iRNA-PseU: Identifying RNA pseudouridine sites

Molecular Therapy Nucleic Acids

Volumn 5, Issue , 2016, Pages e332-

  Chen, Wei ^a,b   Tang, Hua ^c   Ye, Jing ^a   Lin, Hao ^b,d   Chou, Kuo Chen ^b,e  

^a NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b GORDON LIFE SCIENCE INSTITUTE   (United States)

^c SOUTHWEST MEDICAL UNIVERSITY   (China)

^d UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^e KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

iRNA PseU; nucleotide chemical property; nucleotide frequency; pseudouridine; Web server; site

Indexed keywords

PSEUDOURIDINE; RNA; ADENOSINE; CYTIDINE; FUNCTIONAL GROUP; GUANOSINE; MESSENGER RNA; NUCLEOTIDE; RIBOSOME RNA; SMALL NUCLEOLAR RNA; TRANSFER RNA; URIDINE;

ALGORITHM; ARTICLE; CHEMICAL COMPOSITION; DATA PROCESSING; DENSITY; FREQUENCY; GENOME ANALYSIS; INTERNET; JACKKNIFE TEST; ONLINE SYSTEM; PREDICTION; PREDICTOR VARIABLE; PRIORITY JOURNAL; RNA SEQUENCE; CODON USAGE; HUMAN; HYDROGEN BOND; NONHUMAN;

EID: 85015236342     PISSN: None     EISSN: 21622531     Source Type: Journal    
DOI: 10.1038/mtna.2016.37     Document Type: Article

References (85)

