2′-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2′-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2′-O-methylation sites. In this study, based on the experimental 2′-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2′-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%.
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References
1.
BachellerieJ.P., CavailleJ., and HuttenhoferA.2002. The expanding snoRNA world. Biochimie, 84, 775–790.
2.
BruknerI., SanchezR., SuckD., et al.1995. Sequence-dependent bending propensity of DNA as revealed by DNase I: Parameters for trinucleotides. EMBO J, 14, 1812–1818.
3.
CaoR., FreitasC., ChanL., et al.2017a. ProLanGO: Protein function prediction using neural machine translation based on a recurrent neural network. Molecules, 22, 1732.
4.
CaoR., WangZ., WangY., et al.2014. SMOQ: A tool for predicting the absolute residue-specific quality of a single protein model with support vector machines. BMC Bioinformatics, 15, 120.
5.
CaoR.Z., AdhikariB., BhattacharyaD., et al.2017b. QAcon: Single model quality assessment using protein structural and contact information with machine learning techniques. Bioinformatics, 33, 586–588.
6.
CaoR.Z., BhattacharyaD., HouJ., et al.2016. DeepQA: Improving the estimation of single protein model quality with deep belief networks. BMC Bioinformatics, 17, 495.
7.
ChangC.C., HsuC.W., and LinC.J.2000. The analysis of decomposition methods for support vector machines. IEEE Trans Neural Netw, 11, 1003–1008.
8.
ChenW., FengP., DingH., et al.2016a. PAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions. Sci. Rep., 6, 35123.
9.
ChenW., FengP., TangH., et al.2016b. Identifying 2′-O-methylationation sites by integrating nucleotide chemical properties and nucleotide compositions. Genomics, 107, 255–258.
10.
ChenW., FengP., TangH., et al.2016c. RAMPred: Identifying the N(1)-methyladenosine sites in eukaryotic transcriptomes. Sci. Rep., 6, 31080.
11.
ChenW., FengP.M., LinH., et al.2013. iRSpot-PseDNC: Identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 41, e68.
12.
ChenW., LeiT.Y., JinD.C., et al.2014. PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition. Anal. Biochem., 456, 53–60.
13.
ChenW., YangH., FengP., et al.2017. iDNA4mC: Identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics, 33, 3518–3523.
14.
ChenW., ZhangX., BrookerJ., et al.2015. PseKNC-general: A cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics, 31, 119–120.
15.
ChenX.X., TangH., LiW.C., et al.2016d. Identification of bacterial cell wall lyases via pseudo amino acid composition. Biomed. Res. Int., 2016, 1654623.
16.
ChouK.C.2011. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol., 273, 236–247.
17.
DaoF.Y., YangH., SuZ.D., et al.2017. Recent advances in conotoxin classification by using machine learning methods. Molecules, 22, 1057.
18.
DecaturW.A., and FournierM.J.2002. rRNA modifications and ribosome function. Trends Biochem. Sci., 27, 344–351.
19.
DongZ.W., ShaoP., DiaoL.T., et al.2012. RTL-P: A sensitive approach for detecting sites of 2′-O-methylation in RNA molecules. Nucleic Acids Res. 40, e157.
20.
FengP., DingH., YangH., et al.2017. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol. Ther. Nucleic Acids, 7, 155–163.
21.
FengP., YangH., DingH., et al.2018. iDNA6mA-PseKNC: Identifying DNA N(6)-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics. [Epub ahead of print]; DOI:10.1016/j.ygeno.2018.01.005.
22.
FengP.M., DingH., ChenW., et al.2013a. Naive Bayes classifier with feature selection to identify phage virion proteins. Comput. Math. Methods Med., 2013, 530696.
23.
FengP.M., LinH., and ChenW.2013b. Identification of antioxidants from sequence information using naive Bayes. Comput. Math. Methods Med., 2013, 567529.
24.
FuL., NiuB., ZhuZ., et al.2012. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 28, 3150–3152.
25.
JiaC., ZuoY., ZouQ., et al.2018. O-GlcNAcPRED-II: An integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinformatics. [Epub ahead of print]; DOI:10.1093/bioinformatics/bty039.
26.
