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Liu Y, Liu Y, Wang GA, Cheng Y, Bi S, Zhu X. BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for Homo sapiens. FRONTIERS IN BIOINFORMATICS 2022; 2:834153. [PMID: 36304324 PMCID: PMC9580886 DOI: 10.3389/fbinf.2022.834153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/20/2022] [Indexed: 12/21/2022] Open
Abstract
As one of the most important posttranslational modifications (PTMs), protein lysine glycation changes the characteristics of the proteins and leads to the dysfunction of the proteins, which may cause diseases. Accurately detecting the glycation sites is of great benefit for understanding the biological function and potential mechanism of glycation in the treatment of diseases. However, experimental methods are expensive and time-consuming for lysine glycation site identification. Instead, computational methods, with their higher efficiency and lower cost, could be an important supplement to the experimental methods. In this study, we proposed a novel predictor, BERT-Kgly, for protein lysine glycation site prediction, which was developed by extracting embedding features of protein segments from pretrained Bidirectional Encoder Representations from Transformers (BERT) models. Three pretrained BERT models were explored to get the embeddings with optimal representability, and three downstream deep networks were employed to build our models. Our results showed that the model based on embeddings extracted from the BERT model pretrained on 556,603 protein sequences of UniProt outperforms other models. In addition, an independent test set was used to evaluate and compare our model with other existing methods, which indicated that our model was superior to other existing models.
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Affiliation(s)
| | | | | | | | - Shoudong Bi
- *Correspondence: Shoudong Bi, ; Xiaolei Zhu,
| | - Xiaolei Zhu
- *Correspondence: Shoudong Bi, ; Xiaolei Zhu,
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2
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Staem5: A novel computational approachfor accurate prediction of m5C site. MOLECULAR THERAPY. NUCLEIC ACIDS 2021; 26:1027-1034. [PMID: 34786208 PMCID: PMC8571400 DOI: 10.1016/j.omtn.2021.10.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/27/2021] [Accepted: 10/06/2021] [Indexed: 12/25/2022]
Abstract
5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.
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3
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WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction. Methods 2021; 203:378-382. [PMID: 34245870 DOI: 10.1016/j.ymeth.2021.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/28/2021] [Accepted: 07/05/2021] [Indexed: 01/12/2023] Open
Abstract
The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orchestrated in the living organisms, those markers that cannot be unambiguously recovered from the primary sequence often help to predict other biological events. To the best of our knowledge, there is no current tool to build and deploy machine learning models that consider genomic evidence. We therefore developed the WHISTLE server, the first machine learning platform based on genomic coordinates. It features convenient covariate extraction and model web deployment with 46 distinct genomic features integrated along with the conventional sequence features. We showed that, when predicting m6A sites from SRAMP project, the model integrating genomic features substantially outperformed those based on only sequence features. The WHISTLE server should be a useful tool for studying biological attributes specifically associated with genomic coordinates, and is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/whi2.
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Abstract
Background:
Bioluminescence is a unique and significant phenomenon in nature.
Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical
research, including for gene expression analysis and bioluminescence imaging technology. In recent
years, researchers have identified a number of methods for predicting bioluminescent proteins
(BLPs), which have increased in accuracy, but could be further improved.
Method:
In this study, a new bioluminescent proteins prediction method, based on a voting
algorithm, is proposed. Four methods of feature extraction based on the amino acid sequence were
used. 314 dimensional features in total were extracted from amino acid composition,
physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest
MCC value to establish the optimal prediction model, a voting algorithm was then used to build the
model. To create the best performing model, the selection of base classifiers and vote counting rules
are discussed.
Results:
The proposed model achieved 93.4% accuracy, 93.4% sensitivity and
91.7% specificity in the test set, which was better than any other method. A previous prediction of
bioluminescent proteins in three lineages was also improved using the model building method,
resulting in greatly improved accuracy.
