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Nie F, Feng P, Song X, Wu M, Tang Q, Chen W. RNAWRE: a resource of writers, readers and erasers of RNA modifications. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2020:5865458. [PMID: 32608478 PMCID: PMC7327530 DOI: 10.1093/database/baaa049] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 05/25/2020] [Accepted: 05/29/2020] [Indexed: 12/12/2022]
Abstract
RNA modifications are involved in various kinds of cellular biological processes. Accumulated evidences have demonstrated that the functions of RNA modifications are determined by the effectors that can catalyze, recognize and remove RNA modifications. They are called ‘writers’, ‘readers’ and ‘erasers’. The identification of RNA modification effectors will be helpful for understanding the regulatory mechanisms and biological functions of RNA modifications. In this work, we developed a database called RNAWRE that specially deposits RNA modification effectors. The current version of RNAWRE stored 2045 manually curated writers, readers and erasers for the six major kinds of RNA modifications, namely Cap, m1A, m6A, m5C, ψ and Poly A. The main modules of RNAWRE not only allow browsing and downloading the RNA modification effectors but also support the BLAST search of the potential RNA modification effectors in other species. We hope that RNAWRE will be helpful for the researches on RNA modifications. Database URL: http://rnawre.bio2db.com
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Affiliation(s)
- Fulei Nie
- School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, 21 Bohai Road, Caofeidian Xincheng, Tangshan 063009, China
| | - Pengmian Feng
- School of Basic Medical Sciences, 1166 Liutai Avenue, Wenjiang District, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xiaoming Song
- School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, 21 Bohai Road, Caofeidian Xincheng, Tangshan 063009, China
| | - Meng Wu
- School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, 21 Bohai Road, Caofeidian Xincheng, Tangshan 063009, China
| | - Qiang Tang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu 611137, China
| | - Wei Chen
- School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, 21 Bohai Road, Caofeidian Xincheng, Tangshan 063009, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu 611137, China
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52
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Zhang J, Chen Q, Liu B. DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1451-1463. [PMID: 31722485 DOI: 10.1109/tcbb.2019.2952338] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two kinds of crucial proteins, which are associated with various cellule activities and some important diseases. Accurate identification of DBPs and RBPs facilitate both theoretical research and real world application. Existing sequence-based DBP predictors can accurately identify DBPs but incorrectly predict many RBPs as DBPs, and vice versa, resulting in low prediction precision. Moreover, some proteins (DRBPs) interacting with both DNA and RNA play important roles in gene expression and cannot be identified by existing computational methods. In this study, a two-level predictor named DeepDRBP-2L was proposed by combining Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM). It is the first computational method that is able to identify DBPs, RBPs and DRBPs. Rigorous cross-validations and independent tests showed that DeepDRBP-2L is able to overcome the shortcoming of the existing methods and can go one further step to identify DRBPs. Application of DeepDRBP-2L to tomato genome further demonstrated its performance. The webserver of DeepDRBP-2L is freely available at http://bliulab.net/DeepDRBP-2L.
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Pan J, Huang Z, Xu Y. m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma. Front Cell Dev Biol 2021; 9:671821. [PMID: 34268304 PMCID: PMC8277384 DOI: 10.3389/fcell.2021.671821] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/01/2021] [Indexed: 12/24/2022] Open
Abstract
Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs–mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.
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Affiliation(s)
- Junfan Pan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Zhidong Huang
- Quanzhou First Hospital, Fujian Medical University, Quanzhou, China
| | - Yiquan Xu
- Department of Thoracic Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
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CWLy-RF: A novel approach for identifying cell wall lyases based on random forest classifier. Genomics 2021; 113:2919-2924. [PMID: 34186189 DOI: 10.1016/j.ygeno.2021.06.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/20/2021] [Accepted: 06/25/2021] [Indexed: 02/05/2023]
Abstract
Drug resistance of pathogenic bacteria has become increasingly serious due to the abuse of antibiotics in recent years. Researchers have found that cell wall lyases are effective antibacterial agents that can specifically recognize target bacteria and degrade bacterial peptidoglycan. Traditional wet experiments are usually expensive, time-consuming and laborious for the identification of lyases. Therefore, there is an urgent need to develop prediction tools based on computer methods to identify lyases quickly and accurately. In this paper, a new predictor, CWLy-RF, is proposed based on the random forest (RF) algorithm to identify cell wall lyases. In this method, we combined three features, namely, 400D, 188D and the composition of k-spaced amino acid group pairs, using mixed-feature representation methods. Afterward, we improved the feature representation ability with the selected top 100 features by using the information gain method and trained a predictive model using RF. The constructed prediction model is evaluated by using 10-fold cross-validation. The accuracy obtained was 96.09%, the AUC was 0.993, the MCC was 0.922, the sensitivity was 94.92%, and the specificity was 97.32%. We have proved that the proposed predictor CWLy-RF is superior to other latest models, and it will hopefully become an effective and useful tool for identifying lyases.
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Zhang SY, Zhang SW, Zhang T, Fan XN, Meng J. Recent advances in functional annotation and prediction of the epitranscriptome. Comput Struct Biotechnol J 2021; 19:3015-3026. [PMID: 34136099 PMCID: PMC8175281 DOI: 10.1016/j.csbj.2021.05.030] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/16/2021] [Accepted: 05/18/2021] [Indexed: 12/17/2022] Open
Abstract
RNA modifications, in particular N6-methyladenosine (m6A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.
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Affiliation(s)
- Song-Yao Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Teng Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xiao-Nan Fan
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
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Charoenkwan P, Chiangjong W, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W. StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides. Brief Bioinform 2021; 22:6271998. [PMID: 33963832 DOI: 10.1093/bib/bbab172] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/30/2021] [Accepted: 04/10/2021] [Indexed: 12/13/2022] Open
Abstract
The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Wararat Chiangjong
- Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | | | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song XM, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3348-3363. [PMID: 34198389 DOI: 10.3934/mbe.2021167] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.
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Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rida Sarwar Khan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Ming Song
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Sciences, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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Chen K, Song B, Tang Y, Wei Z, Xu Q, Su J, de Magalhães JP, Rigden DJ, Meng J. RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis. Nucleic Acids Res 2021; 49:D1396-D1404. [PMID: 33010174 PMCID: PMC7778951 DOI: 10.1093/nar/gkaa790] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 12/11/2022] Open
Abstract
Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.
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Affiliation(s)
- Kunqi Chen
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
- Institute of Ageing & Chronic Disease, University of Liverpool, L7 8TX Liverpool, UK
| | - Bowen Song
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, UK
- Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Yujiao Tang
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, UK
| | - Zhen Wei
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, UK
| | - Qingru Xu
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Jionglong Su
- Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | | | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, UK
| | - Jia Meng
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, UK
- AI University Research Centre, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
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Ao C, Yu L, Zou Q. Prediction of bio-sequence modifications and the associations with diseases. Brief Funct Genomics 2020; 20:1-18. [PMID: 33313647 DOI: 10.1093/bfgp/elaa023] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 12/22/2022] Open
Abstract
Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.
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Jiang J, Song B, Tang Y, Chen K, Wei Z, Meng J. m5UPred: A Web Server for the Prediction of RNA 5-Methyluridine Sites from Sequences. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 22:742-747. [PMID: 33230471 PMCID: PMC7595847 DOI: 10.1016/j.omtn.2020.09.031] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 09/25/2020] [Indexed: 11/16/2022]
Abstract
As one of the widely occurring RNA modifications, 5-methyluridine (m5U) has recently been shown to play critical roles in various biological functions and disease pathogenesis, such as under stress response and during breast cancer development. Precise identification of m5U sites on RNA is vital for the understanding of the regulatory mechanisms of RNA life. We present here m5UPred, the first web server for in silico identification of m5U sites from the primary sequences of RNA. Built upon the support vector machine (SVM) algorithm and the biochemical encoding scheme, m5UPred achieved reasonable prediction performance with the area under the receiver operating characteristic curve (AUC) greater than 0.954 by 5-fold cross-validation and independent testing datasets. To critically test and validate the performance of our newly proposed predictor, the experimentally validated m5U sites were further separated by high-throughput sequencing techniques (miCLIP-Seq and FICC-Seq) and cell types (HEK293 and HAP1). When tested on cross-technique and cross-cell-type validation using independent datasets, m5UPred achieved an average AUC of 0.922 and 0.926 under mature mRNA mode, respectively, showing reasonable accuracy and reliability. The m5UPred web server is freely accessible now and it should make a useful tool for the researchers who are interested in m5U RNA modification.