KissT.2002. Small nucleolar RNAs: An abundant group of noncoding RNAs with diverse cellular functions. Cell, 109, 145–148.
27.
LaiH.Y., ChenX.X., ChenW., et al.2017. Sequence-based predictive modeling to identify cancerlectins. Oncotarget, 8, 28169–28175.
28.
LiJ., YangZ., YuB., et al.2005. Methylation protects miRNAs and siRNAs from a 3′-end uridylation activity in Arabidopsis. Curr. Biol., 15, 1501–1507.
29.
LiangZ.Y., LaiH.Y., YangH., et al.2017. Pro54DB: A database for experimentally verified sigma-54 promoters. Bioinformatics, 33, 467–469.
30.
LinH., DengE.Z., DingH., et al.2014. iPro54-PseKNC: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res. 42, 12961–12972.
31.
LinH., LiangZ.Y., TangH., et al.2017. Identifying sigma70 promoters with novel pseudo nucleotide composition. IEEE/ACM Trans. Comput. Biol. Bioinform. [Epub ahead of print]; DOI:10.1109/TCBB.2017.2666141.
32.
MadenB.E.2001. Mapping 2′-O-methyl groups in ribosomal RNA. Methods, 25, 374–382.
33.
ManavalanB., BasithS., ShinT.H., et al.2017. MLACP: Machine-learning-based prediction of anticancer peptides. Oncotarget, 8, 77121–77136.
34.
ManavalanB., and LeeJ.2017. SVMQA: Support-vector-machine-based protein single-model quality assessment. Bioinformatics, 33, 2496–2503.
35.
ManavalanB., ShinT.H., and LeeG.2018a. DHSpred: Support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest. Oncotarget, 9, 1944–1956.
36.
ManavalanB., ShinT.H., and LeeG.2018b. PVP-SVM: Sequence-based prediction of phage virion proteins using a support vector machine. Front. Microbiol., 9, 476.
37.
PengH., LongF., and DingC.2005. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern. Anal. Mach. Intell., 27, 1226–1238.
38.
RamachandranV., and ChenX.2008. Degradation of microRNAs by a family of exoribonucleases in Arabidopsis. Science, 321, 1490–1492.
39.
SchölkopfB., Burges, ChristopherJ.C., and SmolaA.J. (eds). 1999. Advances in Kernel Methods: Support Vectror Learning. MIT Press, Cambridge, MA.
40.
SunW.J., LiJ.H., LiuS., et al.2016. RMBase: A resource for decoding the landscape of RNA modifications from high-throughput sequencing data. Nucleic Acids Res. 44, D259–D265.
41.
TangH., CaoR.Z., WangW., et al.2017. A two-step discriminated method to identify thermophilic proteins. Int. J. Biomath. 10, 1750050.
42.
TangH., ChenW., and LinH.2016. Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique. Mol. Biosyst., 12, 1269–1275.
43.
YangH., TangH., ChenX.X., et al.2016. Identification of secretory proteins in Mycobacterium tuberculosis using pseudo amino acid composition. Biomed. Res. Int. 2016, 5413903.
44.
ZhangT., TanP., WangL., et al.2017. RNALocate: A resource for RNA subcellular localizations. Nucleic Acids Res. 45, D135–D138.
45.
ZhaoY.W., LaiH.Y., TangH., et al.2016. Prediction of phosphothreonine sites in human proteins by fusing different features. Sci. Rep. 6, 34817.
46.
ZhaoY.W., SuZ.D., YangW., et al.2017. IonchanPred 2.0: A tool to predict ion channels and their types. Int J Mol Sci, 18, E1838.
47.
ZhuP.P., LiW.C., ZhongZ.J., et al.2015. Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. Mol. Biosyst., 11, 558–563.
48.
ZouQ., JuY., and LiD.2016a. Protein folds prediction with hierarchical structured SVM. Curr. Proteomics, 13, 79–85.
49.
ZouQ., ZengJ., CaoL., et al.2016b. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing, 173, 346–354.
50.
ZustR., Cervantes-BarraganL., HabjanM., et al.2011. Ribose 2′-O-methylation provides a molecular signature for the distinction of self and non-self mRNA dependent on the RNA sensor Mda5. Nat. Immunol., 12, 137–143.