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Affiliation(s)
- Shulin Zhao
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba Science City, Japan
| | - Jun Zhang
- Rehabilitation Department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Shuguang Han
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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5
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Niu M, Lin Y, Zou Q. sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks. PLANT MOLECULAR BIOLOGY 2021; 105:483-495. [PMID: 33385273 DOI: 10.1007/s11103-020-01102-y] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 12/01/2020] [Indexed: 06/12/2023]
Abstract
KEY MESSAGE We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (sgRNA) plays an important role in gene redirection and editing. sgRNA has played an important role in the improvement of agronomic species, but there is a lack of effective bioinformatics tools to identify the activity of sgRNA in agronomic species. Therefore, it is necessary to develop a method based on machine learning to identify sgRNA high on-target activity. In this work, we proposed a simple convolutional neural network method to identify sgRNA high on-target activity. Our study used one-hot encoding and k-mers for sequence data conversion and a voting algorithm for constructing the convolutional neural network ensemble model sgRNACNN for the prediction of sgRNA activity. The ensemble model sgRNACNN was used for predictions in four crops: Glycine max, Zea mays, Sorghum bicolor and Triticum aestivum. The accuracy rates of the four crops in the sgRNACNN model were 82.43%, 80.33%, 78.25% and 87.49%, respectively. The experimental results showed that sgRNACNN realizes the identification of high on-target activity sgRNA of agronomic data and can meet the demands of sgRNA activity prediction in agronomy to a certain extent. These results have certain significance for guiding crop gene editing and academic research. The source code and relevant dataset can be found in the following link: https://github.com/nmt315320/sgRNACNN.git .
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Affiliation(s)
- Mengting Niu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuan Lin
- Department of System Integration, Sparebanken Vest, Bergen, Norway.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
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6
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Huang Q, Zhou W, Guo F, Xu L, Zhang L. 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning. PeerJ 2021; 9:e10813. [PMID: 33604189 PMCID: PMC7866889 DOI: 10.7717/peerj.10813] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 12/30/2020] [Indexed: 01/03/2023] Open
Abstract
With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, Mus musculus, and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/.
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Affiliation(s)
- Qianfei Huang
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
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7
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He S, Guo F, Zou Q, HuiDing. MRMD2.0: A Python Tool for Machine Learning with Feature Ranking and Reduction. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200503030350] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
The study aims to find a way to reduce the dimensionality of the dataset.
Background:
Dimensionality reduction is the key issue of the machine learning process. It does
not only improve the prediction performance but also could recommend the intrinsic features and
help to explore the biological expression of the machine learning “black box”.
Objective:
A variety of feature selection algorithms are used to select data features to achieve
dimensionality reduction.
Methods:
First, MRMD2.0 integrated 7 different popular feature ranking algorithms with
PageRank strategy. Second, optimized dimensionality was detected with forward adding strategy.
Result:
We have achieved good results in our experiments.
Conclusion:
Several works have been tested with MRMD2.0. It showed well performance.
Otherwise, it also can draw the performance curves according to the feature dimensionality. If
users want to sacrifice accuracy for fewer features, they can select the dimensionality from the
performance curves.
Other:
We developed friendly python tools together with the web server. The users could upload
their csv, arff or libsvm format files. Then the webserver would help to rank features and find the
optimized dimensionality.