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Affiliation(s)
- Jie Jiang
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX, Liverpool, UK
| | - Bowen Song
- Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX, Liverpool, UK
| | - Yujiao Tang
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX, Liverpool, UK
| | - Kunqi Chen
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China
- Institute of Ageing & Chronic Disease, University of Liverpool, L7 8TX, Liverpool, UK
| | - Zhen Wei
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX, Liverpool, UK
| | - Jia Meng
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China
- AI University Research Centre, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX, Liverpool, UK
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Chen X, Xiong Y, Liu Y, Chen Y, Bi S, Zhu X. m5CPred-SVM: a novel method for predicting m5C sites of RNA. BMC Bioinformatics 2020; 21:489. [PMID: 33126851 PMCID: PMC7602301 DOI: 10.1186/s12859-020-03828-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/21/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. RESULTS In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites. CONCLUSION In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at https://zhulab.ahu.edu.cn/m5CPred-SVM .
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Affiliation(s)
- Xiao Chen
- School of Sciences, Anhui Agricultural University, Hefei, 230036 Anhui China
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Yinbo Liu
- School of Sciences, Anhui Agricultural University, Hefei, 230036 Anhui China
| | - Yuqing Chen
- School of Sciences, Anhui Agricultural University, Hefei, 230036 Anhui China
| | - Shoudong Bi
- School of Sciences, Anhui Agricultural University, Hefei, 230036 Anhui China
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, 230036 Anhui China
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Li Q, Xu L, Li Q, Zhang L. Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8852258. [PMID: 33133227 PMCID: PMC7591959 DOI: 10.1155/2020/8852258] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/16/2020] [Accepted: 09/30/2020] [Indexed: 12/21/2022]
Abstract
Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a higher level of complexity than those of coding genes. In order to solve this problem, many computer studies have been carried out in this field, but there are still some deficiencies in these prediction models. In this paper, we use various feature extraction strategies, dimension reduction technology, and a comprehensive application of machine model and recurrent neural network model to achieve an accurate prediction of enhancer identification and classification with the accuracy of was 76.7% and 84.9%, respectively. The model proposed in this paper is superior to the previous methods in performance index or feature dimension, which provides inspiration for the prediction of enhancers by computer technology in the future.
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Affiliation(s)
- Qingwen Li
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Qingyuan Li
- Forestry and Fruit Tree Research Institute, Wuhan Academy of Agricultural Sciences, Wuhan, China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
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Abstract
Background:
Thermophilic proteins can maintain good activity under high temperature,
therefore, it is important to study thermophilic proteins for the thermal stability of proteins.
Objective:
In order to solve the problem of low precision and low efficiency in predicting
thermophilic proteins, a prediction method based on feature fusion and machine learning was
proposed in this paper.
Methods:
For the selected thermophilic data sets, firstly, the thermophilic protein sequence was
characterized based on feature fusion by the combination of g-gap dipeptide, entropy density and
autocorrelation coefficient. Then, Kernel Principal Component Analysis (KPCA) was used to reduce
the dimension of the expressed protein sequence features in order to reduce the training time and
improve efficiency. Finally, the classification model was designed by using the classification
algorithm.
Results:
A variety of classification algorithms was used to train and test on the selected thermophilic
dataset. By comparison, the accuracy of the Support Vector Machine (SVM) under the jackknife
method was over 92%. The combination of other evaluation indicators also proved that the SVM
performance was the best.
Conclusion:
Because of choosing an effectively feature representation method and a robust
classifier, the proposed method is suitable for predicting thermophilic proteins and is superior to
most reported methods.
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Affiliation(s)
- Xian-Fang Wang
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Yi-Feng Liu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Hong-Fei Li
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Fan Lu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
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64
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Wang Z, Liu D, Xu B, Tian R, Zuo Y. Modular arrangements of sequence motifs determine the functional diversity of KDM proteins. Brief Bioinform 2020; 22:5912575. [PMID: 32987405 DOI: 10.1093/bib/bbaa215] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Histone lysine demethylases (KDMs) play a vital role in regulating chromatin dynamics and transcription. KDM proteins are given modular activities by its sequence motifs with obvious roles division, which endow the complex and diverse functions. In our review, according to functional features, we classify sequence motifs into four classes: catalytic motifs, targeting motifs, regulatory motifs and potential motifs. JmjC, as the main catalytic motif, combines to Fe2+ and α-ketoglutarate by residues H-D/E-H and S-N-N/Y-K-N/Y-T/S. Targeting motifs make catalytic motifs recognize specific methylated lysines, such as PHD that helps KDM5 to demethylate H3K4me3. Regulatory motifs consist of a functional network. For example, NLS, Ser-rich, TPR and JmjN motifs regulate the nuclear localization. And interactions through the CW-type-C4H2C2-SWIRM are necessary to the demethylase activity of KDM1B. Additionally, many conservative domains that have potential functions but no deep exploration are reviewed for the first time. These conservative domains are usually amino acid-rich regions, which have great research value. The arrangements of four types of sequence motifs generate that KDM proteins diversify toward modular activities and biological functions. Finally, we draw a blueprint of functional mechanisms to discuss the modular activity of KDMs.
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Affiliation(s)
- Zerong Wang
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Dongyang Liu
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University. He is now studying for a master's degree at the institute of botany of the Chinese Academy of Sciences. His research interests include bioinformatics and computational genomics
| | - Baofang Xu
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Ruixia Tian
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Yongchun Zuo
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University. His research interests include bioinformatics and integration analysis of multiomics in cell reprogramming
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65
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Han Y, Cheng L, Sun W. Analysis of Protein-Protein Interaction Networks through Computational Approaches. Protein Pept Lett 2020; 27:265-278. [PMID: 31692419 DOI: 10.2174/0929866526666191105142034] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 05/08/2019] [Accepted: 09/26/2019] [Indexed: 01/02/2023]
Abstract
The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.
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Affiliation(s)
- Ying Han
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weiju Sun
- Cardiovascular Department, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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66
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Li R, Li H, Yang S, Feng X. The Influences of Palindromes in mRNA on Protein Folding Rates. Protein Pept Lett 2020; 27:303-312. [PMID: 31612810 DOI: 10.2174/0929866526666191014144015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 06/14/2019] [Accepted: 06/29/2019] [Indexed: 01/21/2023]
Abstract
BACKGROUND It is currently believed that protein folding rates are influenced by protein structure, environment and temperature, amino acid sequence and so on. We have been working for long to determine whether and in what ways mRNA affects the protein folding rate. A large number of palindromes aroused our attention in our previous research. Whether these palindromes do have important influences on protein folding rates and what's the mechanism? Very few related studies are focused on these problems. OBJECTIVE In this article, our motivation is to find out if palindromes have important influences on protein folding rates and what's the mechanism. METHODS In this article, the parameters of the palindromes were defined and calculated, the linear regression analysis between the values of each parameter and the experimental protein folding rates were done. Furthermore, to compare the results of different kinds of proteins, proteins were classified into the two-state proteins and the multi-state proteins. For the two kinds of proteins, the above linear regression analysis were performed respectively. RESULTS Protein folding rates were negatively correlated to the palindrome frequencies for all proteins. An extremely significant negative linear correlation appeared in the relationship between palindrome densities and protein folding rates. And the repeatedly used bases by different palindromes simultaneously have an important effect on the relationship between palindrome density and protein folding rate. CONCLUSION The palindromes have important influences on protein folding rates, and the repeatedly used bases in different palindromes simultaneously play a key role in influencing the protein folding rates.
