DeepM6ASeq-EL: prediction of human N6-methyladenosine (m 6A) sites with LSTM and ensemble learning
Juntao CHEN1, Quan ZOU1,2(), Jing LI3
1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610051, China 2. Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou 571158, China 3. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
N6-methyladenosine (m 6A) is a prevalent methylation modification and plays a vital role in various biological processes, such as metabolism, mRNA processing, synthesis, and transport. Recent studies have suggested that m 6A modification is related to common diseases such as cancer, tumours, and obesity. Therefore, accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of bioinformatics. However, traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs, significant time requirements and inaccurate identification of sites. But through the use of traditional experimental methods, researchers have produced many large databases of m 6A sites. With the support of these basic databases and existing deep learning methods, we developed an m 6A site predictor named DeepM6ASeq-EL, which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard voting. Compared to the state-of-the-art prediction method WHISTLE (average AUC 0.948 and 0.880), the DeepM6ASeq-EL had a lower accuracy in m 6A site prediction (average AUC: 0.861 for the full transcript models and 0.809 for the mature messenger RNA models) when tested on six independent datasets.
D Dunn , J Smith . Occurrence of a new base in the deoxyribonucleic acid of a strain of Bacterium coli. Nature, 1955, 175( 4451): 336– 337 https://doi.org/10.1038/175336a0
2
J M Adams , S Cory . Modified nucleosides and bizarre 5'-termini in mouse myeloma mRNA. Nature, 1975, 255( 5503): 28– 33 https://doi.org/10.1038/255028a0
3
G Lichinchi , S Gao , Y Saletore , G M Gonzalez , V Bansal , Y Wang , C E Mason , T M Rana . Dynamics of the human and viral m 6A RNA methylomes during HIV-1 infection of T cells. Nature Microbiology, 2016, 1( 4): 1– 9
4
J Yin , W Sun , F Li , J Hong , X Li , Y Zhou , Y Lu , M Liu , X Zhang , N Chen , X Jin , J Xue , S Zeng , L Yu , F Zhu . VARIDT 1.0: variability of drug transporter database. Nucleic Acids Res, 2020, 48( D1): D1042– D1050 https://doi.org/10.1093/nar/gkz779
5
K D Meyer , S R Jaffrey . The dynamic epitranscriptome: N6-methyladenosine and gene expression control. Nature Reviews Molecular Cell Biology, 2014, 15( 5): 313– 326 https://doi.org/10.1038/nrm3785
6
J Tang , J Fu , Y Wang , Y Luo , Q Yang , B Li , G Tu , J Hong , X Cui , Y Chen , L Yao , W Xue , F Zhu . Simultaneous improvement in the precision, accuracy, and robustness of label-free proteome quantification by optimizing data manipulation chains. Mol Cell Proteomics, 2019, 18( 8): 1683– 1699 https://doi.org/10.1074/mcp.RA118.001169
7
Q Cui , H Shi , P Ye , L Li , Q Qu , G Sun , G Sun , Z Lu , Y Huang , C-G Yang . m 6A RNA methylation regulates the self-renewal and tumorigenesis of glioblastoma stem cells. Cell Reports, 2017, 18( 11): 2622– 2634 https://doi.org/10.1016/j.celrep.2017.02.059
8
G Jia , Y Fu , X Zhao , Q Dai , G Zheng , Y Yang , C Yi , T Lindahl , T Pan , Y-G Yang . N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nature Chemical Biology, 2011, 7( 12): 885– https://doi.org/10.1038/nchembio.687
9
S Fang , J Pan , C Zhou , H Tian , J He , W Shen , X Jin , X Meng , N Jiang , Z Gong . Circular RNAs serve as novel biomarkers and therapeutic targets in cancers. Curr Gene Ther, 2019, 19( 2): 125– 133 https://doi.org/10.2174/1566523218666181109142756