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Affiliation(s)
- Shida He
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - HuiDing
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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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: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [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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9
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Zhuang J, Liu D, Lin M, Qiu W, Liu J, Chen S. PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm. Front Genet 2021; 12:773882. [PMID: 34868261 PMCID: PMC8637112 DOI: 10.3389/fgene.2021.773882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/04/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desirable as the lab exploratory techniques are expensive and time-consuming. Results: In this work, we propose a deep learning framework, called PseUdeep, to identify Ψ sites of three species: H. sapiens, S. cerevisiae, and M. musculus. In this method, three encoding methods are used to extract the features of RNA sequences, that is, one-hot encoding, K-tuple nucleotide frequency pattern, and position-specific nucleotide composition. The three feature matrices are convoluted twice and fed into the capsule neural network and bidirectional gated recurrent unit network with a self-attention mechanism for classification. Conclusion: Compared with other state-of-the-art methods, our model gets the highest accuracy of the prediction on the independent testing data set S-200; the accuracy improves 12.38%, and on the independent testing data set H-200, the accuracy improves 0.68%. Moreover, the dimensions of the features we derive from the RNA sequences are only 109,109, and 119 in H. sapiens, M. musculus, and S. cerevisiae, which is much smaller than those used in the traditional algorithms. On evaluation via tenfold cross-validation and two independent testing data sets, PseUdeep outperforms the best traditional machine learning model available. PseUdeep source code and data sets are available at https://github.com/dan111262/PseUdeep.
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Affiliation(s)
- Jujuan Zhuang
- College of Science, Dalian Maritime University, Dalian, China
| | - Danyang Liu
- College of Science, Dalian Maritime University, Dalian, China
| | - Meng Lin
- College of Science, Dalian Maritime University, Dalian, China
| | - Wenjing Qiu
- Electrical and Information Engineering, Anhui University of Technology, Anhui, China
- Geneis (Beijing) Co., Ltd., Beijing, China
| | | | - Size Chen
- Department of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Guangdong Provincial Engineering Research Center for Esophageal Cancer Precise Therapy, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Central Laboratory, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- *Correspondence: Size Chen,
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10
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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: 21] [Impact Index Per Article: 5.3] [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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11
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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: 1.3] [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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12
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Tang R, Zhang Y, Liang C, Xu J, Meng Q, Hua J, Liu J, Zhang B, Yu X, Shi S. The role of m6A-related genes in the prognosis and immune microenvironment of pancreatic adenocarcinoma. PeerJ 2020; 8:e9602. [PMID: 33062408 PMCID: PMC7528816 DOI: 10.7717/peerj.9602] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 07/03/2020] [Indexed: 12/13/2022] Open
Abstract
Background Pancreatic adenocarcinoma (PAAD) is among the most lethal diseases and has a dismal prognosis; however, efficient treatment is currently limited. Several studies have observed epigenetic variation during tumorigenesis, suggesting the potential role of RNA methylation, especially N6-methyladenosine (m6A) modification, as a novel epigenetic modification mediating PAAD prognosis. Methods The expression levels of m6A-related genes were downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA) and Genotype-Tissue Expression (GTEx) projects, and the findings were validated in four Expression Omnibus (GEO) datasets. A predictive model was constructed using a lasso regression and evaluated by a survival analysis and receiver operating characteristic curve. Consensus clustering identified two distinct subgroups with different immune activity signatures based on the expression pattern of m6A-related genes. The relationship between the mutation state of m6A-related genes and infiltration of immune cells was established and visualized using Tumor Immune Estimation Resource (https://cistrome.shinyapps.io/timer/). Results Fourteen of twenty-one m6A-related genes were differentially expressed between PAAD and normal tissues in TCGA-GTEx cohort. Among these genes, HNRNPC, IGF2BP2 and YTHDF1 were further validated in four GEO datasets. Moreover, an m6A-based model exhibited moderate accuracy in predicting overall survival in PAAD samples. Additionally, potential m6A modification targets were screened by selecting genes from a set of 23,391 genes that not only harbored the most m6A-modified sites but also showed a robust correlation with PAAD survival. Moreover, we correlated the expression level of m6A-related genes with the immune microenvironment of pancreatic cancer for the first time. Specifically, both arm-level gain and deletion of ALKBH5 decreased the infiltration of CD8+T cells (P < 0.05 and P < 0.01, respectively). Conclusion Collectively, our findings suggest a novel anticancer strategy for restoring balanced RNA methylation in tumor cells and guide clinical physicians in developing a new practical approach for considering the impact of related genes on prognosis.
