jiang JY, Ju CJT, Hao J, Chen M, Wang W. JEDI: circular RNA prediction based on junction encoders and deep interaction among splice sites. Bioinformatics 2021; 37:i289-i298. [PMID: 34252942 PMCID: PMC8336595 DOI: 10.1093/bioinformatics/btab288] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Motivation Circular RNA (circRNA) is a novel class of long non-coding RNAs that have been broadly discovered in the eukaryotic transcriptome. The circular structure arises from a non-canonical splicing process, where the donor site backspliced to an upstream acceptor site. These circRNA sequences are conserved across species. More importantly, rising evidence suggests their vital roles in gene regulation and association with diseases. As the fundamental effort toward elucidating their functions and mechanisms, several computational methods have been proposed to predict the circular structure from the primary sequence. Recently, advanced computational methods leverage deep learning to capture the relevant patterns from RNA sequences and model their interactions to facilitate the prediction. However, these methods fail to fully explore positional information of splice junctions and their deep interaction. Results We present a robust end-to-end framework, Junction Encoder with Deep Interaction (JEDI), for circRNA prediction using only nucleotide sequences. JEDI first leverages the attention mechanism to encode each junction site based on deep bidirectional recurrent neural networks and then presents the novel cross-attention layer to model deep interaction among these sites for backsplicing. Finally, JEDI can not only predict circRNAs but also interpret relationships among splice sites to discover backsplicing hotspots within a gene region. Experiments demonstrate JEDI significantly outperforms state-of-the-art approaches in circRNA prediction on both isoform level and gene level. Moreover, JEDI also shows promising results on zero-shot backsplicing discovery, where none of the existing approaches can achieve. Availability and implementation The implementation of our framework is available at https://github.com/hallogameboy/JEDI. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jyun-Yu Jiang
- Department of Computer Science, University of California, Los Angeles, CA 90024, USA
| | - Chelsea J-T Ju
- Department of Computer Science, University of California, Los Angeles, CA 90024, USA
| | - Junheng Hao
- Department of Computer Science, University of California, Los Angeles, CA 90024, USA
| | - Muhao Chen
- Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
| | - Wei Wang
- Department of Computer Science, University of California, Los Angeles, CA 90024, USA
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29
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Function of Circular RNAs in Fish and Their Potential Application as Biomarkers. Int J Mol Sci 2021; 22:ijms22137119. [PMID: 34281172 PMCID: PMC8268770 DOI: 10.3390/ijms22137119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/25/2021] [Accepted: 06/27/2021] [Indexed: 11/16/2022] Open
Abstract
Circular RNAs (circRNAs) are an emerging class of regulatory RNAs with a covalently closed-loop structure formed during pre-mRNA splicing. Recent advances in high-throughput RNA sequencing and circRNA-specific computational tools have driven the development of novel approaches to their identification and functional characterization. CircRNAs are stable, developmentally regulated, and show tissue- and cell-type-specific expression across different taxonomic groups. They play a crucial role in regulating various biological processes at post-transcriptional and translational levels. However, the involvement of circRNAs in fish immunity has only recently been recognized. There is also broad evidence in mammals that the timely expression of circRNAs in muscle plays an essential role in growth regulation but our understanding of their expression and function in teleosts is still very limited. Here, we discuss the available knowledge about circRNAs and their role in growth and immunity in vertebrates from a comparative perspective, with emphasis on cultured teleost fish. We expect that the interest in teleost circRNAs will increase substantially soon, and we propose that they may be used as biomarkers for selective breeding of farmed fish, thus contributing to the sustainability of the aquaculture sector.
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30
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Song B, Li Z, Lin X, Wang J, Wang T, Fu X. Pretraining model for biological sequence data. Brief Funct Genomics 2021; 20:181-195. [PMID: 34050350 PMCID: PMC8194843 DOI: 10.1093/bfgp/elab025] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/13/2021] [Accepted: 04/21/2021] [Indexed: 12/26/2022] Open
Abstract
With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.
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Affiliation(s)
| | | | | | | | | | - Xiangzheng Fu
- Corresponding author: Xiangzheng Fu, College of Information Science and Engineering, Hunan University, Changsha, Hunan, China. Tel: 86-0731-88821907; E-mail:
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31
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Liu D, Fang L. Current research on circular RNAs and their potential clinical implications in breast cancer. Cancer Biol Med 2021; 18:j.issn.2095-3941.2020.0275. [PMID: 34018386 PMCID: PMC8330541 DOI: 10.20892/j.issn.2095-3941.2020.0275] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 11/26/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer (BC) is one of the most common cancers and the leading causes of death among women worldwide, and its morbidity rate is growing. Discovery of novel biomarkers is necessary for early BC detection, treatment, and prognostication. Circular RNAs (circRNAs), a novel type of endogenous non-coding RNAs with covalently closed continuous loops, have been found to have a crucial role in tumorigenesis. Studies have demonstrated that circRNAs are aberrantly expressed in the tumor tissues and plasma of patients with BC, and they modulate gene expression affecting the proliferation, metastasis, and chemoresistance of BC by specifically binding and regulating the expression of microRNAs (miRNAs). Therefore, circRNAs can be used as novel potential diagnostic and prognostic markers, and therapeutic targets for BC. This article summarizes the properties, functions, and regulatory mechanisms of circRNAs, particularly current research on their association with BC proliferation, metastasis, and chemoresistance.