L Cheng , H Yang , H Zhao , X Pei , H Shi , J Sun , Y Zhang , Z Wang , M Zhou . MetSigDis: a manually curated resource for the metabolic signatures of diseases. Brief Bioinform, 2019, 20( 1): 203– 209 https://doi.org/10.1093/bib/bbx103
12
Q Yang , Y Wang , Y Zhang , F Li , W Xia , Y Zhou , Y Qiu , H Li , F Zhu . NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res, 2020,
13
Y Wang , S Zhang , F Li , Y Zhou , Y Zhang , Z Wang , R Zhang , J Zhu , Y Ren , Y Tan , C Qin , Y Li , X Li , Y Chen , F Zhu . Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res, 2020, 48( D1): D1031– D1041
14
B Li , J Tang , Q Yang , S Li , X Cui , Y Li , Y Chen , W Xue , X Li , F Zhu . NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res, 2017, 45( W1): W162– W170 https://doi.org/10.1093/nar/gkx449
15
B Liu . BioSeq-Analysis: a platform for DNA, RNA, and protein sequence analysis based on machine learning approaches. Briefings in Bioinformatics, 2019, 20( 4): 1280– 1294 https://doi.org/10.1093/bib/bbx165
16
D Dominissini , S Moshitch-Moshkovitz , S Schwartz , M Salmon-Divon , L Ungar , S Osenberg , K Cesarkas , J Jacob-Hirsch , N Amariglio , M Kupiec . Topology of the human and mouse m 6A RNA methylomes revealed by m 6A-seq. Nature, 2012, 485( 7397): 201– 206 https://doi.org/10.1038/nature11112
17
K D Meyer , Y Saletore , P Zumbo , O Elemento , C E Mason , S R Jaffrey . Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons. Cell, 2012, 149( 7): 1635– 1646 https://doi.org/10.1016/j.cell.2012.05.003
18
B Linder , A V Grozhik , A O Olarerin-George , C Meydan , C E Mason , S R Jaffrey . Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nature Methods, 2015, 12( 8): 767– 772 https://doi.org/10.1038/nmeth.3453
19
Y H Li , X X Li , J J Hong , Y X Wang , J B Fu , H Yang , C Y Yu , F C Li , J Hu , W W Xue , Y Y Jiang , Y Z Chen , F Zhu . Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs. Brief Bioinform, 2020, 21( 2): 649– 662 https://doi.org/10.1093/bib/bby130
20
W Xue , F Yang , P Wang , G Zheng , Y Chen , X Yao , F Zhu . What contributes to serotonin-norepinephrine reuptake inhibitors' dual-targeting mechanism? the key role of transmembrane domain 6 in human serotonin and norepinephrine transporters revealed by molecular dynamics simulation. ACS Chem Neurosci, 2018, 9( 5): 1128– 1140 https://doi.org/10.1021/acschemneuro.7b00490
21
W Chen , P Feng , H Ding , H Lin , K-C Chou . iRNA-methyl: identifying N6-methyladenosine sites using pseudo nucleotide composition. Analytical Biochemistry, 2015, 490 : 26– 33 https://doi.org/10.1016/j.ab.2015.08.021
22
J Tang , J Fu , Y Wang , B Li , Y Li , Q Yang , X Cui , J Hong , X Li , Y Chen , W Xue , F Zhu . ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinform, 2020, 21( 2): 621– 636 https://doi.org/10.1093/bib/bby127
23
H Liu , H Wang , Z Wei , S Zhang , G Hua , S-W Zhang , L Zhang , S-J Gao , J Meng , X Chen . MeT-DB V2.0: elucidating context-specific functions of N 6-methyl-adenosine methyltranscriptome. Nucleic Acids Research, 2018, 46( D1): D281– D287 https://doi.org/10.1093/nar/gkx1080
24
J-J Xuan , W-J Sun , P-H Lin , K-R Zhou , S Liu , L-L Zheng , L-H Qu , J-H Yang . RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data. Nucleic Acids Research, 2018, 46( D1): D327– D334 https://doi.org/10.1093/nar/gkx934
25
Z Liu , X Xiao , D-J Yu , J Jia , W-R Qiu , K-C Chou . pRNAm-PC: predicting N6-methyladenosine sites in RNA sequences via physical–chemical properties. Analytical Biochemistry, 2016, 497 : 60– 67 https://doi.org/10.1016/j.ab.2015.12.017
26
M Zhang , J-W Sun , Z Liu , M-W Ren , H-B Shen , D-J Yu . Improving N6-methyladenosine site prediction with heuristic selection of nucleotide physical–chemical properties. Analytical Biochemistry, 2016, 508 : 104– 113 https://doi.org/10.1016/j.ab.2016.06.001