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Affiliation(s)
- Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Yiyin Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Chen Liang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Qingcai Meng
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jie Hua
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jiang Liu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Bo Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
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13
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Chen T, Wang X, Chu Y, Wang Y, Jiang M, Wei DQ, Xiong Y. T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm. Front Microbiol 2020; 11:580382. [PMID: 33072049 PMCID: PMC7541839 DOI: 10.3389/fmicb.2020.580382] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/21/2020] [Indexed: 12/19/2022] Open
Abstract
Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time- and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.
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Affiliation(s)
- Tianhang Chen
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xiangeng Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Mingming Jiang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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14
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Wahab A, Mahmoudi O, Kim J, Chong KT. DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning. Cells 2020; 9:E1756. [PMID: 32707969 PMCID: PMC7465362 DOI: 10.3390/cells9081756] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/17/2020] [Accepted: 07/17/2020] [Indexed: 11/24/2022] Open
Abstract
N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.
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Affiliation(s)
- Abdul Wahab
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea; (A.W.); (O.M.)
| | - Omid Mahmoudi
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea; (A.W.); (O.M.)
| | - Jeehong Kim
- Department of New & Renewable Energy, VISION College of Jeonju, Jeonju 55069, Korea
| | - Kil To Chong
- Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Korea
- Advance Electronics & Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
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15
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Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9235920. [PMID: 32596396 PMCID: PMC7273372 DOI: 10.1155/2020/9235920] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 04/22/2020] [Indexed: 11/17/2022]
Abstract
Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of k-spaced amino acid pairs (CKSAAP), and selected informative amino acid pairs through the use of a feature selection technique. A training dataset including 1117 human enzymes and 2099 nonenzymes and a test dataset including 684 human enzymes and 1270 nonenzymes were constructed to train and test the proposed model. The results of jackknife cross-validation showed that the overall accuracy was 76.46% for the training set and 76.21% for the test set, which are higher than the 72.6% achieved in previous research. Furthermore, various feature extraction methods and mainstream classifiers were compared in this task, and informative feature parameters of k-spaced amino acid pairs were selected and compared. The results suggest that our classifier can be used in human enzyme identification effectively and efficiently and can help to understand their functions and develop new drugs.
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16
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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: 7.8] [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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17
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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: 2.0] [Reference Citation Analysis] [Abstract] [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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18
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Mahmoudi O, Wahab A, Chong KT. iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm. Genes (Basel) 2020; 11:genes11050529. [PMID: 32397453 PMCID: PMC7288457 DOI: 10.3390/genes11050529] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 04/30/2020] [Accepted: 05/05/2020] [Indexed: 12/12/2022] Open
Abstract
One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequences through the high-throughput laboratory techniques but still, these lab processes are time consuming and costly. Diverse computational methods have been proposed to identify m6A sites accurately. In this paper, we proposed a computational model named iMethyl-deep to identify m6A Saccharomyces Cerevisiae on two benchmark datasets M6A2614 and M6A6540 by using single nucleotide resolution to convert RNA sequence into a high quality feature representation. The iMethyl-deep obtained 89.19% and 87.44% of accuracy on M6A2614 and M6A6540 respectively which show that our proposed method outperforms the state-of-the-art predictors, at least 8.44%, 8.96%, 8.69% and 0.173 on M6A2614 and 15.47%, 28.52%, 25.54 and 0.5 on M6A6540 higher in terms of four metrics Sp, Sn, ACC and MCC respectively. Meanwhile, M6A6540 dataset never used to train a model.
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Affiliation(s)
- Omid Mahmoudi
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea; (O.M.); (A.W.)
| | - Abdul Wahab
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea; (O.M.); (A.W.)
| | - Kil To Chong
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
- Correspondence:
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19
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Meng C, Hu Y, Zhang Y, Guo F. PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides. Front Bioeng Biotechnol 2020; 8:245. [PMID: 32296690 PMCID: PMC7137786 DOI: 10.3389/fbioe.2020.00245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/09/2020] [Indexed: 12/11/2022] Open
Abstract
Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.