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Affiliation(s)
- Diya Liu
- Department of Thyroid and Breast Diseases, Shanghai Tenth People’s Hospital, Shanghai 200070, China
| | - Lin Fang
- Department of Thyroid and Breast Diseases, Shanghai Tenth People’s Hospital, Shanghai 200070, China
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32
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Bonidia RP, Sampaio LDH, Domingues DS, Paschoal AR, Lopes FM, de Carvalho ACPLF, Sanches DS. Feature extraction approaches for biological sequences: a comparative study of mathematical features. Brief Bioinform 2021; 22:6135010. [PMID: 33585910 DOI: 10.1093/bib/bbab011] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 12/13/2020] [Accepted: 01/07/2021] [Indexed: 11/14/2022] Open
Abstract
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification. Availability: https://github.com/Bonidia/FeatureExtraction_BiologicalSequences.
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Affiliation(s)
- Robson P Bonidia
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil.,Institute of Mathematics and Computer Sciences, University of São Paulo - USP, São Carlos, 13566-590, Brazil
| | - Lucas D H Sampaio
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
| | - Douglas S Domingues
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil.,Department of Botany, Institute of Biosciences, São Paulo State University (UNESP), Rio Claro 13506-900, Brazil
| | - Alexandre R Paschoal
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
| | - Fabrício M Lopes
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
| | - André C P L F de Carvalho
- Institute of Mathematics and Computer Sciences, University of São Paulo - USP, São Carlos, 13566-590, Brazil
| | - Danilo S Sanches
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
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33
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CircNet: an encoder–decoder-based convolution neural network (CNN) for circular RNA identification. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05673-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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34
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MirLocPredictor: A ConvNet-Based Multi-Label MicroRNA Subcellular Localization Predictor by Incorporating k-Mer Positional Information. Genes (Basel) 2020; 11:genes11121475. [PMID: 33316943 PMCID: PMC7763197 DOI: 10.3390/genes11121475] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 02/06/2023] Open
Abstract
MicroRNAs (miRNA) are small noncoding RNA sequences consisting of about 22 nucleotides that are involved in the regulation of almost 60% of mammalian genes. Presently, there are very limited approaches for the visualization of miRNA locations present inside cells to support the elucidation of pathways and mechanisms behind miRNA function, transport, and biogenesis. MIRLocator, a state-of-the-art tool for the prediction of subcellular localization of miRNAs makes use of a sequence-to-sequence model along with pretrained k-mer embeddings. Existing pretrained k-mer embedding generation methodologies focus on the extraction of semantics of k-mers. However, in RNA sequences, positional information of nucleotides is more important because distinct positions of the four nucleotides define the function of an RNA molecule. Considering the importance of the nucleotide position, we propose a novel approach (kmerPR2vec) which is a fusion of positional information of k-mers with randomly initialized neural k-mer embeddings. In contrast to existing k-mer-based representation, the proposed kmerPR2vec representation is much more rich in terms of semantic information and has more discriminative power. Using novel kmerPR2vec representation, we further present an end-to-end system (MirLocPredictor) which couples the discriminative power of kmerPR2vec with Convolutional Neural Networks (CNNs) for miRNA subcellular location prediction. The effectiveness of the proposed kmerPR2vec approach is evaluated with deep learning-based topologies (i.e., Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN)) and by using 9 different evaluation measures. Analysis of the results reveals that MirLocPredictor outperform state-of-the-art methods with a significant margin of 18% and 19% in terms of precision and recall.
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35
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Yang Y, Hou Z, Ma Z, Li X, Wong KC. iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network. Brief Bioinform 2020; 22:5943796. [PMID: 33126261 DOI: 10.1093/bib/bbaa274] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/07/2020] [Accepted: 09/21/2020] [Indexed: 12/19/2022] Open
Abstract
Circular RNAs (circRNAs) are widely expressed in eukaryotes. The genome-wide interactions between circRNAs and RNA-binding proteins (RBPs) can be probed from cross-linking immunoprecipitation with sequencing data. Therefore, computational methods have been developed for identifying RBP binding sites on circRNAs. Unfortunately, those computational methods often suffer from the low discriminative power of feature representations, numerical instability and poor scalability. To address those limitations, we propose a novel computational method called iCircRBP-DHN using deep hierarchical network for discriminating circRNA-RBP binding sites. The network architecture can be regarded as a deep multi-scale residual network followed by bidirectional gated recurrent units (BiGRUs) with the self-attention mechanism, which can simultaneously extract local and global contextual information. Meanwhile, we propose novel encoding schemes by integrating CircRNA2Vec and the K-tuple nucleotide frequency pattern to represent different degrees of nucleotide dependencies. To validate the effectiveness of our proposed iCircRBP-DHN, we compared its performance with other computational methods on 37 circRNAs datasets and 31 linear RNAs datasets, respectively. The experimental results reveal that iCircRBP-DHN can achieve superior performance over those state-of-the-art algorithms. Moreover, we perform motif analysis on circRNAs bound by those different RBPs, demonstrating that our proposed CircRNA2Vec encoding scheme can be promising. The iCircRBP-DHN method is made available at https://github.com/houzl3416/iCircRBP-DHN.