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Affiliation(s)
- Ruifang Li
- College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
| | - Hong Li
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Sarula Yang
- College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
| | - Xue Feng
- College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
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67
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Xu L, Liang G, Chen B, Tan X, Xiang H, Liao C. A Computational Method for the Identification of Endolysins and Autolysins. Protein Pept Lett 2020; 27:329-336. [PMID: 31577192 DOI: 10.2174/0929866526666191002104735] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 06/27/2019] [Accepted: 09/03/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. OBJECTIVE In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. METHODS We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. RESULTS Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. CONCLUSION The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Baowen Chen
- School of Software, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Xu Tan
- School of Software, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Huaikun Xiang
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China
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68
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Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan MM, Shoombuatong W. iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method. J Proteome Res 2020; 19:4125-4136. [PMID: 32897718 DOI: 10.1021/acs.jproteome.0c00590] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The inhibition of dipeptidyl peptidase IV (DPP-IV, E.C.3.4.14.5) is well recognized as a new avenue for the treatment of Type 2 diabetes (T2D). Until now, peptide-like DDP-IV inhibitors have been shown to normalize the blood glucose concentration in T2D subjects. To the best of our knowledge, there is yet no computational model for predicting and analyzing DPP-IV inhibitory peptides using sequence information. In this study, we present for the first time a simple and easily interpretable sequence-based predictor using the scoring card method (SCM) for modeling the bioactivity of DPP-IV inhibitory peptides (iDPPIV-SCM). Particularly, the iDPPIV-SCM was developed by employing the SCM method together with the propensity scores of amino acids. Rigorous independent test results demonstrated that the proposed iDPPIV-SCM was found to be superior to those of well-known machine learning (ML) classifiers (e.g., k-nearest neighbor, logistic regression, and decision tree) with demonstrated improvements of 2-11, 4-22, and 7-10% for accuracy, MCC, and AUC, respectively, while also achieving comparable results to that of the support vector machine. Furthermore, the analysis of estimated propensity scores of amino acids as derived from the iDPPIV-SCM was performed so as to provide a more in-depth understanding on the molecular basis for enhancing the DPP-IV inhibitory potency. Taken together, these results revealed that iDPPIV-SCM was superior to those of other well-known ML classifiers owing to its simplicity, interpretability, and validity. For the convenience of biologists, the predictive model is deployed as a publicly accessible web server at http://camt.pythonanywhere.com/iDPPIV-SCM. It is anticipated that iDPPIV-SCM can serve as an important tool for the rapid screening of promising DPP-IV inhibitory peptides prior to their synthesis.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Sakawrat Kanthawong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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69
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Bi Y, Xiang D, Ge Z, Li F, Jia C, Song J. An Interpretable Prediction Model for Identifying N 7-Methylguanosine Sites Based on XGBoost and SHAP. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 22:362-372. [PMID: 33230441 PMCID: PMC7533297 DOI: 10.1016/j.omtn.2020.08.022] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 08/20/2020] [Indexed: 12/19/2022]
Abstract
Recent studies have increasingly shown that the chemical modification of mRNA plays an important role in the regulation of gene expression. N7-methylguanosine (m7G) is a type of positively-charged mRNA modification that plays an essential role for efficient gene expression and cell viability. However, the research on m7G has received little attention to date. Bioinformatics tools can be applied as auxiliary methods to identify m7G sites in transcriptomes. In this study, we develop a novel interpretable machine learning-based approach termed XG-m7G for the differentiation of m7G sites using the XGBoost algorithm and six different types of sequence-encoding schemes. Both 10-fold and jackknife cross-validation tests indicate that XG-m7G outperforms iRNA-m7G. Moreover, using the powerful SHAP algorithm, this new framework also provides desirable interpretations of the model performance and highlights the most important features for identifying m7G sites. XG-m7G is anticipated to serve as a useful tool and guide for researchers in their future studies of mRNA modification sites.
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Affiliation(s)
- Yue Bi
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Dongxu Xiang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Zongyuan Ge
- Monash e-Research Centre and Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
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70
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Li Q, Zhou W, Wang D, Wang S, Li Q. Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model. Front Bioeng Biotechnol 2020; 8:892. [PMID: 32903381 PMCID: PMC7434836 DOI: 10.3389/fbioe.2020.00892] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/10/2020] [Indexed: 01/09/2023] Open
Abstract
Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.
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Affiliation(s)
- Qingwen Li
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Wenyang Zhou
- Center for Bioinformatics, School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Donghua Wang
- Department of General Surgery, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Sui Wang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education, Northeast Agricultural University, Harbin, China
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
| | - Qingyuan Li
- Forestry and Fruit Tree Research Institute, Wuhan Academy of Agricultural Sciences, Wuhan, China
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71
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Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features. Genomics 2020; 112:4342-4347. [PMID: 32721444 DOI: 10.1016/j.ygeno.2020.07.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 07/18/2020] [Accepted: 07/22/2020] [Indexed: 12/14/2022]
Abstract
N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.
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72
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Guan ZX, Li SH, Zhang ZM, Zhang D, Yang H, Ding H. A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods. Curr Genomics 2020; 21:11-25. [PMID: 32655294 PMCID: PMC7324890 DOI: 10.2174/1389202921666200214125102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 01/24/2020] [Accepted: 01/30/2020] [Indexed: 11/22/2022] Open
Abstract
MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as time-consuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.
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Affiliation(s)
- Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
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73
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Geng G, Zhang Z, Cheng L. Identification of a Multi-Long Noncoding RNA Signature for the Diagnosis of Type 1 Diabetes Mellitus. Front Bioeng Biotechnol 2020; 8:553. [PMID: 32719778 PMCID: PMC7350420 DOI: 10.3389/fbioe.2020.00553] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 05/07/2020] [Indexed: 02/01/2023] Open
Abstract
Due to the increasing prevalence of type 1 diabetes mellitus (T1DM) and its complications, there is an urgent need to identify novel methods for predicting the occurrence and understanding the pathogenetic mechanisms of the disease. Accumulated data have demonstrated the potential of long noncoding RNAs (lncRNAs), as biomarkers in establishing diagnosis and predicting prognosis of numerous diseases. Yet, little is known about the expression patterns and regulatory roles of lncRNAs in the pathogenesis of T1DM and whether they can be used as diagnostic biomarkers for the disease. To further explore these questions, in the present study, we conducted a comparative analysis of the expression patterns of lncRNAs between 20 T1DM patients and 42 health controls by retrospectively analyzing a published microarray data set. Our results indicate that, compared with healthy controls, diabetic patients had altered levels of lncRNAs. Then, we used three time cross-validation strategy and support vector machine to propose a specific 26-lncRNA signature (termed 26LncSigT1DM). This 26LncSigT1DM signature can be used to effectively distinguish between healthy and diabetic individuals (area under the curve = 0.825) of a validation cohort. After the 26LncSigT1DM was prospectively validated, we used Pearson correlation to identify 915 mRNAs, whose expression levels were positively correlated with those of the 26 lncRNAs. According to their Gene Ontology annotations, these mRNAs participate in processes including cellular response to stimulus, cell communication, multicellular organismal process, and cell motility. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that the genes encoding the 915 mRNAs may be associated with the NOD-like receptor signaling pathway, transforming growth factor β signaling pathway, and mineral absorption, suggesting that the deregulation of these lncRNAs may mediate inflammatory abnormalities and immune dysfunctions, which jointly promote the pathogenesis of T1DM. Thus, our study identifies a novel diagnostic tool and may shed more light on the molecular mechanisms underlying the pathogenesis of T1DM.