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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
| | - Yang Hu
- School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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20
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Govindaraj RG, Subramaniyam S, Manavalan B. Extremely-randomized-tree-based Prediction of N 6-Methyladenosine Sites in Saccharomyces cerevisiae. Curr Genomics 2020; 21:26-33. [PMID: 32655295 PMCID: PMC7324895 DOI: 10.2174/1389202921666200219125625] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 12/28/2019] [Accepted: 01/24/2020] [Indexed: 02/07/2023] Open
Abstract
Introduction N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. Methodology In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. Results Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. Conclusion In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.
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Affiliation(s)
- Rajiv G Govindaraj
- 1HotSpot Therapeutics, 50 Milk Street, 16 Floor, Boston, MA02109, USA; 2Research and Development Center, In-silicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of Korea; 3Department of Biotechnology, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu641048, India; 4Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Sathiyamoorthy Subramaniyam
- 1HotSpot Therapeutics, 50 Milk Street, 16 Floor, Boston, MA02109, USA; 2Research and Development Center, In-silicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of Korea; 3Department of Biotechnology, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu641048, India; 4Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Balachandran Manavalan
- 1HotSpot Therapeutics, 50 Milk Street, 16 Floor, Boston, MA02109, USA; 2Research and Development Center, In-silicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of Korea; 3Department of Biotechnology, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu641048, India; 4Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
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21
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Hou R, Wang L, Wu YJ. Predicting ATP-Binding Cassette Transporters Using the Random Forest Method. Front Genet 2020; 11:156. [PMID: 32269586 PMCID: PMC7109328 DOI: 10.3389/fgene.2020.00156] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 02/11/2020] [Indexed: 12/21/2022] Open
Abstract
ATP-binding cassette (ABC) proteins play important roles in a wide variety of species. These proteins are involved in absorbing nutrients, exporting toxic substances, and regulating potassium channels, and they contribute to drug resistance in cancer cells. Therefore, the identification of ABC transporters is an urgent task. The present study used 188D as the feature extraction method, which is based on sequence information and physicochemical properties. We also visualized the feature extracted by t-Distributed Stochastic Neighbor Embedding (t-SNE). The sample based on the features extracted by 188D may be separated. Further, random forest (RF) is an efficient classifier to identify proteins. Under the 10-fold cross-validation of the model proposed here for a training set, the average accuracy rate of 10 training sets was 89.54%. We obtained values of 0.87 for specificity, 0.92 for sensitivity, and 0.79 for MCC. In the testing set, the accuracy achieved was 89%. These results suggest that the model combining 188D with RF is an optimal tool to identify ABC transporters.
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Affiliation(s)
- Ruiyan Hou
- Laboratory of Molecular Toxicology, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,College of Life Science, University of Chinese Academy of Sciences, Beijing, China
| | - Lida Wang
- Department of Scientific Research, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yi-Jun Wu
- Laboratory of Molecular Toxicology, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
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22
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Wang C, Zhang J, Wang X, Han K, Guo M. Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion. Front Genet 2020; 11:5. [PMID: 32117433 PMCID: PMC7010852 DOI: 10.3389/fgene.2020.00005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 01/06/2020] [Indexed: 12/23/2022] Open
Abstract
Complex diseases seriously affect people's physical and mental health. The discovery of disease-causing genes has become a target of research. With the emergence of bioinformatics and the rapid development of biotechnology, to overcome the inherent difficulties of the long experimental period and high cost of traditional biomedical methods, researchers have proposed many gene prioritization algorithms that use a large amount of biological data to mine pathogenic genes. However, because the currently known gene–disease association matrix is still very sparse and lacks evidence that genes and diseases are unrelated, there are limits to the predictive performance of gene prioritization algorithms. Based on the hypothesis that functionally related gene mutations may lead to similar disease phenotypes, this paper proposes a PU induction matrix completion algorithm based on heterogeneous information fusion (PUIMCHIF) to predict candidate genes involved in the pathogenicity of human diseases. On the one hand, PUIMCHIF uses different compact feature learning methods to extract features of genes and diseases from multiple data sources, making up for the lack of sparse data. On the other hand, based on the prior knowledge that most of the unknown gene–disease associations are unrelated, we use the PU-Learning strategy to treat the unknown unlabeled data as negative examples for biased learning. The experimental results of the PUIMCHIF algorithm regarding the three indexes of precision, recall, and mean percentile ranking (MPR) were significantly better than those of other algorithms. In the top 100 global prediction analysis of multiple genes and multiple diseases, the probability of recovering true gene associations using PUIMCHIF reached 50% and the MPR value was 10.94%. The PUIMCHIF algorithm has higher priority than those from other methods, such as IMC and CATAPULT.