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Affiliation(s)
- Yuning Yang
- School of Information Science and Technology, Northeast Normal University
| | - Zilong Hou
- School of Artificial Intelligence, Jilin University
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University
| | - Ka-Chun Wong
- School of Artificial Intelligence, Jilin University
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36
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Disease-Associated Circular RNAs: From Biology to Computational Identification. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6798590. [PMID: 32908906 PMCID: PMC7450300 DOI: 10.1155/2020/6798590] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/10/2020] [Indexed: 02/07/2023]
Abstract
Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer's disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.
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37
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Zhang G, Deng Y, Liu Q, Ye B, Dai Z, Chen Y, Dai X. Identifying Circular RNA and Predicting Its Regulatory Interactions by Machine Learning. Front Genet 2020; 11:655. [PMID: 32849764 PMCID: PMC7396586 DOI: 10.3389/fgene.2020.00655] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 05/29/2020] [Indexed: 12/11/2022] Open
Abstract
Circular RNA (circRNA) is a closed long non-coding RNA (lncRNA) formed by covalently closed loops through back-splicing. Emerging evidence indicates that circRNA can influence cellular physiology through various molecular mechanisms. Thus, accurate circRNA identification and prediction of its regulatory information are critical for understanding its biogenesis. Although several computational tools based on machine learning have been proposed for circRNA identification, the prediction accuracy remains to be improved. Here, first we present circLGB, a machine learning-based framework to discriminate circRNA from other lncRNAs. circLGB integrates commonly used sequence-derived features and three new features containing adenosine to inosine (A-to-I) deamination, A-to-I density and the internal ribosome entry site. circLGB categorizes circRNAs by utilizing a LightGBM classifier with feature selection. Second, we introduce circMRT, an ensemble machine learning framework to systematically predict the regulatory information for circRNA, including their interactions with microRNA, the RNA binding protein, and transcriptional regulation. Feature sets including sequence-based features, graph features, genome context, and regulatory information features were modeled in circMRT. Experiments on public and our constructed datasets show that the proposed algorithms outperform the available state-of-the-art methods. circLGB is available at http://www.circlgb.com. Source codes are available at https://github.com/Peppags/circLGB-circMRT.
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Affiliation(s)
- Guishan Zhang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China
| | - Yiyun Deng
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China
| | - Qingyu Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China
| | - Bingxu Ye
- Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, College of Engineering, Shantou University, Shantou, China
| | - Zhiming Dai
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.,Guangdong Province Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University, Guangzhou, China
| | - Yaowen Chen
- Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, College of Engineering, Shantou University, Shantou, China
| | - Xianhua Dai
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China.,Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China
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38
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CirRNAPL: A web server for the identification of circRNA based on extreme learning machine. Comput Struct Biotechnol J 2020; 18:834-842. [PMID: 32308930 PMCID: PMC7153170 DOI: 10.1016/j.csbj.2020.03.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 03/29/2020] [Accepted: 03/29/2020] [Indexed: 12/27/2022] Open
Abstract
Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://server.malab.cn/CirRNAPL/.
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Key Words
- ACC, Accuracy
- CNN, Convolutional Neural Networks
- Circular RNA
- DAC, Dinucleotide-based auto-covariance
- DACC, Dinucleotide-based auto-cross-covariance
- DCC, Dinucleotide-based cross-covariance
- ELM, extreme learning machine
- Expression level
- Extreme learning machine
- GAC, Geary autocorrelation
- Identification
- MAC, Moran autocorrelation
- MCC, Matthews Correlation Coefficient
- MRMD, Maximum-Relevance-Maximum-Distance
- NMBAC, Normalized Moreau–Broto autocorrelation
- PC-PseDNC-General, General parallel correlation pseudo-dinucleotide composition
- PCGs, protein coding genes
- PSO, particle swarm optimization algorithm
- Particle swarm optimization algorithm
- PseDPC, Pseudo-distance structure status pair composition
- PseSSC, Pseudo-structure status composition
- RBF, radial basis function
- RF, random forest
- SC-PseDNC-General, General series correlation pseudo-dinucleotide composition
- SE, Sensitivity
- SP, Specifity
- SVM, support vector machine
- Triplet, Local structure-sequence triplet element
- circRNA, circular RNA
- lncRNAs, long non-coding RNAs
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