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Affiliation(s)
- Guannan Geng
- Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zicheng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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74
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Zhao T, Hu Y, Zang T, Wang Y. Identifying Protein Biomarkers in Blood for Alzheimer's Disease. Front Cell Dev Biol 2020; 8:472. [PMID: 32626709 PMCID: PMC7314983 DOI: 10.3389/fcell.2020.00472] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 05/20/2020] [Indexed: 12/26/2022] Open
Abstract
Background: At present, the main diagnostic methods for Alzheimer's disease (AD) are positron emission tomography (PET) scanning of the brain and analysis of cerebrospinal fluid (CSF) sample, but these methods are expensive and harmful to patients. Recently, more researchers focus on diagnosing AD by detecting biomarkers in blood, which is a cheaper and harmless way. Therefore, identifying AD-related proteins in blood can help treatment and diagnosis. Methods: We proposed a hypothesis that similar diseases share similar proteins. Diseases with similar symptoms are caused by abnormalities of similar proteins. Assuming that the similarities between AD and other diseases obey the normal distribution, we developed an iterative method based on disease similarity (IBDS). We combined Elastic Network (EN) with Minimum angle regression (MAR) to find the optimal solution. Finally, we used case studies and Summary data Mendelian Random (SMR) to verify our method. Results: We selected 39 diseases which are highly related to AD. They correspond 1,481 kinds of proteins. One hundred and eighty-four proteins are reported to be related to AD in Uniprot and the number would be 284 with our method. The AUC of our method by cross-validation is 0.9251 which is much higher than previous methods. Conclusion: In this paper, we presented a novel method for prioritizing AD-related proteins. Seven proteins have tissue specificity in blood among these 284 proteins, which could be used to diagnose AD in future. Case studies and SMR have been used to prove the relationship between these 7 proteins and AD. Availability and Implementation: https://github.com/zty2009/Identifying-Protein-Biomarkers-in-Blood-for-Alzheimer-s-Disease.
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Affiliation(s)
- Tianyi Zhao
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yang Hu
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zang
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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75
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Zhang D, Guan ZX, Zhang ZM, Li SH, Dao FY, Tang H, Lin H. Recent Development of Computational Predicting Bioluminescent Proteins. Curr Pharm Des 2020; 25:4264-4273. [PMID: 31696804 DOI: 10.2174/1381612825666191107100758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/04/2019] [Indexed: 12/22/2022]
Abstract
Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.
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Affiliation(s)
- Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Dou L, Li X, Ding H, Xu L, Xiang H. Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 21:332-342. [PMID: 32645685 PMCID: PMC7340967 DOI: 10.1016/j.omtn.2020.06.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/03/2020] [Accepted: 06/04/2020] [Indexed: 12/14/2022]
Abstract
5-Methylcytosine (m5C) is a well-known post-transcriptional modification that plays significant roles in biological processes, such as RNA metabolism, tRNA recognition, and stress responses. Traditional high-throughput techniques on identification of m5C sites are usually time consuming and expensive. In addition, the number of RNA sequences shows explosive growth in the post-genomic era. Thus, machine-learning-based methods are urgently requested to quickly predict RNA m5C modifications with high accuracy. Here, we propose a noval support-vector-machine (SVM)-based tool, called iRNA-m5C_SVM, by combining multiple sequence features to identify m5C sites in Arabidopsis thaliana. Eight kinds of popular feature-extraction methods were first investigated systematically. Then, four well-performing features were incorporated to construct a comprehensive model, including position-specific propensity (PSP) (PSNP, PSDP, and PSTP, associated with frequencies of nucleotides, dinucleotides, and trinucleotides, respectively), nucleotide composition (nucleic acid, di-nucleotide, and tri-nucleotide compositions; NAC, DNC, and TNC, respectively), electron-ion interaction pseudopotentials of trinucleotide (PseEIIPs), and general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-general). Evaluated accuracies over 10-fold cross-validation and independent tests achieved 73.06% and 80.15%, respectively, which showed the best predictive performances in A. thaliana among existing models. It is believed that the proposed model in this work can be a promising alternative for further research on m5C modification sites in plant.
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Affiliation(s)
- Lijun Dou
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoling Li
- Department of Oncology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.
| | - Huaikun Xiang
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China.
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77
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Wang C, Zhang Y, Han S. Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification. BIOMED RESEARCH INTERNATIONAL 2020; 2020:2468789. [PMID: 32566672 PMCID: PMC7275950 DOI: 10.1155/2020/2468789] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 03/20/2020] [Accepted: 03/25/2020] [Indexed: 12/19/2022]
Abstract
Fungi play essential roles in many ecological processes, and taxonomic classification is fundamental for microbial community characterization and vital for the study and preservation of fungal biodiversity. To cope with massive fungal barcode data, tools that can implement extensive volumes of barcode sequences, especially the internal transcribed spacer (ITS) region, are necessary. However, high variation in the ITS region and computational requirements for processing high-dimensional features remain challenging for existing predictors. In this study, we developed Its2vec, a bioinformatics tool for the classification of fungal ITS barcodes to the species level. An ITS database covering more than 25,000 species in a broad range of fungal taxa was assembled. For dimensionality reduction, a word embedding algorithm was used to represent an ITS sequence as a dense low-dimensional vector. A random forest-based classifier was built for species identification. Benchmarking results showed that our model achieved an accuracy comparable to that of several state-of-the-art predictors, and more importantly, it could implement large datasets and greatly reduce dimensionality. We expect the Its2vec model to be helpful for fungal species identification and, thus, for revealing microbial community structures and in deepening our understanding of their functional mechanisms.
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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 Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin 150088, China
| | - Shuguang Han
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 60054, China
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78
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Jin X, Liao Q, Liu B. PL-search: a profile-link-based search method for protein remote homology detection. Brief Bioinform 2020; 22:5840006. [PMID: 32427287 DOI: 10.1093/bib/bbaa051] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/11/2020] [Accepted: 03/12/2020] [Indexed: 12/26/2022] Open
Abstract
Protein remote homology detection is a fundamental and important task for protein structure and function analysis. Several search methods have been proposed to improve the detection performance of the remote homologues and the accuracy of ranking lists. The position-specific scoring matrix (PSSM) profile and hidden Markov model (HMM) profile can contribute to improving the performance of the state-of-the-art search methods. In this paper, we improved the profile-link (PL) information for constructing PSSM or HMM profiles, and proposed a PL-based search method (PL-search). In PL-search, more robust PLs are constructed through the double-link and iterative extending strategies, and an accurate similarity score of sequence pairs is calculated from the two-level Jaccard distance for remote homologues. We tested our method on two widely used benchmark datasets. Our results show that whether HHblits, JackHMMER or position-specific iterated-BLAST is used, PL-search obviously improves the search performance in terms of ranking quality as well as the number of detected remote homologues. For ease of use of PL-search, both its stand-alone tool and the web server are constructed, which can be accessed at http://bliulab.net/PL-search/.
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79
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Li F, Chen J, Ge Z, Wen Y, Yue Y, Hayashida M, Baggag A, Bensmail H, Song J. Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework. Brief Bioinform 2020; 22:2126-2140. [PMID: 32363397 DOI: 10.1093/bib/bbaa049] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 02/25/2020] [Accepted: 03/11/2020] [Indexed: 12/12/2022] Open
Abstract
Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing 'Black-box' approaches that are unable to reveal causal relationships from large amounts of initially encoded features.