27
Y Zhou , P Zeng , Y-H Li , Z Zhang , Q Cui . SRAMP: prediction of mammalian N6-methyladenosine (m 6A) sites based on sequence-derived features. Nucleic Acids Research, 2016, 44( 10): e91– e91 https://doi.org/10.1093/nar/gkw104
28
W Chen , H Tang , H Lin . MethyRNA: a web server for identification of N6-methyladenosine sites. Journal of Biomolecular Structure and Dynamics, 2017, 35( 3): 683– 687 https://doi.org/10.1080/07391102.2016.1157761
29
C Fan , D Liu , R Huang , Z Chen , L Deng . PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility. BMC Bioinformatics: BioMed Central, 2016, 8 :
30
H Wang , C Liu , L Deng . Enhanced prediction of hot spots at protein-protein interfaces using extreme gradient boosting. Scientific Reports, 2018, 8( 1): 14285– https://doi.org/10.1038/s41598-018-32511-1
31
L Deng , W Li , J Zhang . LDAH2V: exploring meta-paths across multiple networks for lncRNA-disease association prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019,
32
X Qiang , H Chen , X Ye , R Su , L Wei . M6AMRFS: robust prediction of N6-methyladenosine sites with sequence-based features in multiple species. Frontiers in Genetics, 2018, 9 : 495– https://doi.org/10.3389/fgene.2018.00495
33
L Wei , H Chen , R Su . M6APred-EL: a sequence-based predictor for identifying N6-methyladenosine sites using ensemble learning. Molecular Therapy-Nucleic Acids, 2018, 12 : 635– 644 https://doi.org/10.1016/j.omtn.2018.07.004
34
Y Zhang , M Hamada . DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning. BMC bioinformatics, 2018, 19( 19): 524–
35
Q Zou , P Xing , L Wei , B Liu . Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA. Rna, 2019, 25( 2): 205– 218 https://doi.org/10.1261/rna.069112.118
36
K Chen , Z Wei , Q Zhang , X Wu , R Rong , Z Lu , J Su , J P de Magalhaes , D J Rigden , J Meng . WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach. Nucleic Acids Research, 2019, 47( 7): e41– e41 https://doi.org/10.1093/nar/gkz074
37
K Liu , W Chen . iMRM:a platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics, 2020,
38
L P Vu , B F Pickering , Y Cheng , S Zaccara , D Nguyen , G Minuesa , T Chou , A Chow , Y Saletore , M MacKay . The N6-methyladenosine (m 6A)-forming enzyme METTL3 controls myeloid differentiation of normal hematopoietic and leukemia cells. Nature Medicine, 2017, 23( 11): 1369– https://doi.org/10.1038/nm.4416
39
S Ke , E A Alemu , C Mertens , E C Gantman , J J Fak , A Mele , B Haripal , I Zucker-Scharff , M J Moore , C Y Park . A majority of m6A residues are in the last exons, allowing the potential for 3' UTR regulation. Genes & Development, 2015, 29( 19): 2037– 2053
40
S Ke , A Pandya-Jones , Y Saito , J J Fak , C B Vågbø , S Geula , J H Hanna , D L Black , J E Darnell , R B Darnell . m6A mRNA modifications are deposited in nascent pre-mRNA and are not required for splicing but do specify cytoplasmic turnover. Genes & Development, 2017, 31( 10): 990– 1006
41
F Y Dao , H Lv , H Zulfiqar , H Yang , W Su , H Gao , H Ding , H Lin . A computational platform to identify origins of replication sites in eukaryotes. Brief Bioinform, 2020,
42
H Lv , Z M Zhang , S H Li , J X Tan , W Chen , H Lin . Evaluation of different computational methods on 5-methylcytosine sites identification. Briefings in Bioinformatics, 2019,
43
J W Li , Y Q Pu , J J Tang , Q Zou , F Guo . DeepAVP: a dual-channel deep neural network for identifying variable-length antiviral peptides. IEEE Journal of Biomedical and Health Informatics, 2020, 1– 1
44
J Hong , Y Luo , Y Zhang , J Ying , W Xue , T Xie , L Tao , F Zhu . Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning. Brief Bioinform, 2019,