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Affiliation(s)
- Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jie Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xueping Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Ke Han
- School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.,Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing, China
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23
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Lv Z, Zhang J, Ding H, Zou Q. RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites. Front Bioeng Biotechnol 2020; 8:134. [PMID: 32175316 PMCID: PMC7054385 DOI: 10.3389/fbioe.2020.00134] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 02/10/2020] [Indexed: 12/21/2022] Open
Abstract
One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.
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Affiliation(s)
- Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Jun Zhang
- Rehabilitation Department, 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
| | - Quan Zou
- 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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24
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Huang Q, Zhang J, Wei L, Guo F, Zou Q. 6mA-RicePred: A Method for Identifying DNA N 6-Methyladenine Sites in the Rice Genome Based on Feature Fusion. FRONTIERS IN PLANT SCIENCE 2020; 11:4. [PMID: 32076430 PMCID: PMC7006724 DOI: 10.3389/fpls.2020.00004] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 01/06/2020] [Indexed: 06/01/2023]
Abstract
MOTIVATION The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. RESULTS In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.
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Affiliation(s)
- Qianfei Huang
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jun Zhang
- Rehabilitation Department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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25
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Wang X, Zhu X, Ye M, Wang Y, Li CD, Xiong Y, Wei DQ. STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity. Front Bioeng Biotechnol 2019; 7:306. [PMID: 31781551 PMCID: PMC6851049 DOI: 10.3389/fbioe.2019.00306] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 10/17/2019] [Indexed: 12/11/2022] Open
Abstract
Membrane transport proteins play crucial roles in the pharmacokinetics of substrate drugs, the drug resistance in cancer and are vital to the process of drug discovery, development and anti-cancer therapeutics. However, experimental methods to profile a substrate drug against a panel of transporters to determine its specificity are labor intensive and time consuming. In this article, we aim to develop an in silico multi-label classification approach to predict whether a substrate can specifically recognize one of the 13 categories of drug transporters ranging from ATP-binding cassette to solute carrier families using both structural fingerprints and chemical ontologies information of substrates. The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity. This method builds predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes union of label sets for a compound as final prediction. NLSP lies into the ensembles of multi-label classifier category in multi-label learning field. We utilized Cramér's V statistics to quantify the label correlations and depicted them via a heatmap. The jackknife tests and iterative stratification based cross-validation method were adopted on a benchmark dataset to evaluate the prediction performance of the proposed models both in multi-label and label-wise manner. Compared with other powerful multi-label methods, ML-kNN, MTSVM, and RAkELd, our multi-label classification model of NLPS-RF (random forest-based NLSP) has proven to be a feasible and effective model, and performed satisfactorily in the predictive task of transporter-substrate specificity. The idea behind NLSP method is intriguing and the power of NLSP remains to be explored for the multi-label learning problems in bioinformatics. The benchmark dataset, intermediate results and python code which can fully reproduce our experiments and results are available at https://github.com/dqwei-lab/STS.
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Affiliation(s)
- Xiangeng Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, China
| | - Mingzhi Ye
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng-Dong Li
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.,Peng Cheng Laboratory, Shenzhen, China
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