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Affiliation(s)
- Fuyi Li
- Northwest A&F University, China.,Department of Biochemistry and Molecular Biology and the Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Australia
| | - Jinxiang Chen
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University from the College of Information Engineering, Northwest A&F University, China
| | - Zongyuan Ge
- Monash University and also serves as a Deep Learning Specialist at NVIDIA AI Technology Centre. Before joining Monash, he was a research scientist at IBM Research Australia doing research in medical AI during 2016-2018. His research interests are AI, computer vision, medical image, robotics and deep learning
| | - Ya Wen
- computer technology from Ningxia University, China
| | - Yanwei Yue
- medical science from Southern Medical University, China
| | - Morihiro Hayashida
- informatics from Kyoto University, Japan, in 2005. He is an Assistant Professor in the Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, Japan
| | - Abdelkader Baggag
- computer science from the University of Minnesota. He is a Senior Scientist at the Qatar Computing Research Institute (QCRI) and has a joint appointment as an Associate Professor at Hamad Bin Khalifa University (HBKU) in the Division of Information and Computing Technology. His research interests include data mining, linear algebra and machine learning
| | - Halima Bensmail
- University of Pierre & Marie Currie (Paris 6) in France. She is currently a Principal Scientist at QCRI-HBKU and a joint Associate Professor at the College of Computer and Science Engineering, HBKU
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Australia. He is also affiliated with the Monash Centre for Data Science, Faculty of Information Technology, Monash University. His research interests include bioinformatics, computational biology, machine learning, data mining, and pattern recognition
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80
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Cai Y, Wang J, Deng L. SDN2GO: An Integrated Deep Learning Model for Protein Function Prediction. Front Bioeng Biotechnol 2020; 8:391. [PMID: 32411695 PMCID: PMC7201018 DOI: 10.3389/fbioe.2020.00391] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 04/07/2020] [Indexed: 02/01/2023] Open
Abstract
The assignment of function to proteins at a large scale is essential for understanding the molecular mechanism of life. However, only a very small percentage of the more than 179 million proteins in UniProtKB have Gene Ontology (GO) annotations supported by experimental evidence. In this paper, we proposed an integrated deep-learning-based classification model, named SDN2GO, to predict protein functions. SDN2GO applies convolutional neural networks to learn and extract features from sequences, protein domains, and known PPI networks, and then utilizes a weight classifier to integrate these features and achieve accurate predictions of GO terms. We constructed the training set and the independent test set according to the time-delayed principle of the Critical Assessment of Function Annotation (CAFA) and compared it with two highly competitive methods and the classic BLAST method on the independent test set. The results show that our method outperforms others on each sub-ontology of GO. We also investigated the performance of using protein domain information. We learned from the Natural Language Processing (NLP) to process domain information and pre-trained a deep learning sub-model to extract the comprehensive features of domains. The experimental results demonstrate that the domain features we obtained are much improved the performance of our model. Our deep learning models together with the data pre-processing scripts are publicly available as an open source software at https://github.com/Charrick/SDN2GO.
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Affiliation(s)
- Yideng Cai
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jiacheng Wang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
- School of Software, Xinjiang University, Urumqi, China
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81
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Zeng R, Liao M. Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications. Front Bioeng Biotechnol 2020; 8:274. [PMID: 32373597 PMCID: PMC7186498 DOI: 10.3389/fbioe.2020.00274] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/16/2020] [Indexed: 12/21/2022] Open
Abstract
DNA N4-methylcytosine modification (4mC) plays an essential role in a variety of biological processes. Therefore, accurate identification the 4mC distribution in genome-scale is important for systematically understanding its biological functions. In this study, we present Deep4mcPred, a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. In this predictor, we for the first time integrate residual network and recurrent neural network to build a multi-layer deep learning predictive system. As compared to existing predictors using traditional machine learning, our proposed method has two advantages. First, our deep learning framework does not need to specify the features when training the predictive model. It can automatically learn the high-level features and capture the characteristic specificity of 4mC sites, benefiting to distinguish true 4mC sites from non-4mC sites. On the other hand, our deep learning method outperforms the traditional machine learning predictors in performance by benchmarking comparison, demonstrating that the proposed Deep4mcPred is more effective in the DNA 4mC site prediction. Moreover, via experimental comparison, we found that attention mechanism introduced into the deep learning framework is useful to capture the critical features. Additionally, we develop a webserver implementing the proposed method for the academic use of research community, which is now available at http://server.malab.cn/Deep4mcPred.
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Affiliation(s)
- Rao Zeng
- Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China
| | - Minghong Liao
- Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China
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82
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i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes. Comput Struct Biotechnol J 2020; 18:906-912. [PMID: 32322372 PMCID: PMC7168350 DOI: 10.1016/j.csbj.2020.04.001] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/31/2020] [Accepted: 04/03/2020] [Indexed: 12/12/2022] Open
Abstract
N4-methylcytosine (4mC) is one of the most important DNA modifications and involved in regulating cell differentiations and gene expressions. The accurate identification of 4mC sites is necessary to understand various biological functions. In this work, we developed a new computational predictor called i4mC-Mouse to identify 4mC sites in the mouse genome. Herein, six encoding schemes of k-space nucleotide composition (KSNC), k-mer nucleotide composition (Kmer), mono nucleotide binary encoding (MBE), dinucleotide binary encoding, electron–ion interaction pseudo potentials (EIIP) and dinucleotide physicochemical composition were explored that cover different characteristics of DNA sequence information. Subsequently, we built six RF-based encoding models and then linearly combined their probability scores to construct the final predictor. Among the six RF-based models, the Kmer, KSNC, MBE, and EIIP encodings are sufficient, which contributed to 10%, 45%, 25%, and 20% of the prediction performance, respectively. On the independent test the i4mC-Mouse predicted the 4mC sites with accuracy and MCC of 0.816 and 0.633, respectively, which were approximately 2.5% and 5% higher than those of the existing method (4mCpred-EL). For experimental biologists, a freely available web application was implemented at http://kurata14.bio.kyutech.ac.jp/i4mC-Mouse/.
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83
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Rashid MM, Shatabda S, Hasan MM, Kurata H. Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites. Curr Genomics 2020; 21:194-203. [PMID: 33071613 PMCID: PMC7521030 DOI: 10.2174/1389202921666200427210833] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 01/10/2023] Open
Abstract
A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often labor-intensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.
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Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828;, E-mail: and Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828;, E-mail: and Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
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84
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Zhang M, Cheng L, Zhang Y. Characterization of Dysregulated lncRNA-Associated ceRNA Network Reveals Novel lncRNAs With ceRNA Activity as Epigenetic Diagnostic Biomarkers for Osteoporosis Risk. Front Cell Dev Biol 2020; 8:184. [PMID: 32296700 PMCID: PMC7136400 DOI: 10.3389/fcell.2020.00184] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 03/04/2020] [Indexed: 01/28/2023] Open
Abstract
The altered expression of long non-coding RNAs (lncRNAs) has been implicated in the development and human diseases. However, functional roles and regulatory mechanisms of lncRNA as competing endogenous RNAs (ceRNAs) in osteoporosis and their potential clinical implication for osteoporosis risk are largely unexplored. In this study, we performed integrated analysis for paired expression profiles and regulatory relationships of dysregulated lncRNAs, mRNAs, and miRNAs based on “ceRNA hypothesis,” and constructed an osteoporosis-related dysregulated miRNA-mediated lncRNA–mRNA ceRNA network (DysCeNet) composed of 105 nodes (including eight miRNAs, 24 mRNAs, and 73 lncRNAs) and 515 edges. Functional analysis suggested that the DysCeNet was involved in known osteoporosis or bone metabolism-related biological processes and pathways. Then, we performed random forest-based feature selection for 73 lncRNAs with ceRNA activity and identified 25 of 73 lncRNAs as potential diagnostic biomarkers. A random forest-based classifier composed of 25 lncRNA biomarkers (RF-25lncRNA) was developed for predicting osteoporosis risk. Performance evaluation with the leave-one-out cross-validation (LOOCV) procedure showed that the RF-25lncRNA achieved a good performance in distinguishing high- and low-bone mineral density (BMD) subjects in different osteoporosis datasets. Our study for the first time revealed a global view of lncRNA-associated ceRNA regulation in osteoporosis and provided novel lncRNAs with ceRNA activity as candidate epigenetic diagnostic biomarkers for early detection of osteoporosis risk.