45
J Hong , Y Luo , M Mou , J Fu , Y Zhang , W Xue , T Xie , L Tao , Y Lou , F Zhu . Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery. Brief Bioinform, 2019,
46
F Li , Y Zhou , X Zhang , J Tang , Q Yang , Y Zhang , Y Luo , J Hu , W Xue , Y Qiu , Q He , B Yang , F Zhu . SSizer: determining the sample sufficiency for comparative biological study. J Mol Biol, 2020,
47
T Fang , Z Zhang , R Sun , L Zhu , J He , B Huang , Y Xiong , X Zhu . RNAm5CPred: Prediction of RNA 5-methylcytosine sites based on three different kinds of nucleotide composition. Mol Ther Nucleic Acids, 2019, 18 : 739– 747 https://doi.org/10.1016/j.omtn.2019.10.008
48
J He , T Fang , Z Zhang , B Huang , X Zhu , Y Xiong . PseUI: Pseudouridine sites identification based on RNA sequence information. BMC Bioinformatics, 2018, 19( 1): 306– https://doi.org/10.1186/s12859-018-2321-0
49
B Liu , C Li , K Yan . DeepSVM-fold: protein fold recognition by combining Support Vector Machines and pairwise sequence similarity scores generated by deep learning networks. Briefings in Bioinformatics, 2020, 21( 5): 1733– 1741 https://doi.org/10.1093/bib/bbz098
50
B Liu , X Gao , H Zhang . BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA, and protein sequences at sequence level and residue level based on machine learning approaches. Nucleic Acids Research, 2019, 47( 20): e127– https://doi.org/10.1093/nar/gkz740
51
A Kundaje , W Meuleman , J Ernst , M Bilenky , A Yen , A Heravi-Moussavi , P Kheradpour , Z Zhang , J Wang , M J Ziller . Integrative analysis of 111 reference human epigenomes. Nature, 2015, 518( 7539): 317– 330 https://doi.org/10.1038/nature14248
52
H Lv , F Y Dao , D Zhang , Z X Guan , H Yang , W Su , M L Liu , H Ding , W Chen , H Lin . iDNA-MS: an integrated computational tool for detecting dna modification sites in multiple genomes. iScience, 2020, 23( 4): 100991– https://doi.org/10.1016/j.isci.2020.100991
53
L Wei , Q Zou , M Liao , H Lu , Y Zhao . A novel machine learning method for cytokine-receptor interaction prediction. Combinatorial Chemistry & High Throughput Screening, 2016, 19( 2): 144– 152
54
B Wu , H Zhang , L Lin , H Wang , Y Gao , L Zhao , Y-P P Chen , R Chen , L Gu . A similarity searching system for biological phenotype images using deep convolutional encoder-decoder architecture. Current Bioinformatics, 2019, 14( 7): 628– 639 https://doi.org/10.2174/1574893614666190204150109
55
Z B Lv , C Y Ao , Q Zou . Protein function prediction: from traditional classifier to deep learning. Proteomics, 2019, 19( 14): 2–
56
J Zhang , B N Zhong , P F Wang , C Wang , J X Du . Robust feature learning for online discriminative tracking without large-scale pre-training. Frontiers of Computer Science, 2018, 12( 6): 1160– 1172 https://doi.org/10.1007/s11704-017-6281-8
57
Q J Zhang , L Zhang . Convolutional adaptive denoising autoencoders for hierarchical feature extraction. Frontiers of Computer Science, 2018, 12( 6): 1140– 1148 https://doi.org/10.1007/s11704-016-6107-0
58
N Zheng , K Wang , W Zhan , L Deng . Targeting virus-host protein interactions: feature extraction and machine learning approaches. Current Drug Metabolism, 2019, 20( 3): 177– 184 https://doi.org/10.2174/1389200219666180829121038
59
S Liu , C Liu , L Deng . Machine learning approaches for protein–protein interaction hot spot prediction: progress and comparative assessment. Molecules, 2018, 23( 10): 2535– https://doi.org/10.3390/molecules23102535
60
B Liu , Y Zhu . ProtDec-LTR3.0: protein remote homology detection by incorporating profile-based features into Learning to Rank. IEEE Access, 2019, 7 : 102499– 102507 https://doi.org/10.1109/ACCESS.2019.2929363
61
M Zhang , F Li , T T Marquez-Lago , A Leier , C Fan , C K Kwoh , K-C Chou , J Song , C Jia . MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics, 2019, 35( 17): 2957– 2965 https://doi.org/10.1093/bioinformatics/btz016
62
W Yang , X J Zhu , J Huang , H Ding , H Lin . A brief survey of machine learning methods in protein sub-Golgi localization. Current Bioinformatics, 2019, 14 : 234– 240 https://doi.org/10.2174/1574893613666181113131415
63
M L Liu , W Su , Z X Guan , D Zhang , W Chen , L Liu , H Ding . An overview on predicting protein subchloroplast localization by using machine learning methods. Curr Protein Pept Sci, 2020,
64
L Cheng , Y Jiang , H Ju , J Sun , J Peng , M Zhou , Y Hu . InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk. BMC Genomics, 2018, 19( Suppl 1): 919–
65
Y Ding , J Tang , F Guo . Identification of drug-side effect association via multiple information integration with centered kernel alignment. Neurocomputing, 2019, 325 : 211– 224 https://doi.org/10.1016/j.neucom.2018.10.028
66
X Zhu , J He , S Zhao , W Tao , Y Xiong , S Bi . A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae. Brief Funct Genomics, 2019, 18( 6): 367– 376
67
X Shan , X Wang , C D Li , Y Chu , Y Zhang , Y Xiong , D Q Wei . Prediction of CYP450 enzyme-substrate selectivity based on the network-based label space division method. J Chem Inf Model, 2019, 59( 11): 4577– 4586 https://doi.org/10.1021/acs.jcim.9b00749
68
Y Chu , A C Kaushik , X Wang , W Wang , Y Zhang , X Shan , D R Salahub , Y Xiong , D Q Wei . DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform, 2019,
69
Q Xu , Y Xiong , H Dai , K M Kumari , Q Xu , H Y Ou , D Q Wei . PDC-SGB: prediction of effective drug combinations using a stochastic gradient boosting algorithm. J Theor Biol, 2017, 417 : 1– 7 https://doi.org/10.1016/j.jtbi.2017.01.019
70
H Wei , B Liu . iCircDA-MF: identification of circRNA-disease associations based on matrix factorization. Briefings In Bioinformatics, , https://doi.org/10.1093/bib/bbz057
71
C Jia , Y Zuo , Q Zou . O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinformatics, 2018, 34( 12): 2029– 2036 https://doi.org/10.1093/bioinformatics/bty039
72
W Chen , P Feng , T Liu , D Jin . Recent advances in machine learning methods for predicting heat shock proteins. Curr Drug Metab, 2019, 20( 3): 224– 228 https://doi.org/10.2174/1389200219666181031105916
73
H Wang , Y Ding , J Tang , F Guo . Identification of membrane protein types via multivariate information fusion with Hilbert-Schmidt independence criterion. Neurocomputing, 2020, 383 : 257– 269 https://doi.org/10.1016/j.neucom.2019.11.103
74
B Wang , K Lu , X Zheng , B Su , Y Zhou , P Chen , J Zhang . Early stage identification of Alzheimer’s disease using a two-stage ensemble classifier. Current Bioinformatics, 2018, 13( 5): 529– 535 https://doi.org/10.2174/1574893613666180328093114
75
J Li , L Wei , F Guo , Q Zou . EP3: an ensemble predictor that accurately identifies type III secreted effectors. Briefings in Bioinformatics, 2021, 22( 2): 1918– 1928 https://doi.org/10.1093/bib/bbaa1008
76
X Ru , P Cao , L Li , Q Zou . Selecting essential micrornas using a novel voting method. Molecular Therapy - Nucleic Acids, 2019, 18 : 16– 23 https://doi.org/10.1016/j.omtn.2019.07.019
77
X B Dong , Z W Yu , W M Cao , Y F Shi , Q L Ma . A survey on ensemble learning. Frontiers of Computer Science, 2020, 14( 2): 241– 258 https://doi.org/10.1007/s11704-019-8208-z
78
Y Z He , E E Alem , W Wang . Hybritus: a password strength checker by ensemble learning from the query feedbacks of websites. Frontiers of Computer Science, 2020, 14( 3): 14–