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Affiliation(s)
- Meijie Zhang
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Luyang Cheng
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
| | - Yina Zhang
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China
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85
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Kou N, Zhou W, He Y, Ying X, Chai S, Fei T, Fu W, Huang J, Liu H. A Mendelian Randomization Analysis to Expose the Causal Effect of IL-18 on Osteoporosis Based on Genome-Wide Association Study Data. Front Bioeng Biotechnol 2020; 8:201. [PMID: 32266232 PMCID: PMC7099043 DOI: 10.3389/fbioe.2020.00201] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 02/28/2020] [Indexed: 01/16/2023] Open
Abstract
Accumulating evidence showed that Interleukin (IL) level is associated with Osteoporosis. Whereas, most of these associations are based on observational studies. Thus, their causality was still unclear. Mendelian randomization (MR) is a widely used statistical framework that uses genetic instrumental variables (IVs) to explore the causality of intermediate phenotype with disease. To classify their causality, we conducted a MR analysis to investigate the effect of IL-18 level on the risk of Osteoporosis. First, based on summarized genome-wide association study (GWAS) data, 8 independent IL-18 SNPs reaching genome-wide significance were deemed as IVs. Next, Simple median method was used to calculate the pooled odds ratio (OR) of these 8 SNPs for the assessment of IL-8 on the risk of Osteoporosis. Then, MR-Egger regression was utilized to detect potential bias due to the horizontal pleiotropy of these IVs. As a result of simple median method, we get the SE (−0.001; 95% CI−0.002 to 0; P = 0.042), which means low IL-18 level could increases the risk of the development of Osteoporosis. The low intercept (0; 95% CI −0.001 to 0; P = 0.59) shows there is no bias due to the horizontal pleiotropy of the IVs.
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Affiliation(s)
- Ni Kou
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yuzhu He
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Xiaoxia Ying
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Songling Chai
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Tao Fei
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Wenqi Fu
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Jiaqian Huang
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Huiying Liu
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
- *Correspondence: Huiying Liu
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86
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Dou L, Li X, Ding H, Xu L, Xiang H. Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem? MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 19:293-303. [PMID: 31865116 PMCID: PMC6931122 DOI: 10.1016/j.omtn.2019.11.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 10/29/2019] [Accepted: 11/11/2019] [Indexed: 01/01/2023]
Abstract
Pseudouridine (Ψ) is the most abundant RNA modification and has been found in many kinds of RNAs, including snRNA, rRNA, tRNA, mRNA, and snoRNA. Thus, Ψ sites play a significant role in basic research and drug development. Although some experimental techniques have been developed to identify Ψ sites, they are expensive and time consuming, especially in the post-genomic era with the explosive growth of known RNA sequences. Thus, highly accurate computational methods are urgently required to quickly detect the Ψ sites on uncharacterized RNA sequences. Several predictors have been proposed using multifarious features, but their evaluated performances are still unsatisfactory. In this study, we first identified Ψ sites for H. sapiens, S. cerevisiae, and M. musculus using the sequence features from the bi-profile Bayes (BPB) method based on the random forest (RF) and support vector machine (SVM) algorithms, where the performances were evaluated using 5-fold cross-validation and independent tests. It was found that the SVM-based accuracies were 3.55% and 5.09% lower than the iPseU-CUU predictor for the H_990 and S_628 datasets, respectively. Almost the same-level results were obtained for M_994 and an independent H_200 dataset, even showing a 5.0% improvement for S_200. Then, three different kinds of features, including basic Kmer, general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-General), and nucleotide chemical property (NCP) and nucleotide density (ND) from the iRNA-PseU method, were combined with BPB to show their comprehensive performances, where the effective features are selected by the max-relevance-max-distance (MRMD) method. The best evaluated accuracies of the combined features for the S_628 and M_994 datasets were achieved at 70.54% and 72.45%, which were 2.39% and 0.65% higher than iPseU-CUU. For the S_200 dataset, it was also improved 8% from 69% to 77%. However, there was no obvious improvement for H. sapiens, which was evaluated as approximately 63.23% and 72.0% for the H_990 and H_200 datasets, respectively. The overall performances for Ψ identification using BPB features as well as the combined features were not obviously improved. Although some kinds of feature extraction methods based on the RNA sequence information have been applied to construct the predictors in previous studies, the corresponding accuracies are generally in the range of 60%-70%. Thus, researchers need to reconsider whether there is any sequence feature in the RNA Ψ modification prediction problem.
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Affiliation(s)
- Lijun Dou
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoling Li
- Department of Oncology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.
| | - Huaikun Xiang
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China.
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87
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Borrayo E, May-Canche I, Paredes O, Morales JA, Romo-Vázquez R, Vélez-Pérez H. Whole-Genome k-mer Topic Modeling AssociatesBacterial Families. Genes (Basel) 2020; 11:genes11020197. [PMID: 32075081 PMCID: PMC7074292 DOI: 10.3390/genes11020197] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/07/2020] [Accepted: 02/09/2020] [Indexed: 11/16/2022] Open
Abstract
Alignment-free k-mer-based algorithms in whole genome sequence comparisons remainan ongoing challenge. Here, we explore the possibility to use Topic Modeling for organismwhole-genome comparisons. We analyzed 30 complete genomes from three bacterial families bytopic modeling. For this, each genome was considered as a document and 13-mer nucleotiderepresentations as words. Latent Dirichlet allocation was used as the probabilistic modeling of thecorpus. We where able to identify the topic distribution among analyzed genomes, which is highlyconsistent with traditional hierarchical classification. It is possible that topic modeling may be appliedto establish relationships between genome's composition and biological phenomena.
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Affiliation(s)
- Ernesto Borrayo
- Electronics Department, CUCEI, Universidad de Guadalajara, Jalisco 44100, Mexico;
| | - Isaias May-Canche
- Computer Sciences Department, CUCEI, Universidad de Guadalajara, Jalisco 44100, Mexico; (I.M.-C.); (O.P.); (J.A.M.); (R.R.-V.)
- Instituto Tecnológico de Chetumal, Quintana Roo 77000, Mexico
| | - Omar Paredes
- Computer Sciences Department, CUCEI, Universidad de Guadalajara, Jalisco 44100, Mexico; (I.M.-C.); (O.P.); (J.A.M.); (R.R.-V.)
| | - J. Alejandro Morales
- Computer Sciences Department, CUCEI, Universidad de Guadalajara, Jalisco 44100, Mexico; (I.M.-C.); (O.P.); (J.A.M.); (R.R.-V.)
| | - Rebeca Romo-Vázquez
- Computer Sciences Department, CUCEI, Universidad de Guadalajara, Jalisco 44100, Mexico; (I.M.-C.); (O.P.); (J.A.M.); (R.R.-V.)
| | - Hugo Vélez-Pérez
- Computer Sciences Department, CUCEI, Universidad de Guadalajara, Jalisco 44100, Mexico; (I.M.-C.); (O.P.); (J.A.M.); (R.R.-V.)
- Correspondence:
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88
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Zhao X, Jiao Q, Li H, Wu Y, Wang H, Huang S, Wang G. ECFS-DEA: an ensemble classifier-based feature selection for differential expression analysis on expression profiles. BMC Bioinformatics 2020; 21:43. [PMID: 32024464 PMCID: PMC7003361 DOI: 10.1186/s12859-020-3388-y] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 01/27/2020] [Indexed: 11/27/2022] Open
Abstract
Background Various methods for differential expression analysis have been widely used to identify features which best distinguish between different categories of samples. Multiple hypothesis testing may leave out explanatory features, each of which may be composed of individually insignificant variables. Multivariate hypothesis testing holds a non-mainstream position, considering the large computation overhead of large-scale matrix operation. Random forest provides a classification strategy for calculation of variable importance. However, it may be unsuitable for different distributions of samples. Results Based on the thought of using an ensemble classifier, we develop a feature selection tool for differential expression analysis on expression profiles (i.e., ECFS-DEA for short). Considering the differences in sample distribution, a graphical user interface is designed to allow the selection of different base classifiers. Inspired by random forest, a common measure which is applicable to any base classifier is proposed for calculation of variable importance. After an interactive selection of a feature on sorted individual variables, a projection heatmap is presented using k-means clustering. ROC curve is also provided, both of which can intuitively demonstrate the effectiveness of the selected feature. Conclusions Feature selection through ensemble classifiers helps to select important variables and thus is applicable for different sample distributions. Experiments on simulation and realistic data demonstrate the effectiveness of ECFS-DEA for differential expression analysis on expression profiles. The software is available at http://bio-nefu.com/resource/ecfs-dea.
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Affiliation(s)
- Xudong Zhao
- College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China
| | - Qing Jiao
- College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China
| | - Hangyu Li
- College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China
| | - Yiming Wu
- College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China
| | - Hanxu Wang
- College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China
| | - Shan Huang
- Department of Neurology, The 2nd Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Harbin, 150086, China
| | - Guohua Wang
- College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China. .,State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China.
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89
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Zhang ZY, Yang YH, Ding H, Wang D, Chen W, Lin H. Design powerful predictor for mRNA subcellular location prediction in Homo sapiens. Brief Bioinform 2020; 22:526-535. [PMID: 31994694 DOI: 10.1093/bib/bbz177] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/05/2019] [Accepted: 11/21/2019] [Indexed: 12/14/2022] Open
Abstract
Messenger RNAs (mRNAs) shoulder special responsibilities that transmit genetic code from DNA to discrete locations in the cytoplasm. The locating process of mRNA might provide spatial and temporal regulation of mRNA and protein functions. The situ hybridization and quantitative transcriptomics analysis could provide detail information about mRNA subcellular localization; however, they are time consuming and expensive. It is highly desired to develop computational tools for timely and effectively predicting mRNA subcellular location. In this work, by using binomial distribution and one-way analysis of variance, the optimal nonamer composition was obtained to represent mRNA sequences. Subsequently, a predictor based on support vector machine was developed to identify the mRNA subcellular localization. In 5-fold cross-validation, results showed that the accuracy is 90.12% for Homo sapiens (H. sapiens). The predictor may provide a reference for the study of mRNA localization mechanisms and mRNA translocation strategies. An online web server was established based on our models, which is available at http://lin-group.cn/server/iLoc-mRNA/.
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Affiliation(s)
- Zhao-Yue Zhang
- Center for Informational Biology at University of Electronic Science and Technology of China
| | - Yu-He Yang
- Center for Informational Biology at University of Electronic Science and Technology of China
| | - Hui Ding
- Center for Informational Biology at University of Electronic Science and Technology of China
| | - Dong Wang
- Department of Bioinformatics at Southern Medical University
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy at Chengdu University of Traditional Chinese Medicine
| | - Hao Lin
- Center for Informational Biology at University of Electronic Science and Technology of China
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90
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Zhao S, Jiang H, Liang ZH, Ju H. Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms. Front Genet 2020; 10:1336. [PMID: 32038707 PMCID: PMC6993083 DOI: 10.3389/fgene.2019.01336] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 12/06/2019] [Indexed: 12/15/2022] Open
Abstract
Stroke ranks the second leading cause of death among people over the age of 60 in the world. Stroke is widely regarded as a complex disease that is affected by genetic and environmental factors. Evidence from twin and family studies suggests that genetic factors may play an important role in its pathogenesis. Therefore, research on the genetic association of susceptibility genes can help understand the mechanism of stroke. Genome-wide association study (GWAS) has found a large number of stroke-related loci, but their mechanism is unknown. In order to explore the function of single-nucleotide polymorphisms (SNPs) at the molecular level, in this paper, we integrated 8 GWAS datasets with brain expression quantitative trait loci (eQTL) dataset to identify SNPs and genes which are related to four types of stroke (ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke). Thirty-eight SNPs which can affect 14 genes expression are found to be associated with stroke. Among these 14 genes, 10 genes expression are associated with ischemic stroke, one gene for large artery stroke, six genes for cardioembolic stroke and eight genes for small vessel stroke. To explore the effects of environmental factors on stroke, we identified methylation susceptibility loci associated with stroke using methylation quantitative trait loci (MQTL). Thirty-one of these 38 SNPs are at greater risk of methylation and can significantly change gene expression level. Overall, the genetic pathogenesis of stroke is explored from locus to gene, gene to gene expression and gene expression to phenotype.
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Affiliation(s)
- Sheng Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zong-Hui Liang
- Department of Radiology, Jian'an District Centre Hospital of Fudan University, Shanghai, China
| | - Hong Ju
- Department of Information Engineering, Heilongjiang Biological Science and Technology Career Academy, Harbin, China
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91
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Ao C, Zhang Y, Li D, Zhao Y, Zou Q. Progress in the development of antimicrobial peptide prediction tools. Curr Protein Pept Sci 2020; 22:CPPS-EPUB-103746. [PMID: 31957609 DOI: 10.2174/1389203721666200117163802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 06/12/2019] [Accepted: 07/15/2019] [Indexed: 11/22/2022]
Abstract
Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.
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Affiliation(s)
- Chunyan Ao
- Institute of Fundamental and Frontier Sciences - University of Electronic Science and Technology of China Chengdu. China
| | - Yu Zhang
- Department of neurosurgery - Heilongjiang Province Land Reclamation Headquarters General Hospital Harbin. China
| | - Dapeng Li
- Department of Internal Medicine-Oncology - The Fourth Hospital in Qinhuangdao Hebei. China
| | - Yuming Zhao
- Information and Computer Engineering College - Northeast Forestry University Harbin. China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences - University of Electronic Science and Technology of China Chengdu. China
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92
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Li H, Song M, Yang W, Cao P, Zheng L, Zuo Y. A Comparative Analysis of Single-Cell Transcriptome Identifies Reprogramming Driver Factors for Efficiency Improvement. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 19:1053-1064. [PMID: 32045876 PMCID: PMC7015826 DOI: 10.1016/j.omtn.2019.12.035] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/23/2019] [Accepted: 12/26/2019] [Indexed: 12/11/2022]
Abstract
Terminally differentiated somatic cells can be reprogrammed into a totipotent state through somatic cell nuclear transfer (SCNT). The incomplete reprogramming is the major reason for developmental arrest of SCNT embryos at early stages. In our studies, we found that pathways for autophagy, endocytosis, and apoptosis were incompletely activated in nuclear transfer (NT) 2-cell arrest embryos, whereas extensively inhibited pathways for stem cell pluripotency maintenance, DNA repair, cell cycle, and autophagy may result in NT 4-cell embryos arrest. As for NT normal embryos, a significant shift in expression of developmental transcription factors (TFs) Id1, Pou6f1, Cited1, and Zscan4c was observed. Compared with pluripotent gene Ascl2 being activated only in NT 2-cell, Nanog, Dppa2, and Sall4 had major expression waves in normal development of both NT 2-cell and 4-cell embryos. Additionally, Kdm4b/4d and Kdm5b had been confirmed as key markers in NT 2-cell and 4-cell embryos, respectively. Histone acetylases Kat8, Elp6, and Eid1 were co-activated in NT 2-cell and 4-cell embryos to facilitate normal development. Gadd45a as a key driver functions with Tet1 and Tet2 to improve the efficiency of NT reprogramming. Taken together, our findings provided an important theoretical basis for elucidating the potential molecular mechanisms and identified reprogramming driver factor to improve the efficiency of SCNT reprogramming.
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Affiliation(s)
- Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Mingmin Song
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Wuritu Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Pengbo Cao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China.
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93
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Li Q, Dong B, Wang D, Wang S. Identification of Secreted Proteins From Malaria Protozoa With Few Features. IEEE ACCESS 2020; 8:89793-89801. [DOI: 10.1109/access.2020.2994206] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2025]
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94
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Sun S, Wang C, Ding H, Zou Q. Machine learning and its applications in plant molecular studies. Brief Funct Genomics 2019; 19:40-48. [DOI: 10.1093/bfgp/elz036] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 09/06/2019] [Accepted: 09/15/2019] [Indexed: 01/16/2023] Open
Abstract
Abstract
The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies.
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Affiliation(s)
- Shanwen Sun
- University of Bayreuth in Germany. He is now a postdoctoral fellow at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
| | - Chunyu Wang
- Harbin Institute of Technology in China. He is an associate professor in the School of Computer Science and Technology, Harbin Institute of Technology
| | - Hui Ding
- Inner Mongolia University in China. She is an associate professor in the Center for Informational Biology, University of Electronic Science and Technology of China
| | - Quan Zou
- Harbin Institute of Technology in China. He is a professor in the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
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95
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Wang J, Zhang J, Cai Y, Deng L. DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model. Int J Mol Sci 2019; 20:E6046. [PMID: 31801264 PMCID: PMC6928926 DOI: 10.3390/ijms20236046] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 11/25/2019] [Accepted: 11/26/2019] [Indexed: 01/08/2023] Open
Abstract
MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.
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Affiliation(s)
- Jiacheng Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; (J.W.); (Y.C.)
| | - Jingpu Zhang
- School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan 467000, China;
| | - Yideng Cai
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; (J.W.); (Y.C.)
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; (J.W.); (Y.C.)
- School of Software, Xinjiang University, Urumqi 830008, China
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96
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Wang F, Guan ZX, Dao FY, Ding H. A Brief Review of the Computational Identification of Antifreeze Protein. CURR ORG CHEM 2019. [DOI: 10.2174/1385272823666190718145613] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Lots of cold-adapted organisms could produce antifreeze proteins (AFPs) to counter the freezing of cell fluids by controlling the growth of ice crystal. AFPs have been found in various species such as in vertebrates, invertebrates, plants, bacteria, and fungi. These AFPs from fish, insects and plants displayed a high diversity. Thus, the identification of the AFPs is a challenging task in computational proteomics. With the accumulation of AFPs and development of machine meaning methods, it is possible to construct a high-throughput tool to timely identify the AFPs. In this review, we briefly reviewed the application of machine learning methods in antifreeze proteins identification from difference section, including published benchmark dataset, sequence descriptor, classification algorithms and published methods. We hope that this review will produce new ideas and directions for the researches in identifying antifreeze proteins.
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Affiliation(s)
- Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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97
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Fang T, Zhang Z, Sun R, Zhu L, He J, Huang B, Xiong Y, Zhu X. RNAm5CPred: Prediction of RNA 5-Methylcytosine Sites Based on Three Different Kinds of Nucleotide Composition. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:739-747. [PMID: 31726390 PMCID: PMC6859278 DOI: 10.1016/j.omtn.2019.10.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 10/11/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022]
Abstract
5-methylcytosine (m5C) is one of the most common and abundant post-transcriptional modifications (PTCMs) in RNA. Recent studies showed that m5C plays important roles in many biological functions such as RNA metabolism and cell fate decision. Because most experimental methods that determine m5C sites across the transcriptome are time-consuming and expensive, it is urgent to develop accurate computational methods to identify m5C sites effectively. A benchmark dataset is important for developing and evaluating computational methods. In this work, we constructed four different datasets according to the data redundancy and imbalance. Based on these datasets, we generated three different kinds of features, i.e., KNFs (K-nucleotide frequencies), KSNPFs (K-spaced nucleotide pair frequencies), and pseDNC (pseudo-dinucleotide composition), and then used a support vector machine (SVM) to build our models. Based on the imbalanced and nonredundant dataset, Met935, we extensively studied the three kinds of features and determined an optimal combination of the features. Based on the feature combination, we built models on the three different datasets and compared them with state-of-the-art models. According to the predictive results of the stringent jackknife test, the models based on the three features, 4NF, 1SNPF, and pseDNC, are superior or comparable to other methods. To determine the best model between the models based on the imbalanced dataset Met935 and the balanced dataset Met240, we further evaluated the two models on an independent test set Test1157. Our results demonstrate that the model based on the balanced dataset Met240 achieved the highest recall (68.79%) and the highest Matthews correlation coefficient (MCC) (0.154). In addition, the model is also superior to other state-of-the-art methods according to the integrated parameter MCC on the independent test set. Thus, we selected the model based on Met240 as our final model, which was named RNAm5CPred. In addition, a web server for RNAm5CPred (http://zhulab.ahu.edu.cn/RNAm5CPred/) has been provided to facilitate experimental research.
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Affiliation(s)
- Ting Fang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China; School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Zizheng Zhang
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Rui Sun
- Beijing Baidu Netcom Sciences and Technology Co., Ltd., Beijing, China
| | - Lin Zhu
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Jingjing He
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Bei Huang
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China.
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China; School of Life Sciences, Anhui University, Hefei, Anhui 230601, China.
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98
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Li SH, Guan ZX, Zhang D, Zhang ZM, Huang J, Yang W, Lin H. Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods. Med Chem 2019; 16:605-619. [PMID: 31584379 DOI: 10.2174/1573406415666191004101913] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 06/25/2019] [Accepted: 08/23/2019] [Indexed: 01/28/2023]
Abstract
Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)-poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.
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Affiliation(s)
- Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Huang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wuritu Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Development and Planning Department, Inner Mongolia University, Hohhot, P.R. China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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99
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Meng C, Jin S, Wang L, Guo F, Zou Q. AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine. Front Bioeng Biotechnol 2019; 7:224. [PMID: 31620433 PMCID: PMC6759716 DOI: 10.3389/fbioe.2019.00224] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 09/03/2019] [Indexed: 01/03/2023] Open
Abstract
Antioxidant proteins play important roles in countering oxidative damage in organisms. Because it is time-consuming and has a high cost, the accurate identification of antioxidant proteins using biological experiments is a challenging task. For these reasons, we proposed a model using machine-learning algorithms that we named AOPs-SVM, which was developed based on sequence features and a support vector machine. Using a testing dataset, we conducted a jackknife cross-validation test with the proposed AOPs-SVM classifier and obtained 0.68 in sensitivity, 0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC, and 0.832 in AUC. This outperformed existing classifiers. The experiment results demonstrate that the AOPs-SVM is an effective classifier and contributes to the research related to antioxidant proteins. A web server was built at http://server.malab.cn/AOPs-SVM/index.jsp to provide open access.
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Affiliation(s)
- Chaolu Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China.,College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- College of Intelligence and Computing, Tianjin University, Tianjin, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Lv H, Dao FY, Guan ZX, Zhang D, Tan JX, Zhang Y, Chen W, Lin H. iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice. Front Genet 2019; 10:793. [PMID: 31552096 PMCID: PMC6746913 DOI: 10.3389/fgene.2019.00793] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 07/26/2019] [Indexed: 01/08/2023] Open
Abstract
DNA N6-methyladenine (6mA) is a dominant DNA modification form and involved in many biological functions. The accurate genome-wide identification of 6mA sites may increase understanding of its biological functions. Experimental methods for 6mA detection in eukaryotes genome are laborious and expensive. Therefore, it is necessary to develop computational methods to identify 6mA sites on a genomic scale, especially for plant genomes. Based on this consideration, the study aims to develop a machine learning-based method of predicting 6mA sites in the rice genome. We initially used mono-nucleotide binary encoding to formulate positive and negative samples. Subsequently, the machine learning algorithm named Random Forest was utilized to perform the classification for identifying 6mA sites. Our proposed method could produce an area under the receiver operating characteristic curve of 0.964 with an overall accuracy of 0.917, as indicated by the fivefold cross-validation test. Furthermore, an independent dataset was established to assess the generalization ability of our method. Finally, an area under the receiver operating characteristic curve of 0.981 was obtained, suggesting that the proposed method had good performance of predicting 6mA sites in the rice genome. For the convenience of retrieving 6mA sites, on the basis of the computational method, we built a freely accessible web server named iDNA6mA-Rice at http://lin-group.cn/server/iDNA6mA-Rice.
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Affiliation(s)
- Hao Lv
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiu-Xin Tan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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