1. 1 Hudson, GA, Bloomingdale, RJ, Znosko, BM, Thermodynamic contribution and nearest-neighbor parameters of pseudouridine-adenosine base pairs in oligoribonucleotides. RNA 19 (2013), 1474–1482.
2. 2 Ge, J, Yu, YT, RNA pseudouridylation: new insights into an old modification. Trends Biochem Sci 38 (2013), 210–218.
3. 3 Charette, M, Gray, MW, Pseudouridine in RNA: what, where, how, and why. IUBMB Life 49 (2000), 341–351.
4. 4 Davis, DR, Veltri, CA, Nielsen, L, An RNA model system for investigation of pseudouridine stabilization of the codon-anticodon interaction in tRNALys, tRNAHis and tRNATyr. J Biomol Struct Dyn 15 (1998), 1121–1132.
5. 5 Basak, A, Query, CC, A pseudouridine residue in the spliceosome core is part of the filamentous growth program in yeast. Cell Rep 8 (2014), 966–973.
6. 6 Karijolich, J, Yu, YT, The new era of RNA modification. RNA 21 (2015), 659–660.
7. 7 Carlile, TM, Rojas-Duran, MF, Zinshteyn, B, Shin, H, Bartoli, KM, Gilbert, WV, Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells. Nature 515 (2014), 143–146.
8. 8 Lovejoy, AF, Riordan, DP, Brown, PO, Transcriptome-wide mapping of pseudouridines: pseudouridine synthases modify specific mRNAs in S. cerevisiae. PLoS One, 9, 2014, e110799.
9. 9 Schwartz, S, Bernstein, DA, Mumbach, MR, Jovanovic, M, Herbst, RH, León-Ricardo, BX, et al. Transcriptome-wide mapping reveals widespread dynamic-regulated pseudouridylation of ncRNA and mRNA. Cell 159 (2014), 148–162.
10. 10 Li, X, Zhu, P, Ma, S, Song, J, Bai, J, Sun, F, et al. Chemical pulldown reveals dynamic pseudouridylation of the mammalian transcriptome. Nat Chem Biol 11 (2015), 592–597.
11. 11 Li, YH, Zhang, G, Cui, Q, PPUS: a web server to predict PUS-specific pseudouridine sites. Bioinformatics 31 (2015), 3362–3364.
12. 12 Chou, KC, Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273 (2011), 236–247.
13. 13 Chen, W, Feng, PM, Lin, H, Chou, KC, iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res, 41, 2013, e68.
14. 14 Lin, H, Deng, EZ, Ding, H, Chen, W, Chou, KC, iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 42 (2014), 12961–12972.
15. 15 Liu, Z, Xiao, X, Qiu, WR, Chou, KC, iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem 474 (2015), 69–77.
16. 16 Chen, W, Feng, P, Ding, H, Lin, H, Chou, KC, iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal Biochem 490 (2015), 26–33.
17. 17 Jia, J, Liu, Z, Xiao, X, Liu, B, Chou, KC, iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol 377 (2015), 47–56.
18. 18 Liu, B, Fang, L, Long, R, Lan, X, Chou, KC, iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics 32 (2016), 362–369.
19. 19 Jia, J, Liu, Z, Xiao, X, Liu, B, Chou, KC, iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal Biochem 497 (2016), 48–56.
20. 20 Liu, Z, Xiao, X, Yu, DJ, Jia, J, Qiu, WR, Chou, KC, pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal Biochem 497 (2016), 60–67.
21. 21 Jia, J, Liu, Z, Xiao, X, Liu, B, Chou, KC, iPPBS-Opt: a sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets. Molecules, 21, 2016, 95.
22. 22 Chen, J, Liu, H, Yang, J, Chou, KC, Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33 (2007), 423–428.
23. 23 Chou, KC, Prediction of protein signal sequences and their cleavage sites. Proteins 42 (2001), 136–139.
24. 24 Xu, Y, Ding, J, Wu, LY, Chou, KC, iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One, 8, 2013, e55844.
25. 25 Xiao, X, Min, JL, Lin, WZ, Liu, Z, Cheng, X, Chou, KC, iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J Biomol Struct Dyn 33 (2015), 2221–2233.
26. 26 Chen, W, Ding, H, Feng, P, et al. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 7 (2016), 16895–16909 10.18632/oncotarget.17815.
27. 27 Chen, W, Feng, PM, Deng, EZ, Lin, H, Chou, KC, iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem 462 (2014), 76–83.
28. 28 Chen, W, Feng, PM, Lin, H, Chou, KC, iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Res Int, 2014, 2014, 623149.
29. 29 Ding, H, Deng, EZ, Yuan, LF, Liu, L, Lin, H, Chen, W, et al. iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. Biomed Res Int, 2014, 2014, 286419.
30. 30 Liu, B, Fang, L, Liu, F, Wang, X, Chen, J, Chou, KC, Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS One, 10, 2015, e0121501.
31. 31 Chou, KC, A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem 268 (1993), 16938–16948.
32. 32 Chou, KC, Wu, ZC, Xiao, X, iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol Biosyst 8 (2012), 629–641.
33. 33 Lin, WZ, Fang, JA, Xiao, X, Chou, KC, iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol Biosyst 9 (2013), 634–644.
34. 34 Xiao, X, Wu, ZC, Chou, KC, iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J Theor Biol 284 (2011), 42–51.
35. 35 Xiao, X, Wang, P, Lin, WZ, Jia, JH, Chou, KC, iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 436 (2013), 168–177.
36. 36 Chou, KC, Some remarks on predicting multi-label attributes in molecular biosystems. Mol Biosyst 9 (2013), 1092–1100.
37. 37 Chou, KC, Zhang, CT, Prediction of protein structural classes. Crit Rev Biochem Mol Biol 30 (1995), 275–349.
38. 38 Shen, HB, Yang, J, Chou, KC, Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids 33 (2007), 57–67.
39. 39 Chou, KC, Cai, YD, Prediction and classification of protein subcellular location-sequence-order effect and pseudo amino acid composition. J Cell Biochem 90 (2003), 1250–1260.
40. 40 Chou, KC, Cai, YD, Prediction of membrane protein types by incorporating amphipathic effects. J Chem Inf Model 45 (2005), 407–413.
41. 41 Mondal, S, Pai, PP, Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol 356 (2014), 30–35.
42. 42 Dehzangi, A, Heffernan, R, Sharma, A, Lyons, J, Paliwal, K, Sattar, A, Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC. J Theor Biol 364 (2015), 284–294.
43. 43 Fan, GL, Zhang, XY, Liu, YL, Nang, Y, Wang, H, DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns. J Comput Chem 36 (2015), 2317–2327.
44. 44 Kabir, M, Hayat, M, iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples. Mol Genet Genomics 291 (2016), 285–296.
45. 45 Kumar, R, Srivastava, A, Kumari, B, Kumar, M, Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. J Theor Biol 365 (2015), 96–103.
46. 46 Chen, W, Feng, P, Ding, H, Lin, H, Chou, KC, Using deformation energy to analyze nucleosome positioning in genomes. Genomics 107 (2016), 69–75.
47. 47 Chou, KC, Jiang, SP, Liu, WM, Fee, CH, Graph theory of enzyme kinetics: 1. Steady-state reaction system. Scientia Sinica 22 (1979), 341–358.
48. 48 Chou, KC, Forsén, S, Graphical rules for enzyme-catalysed rate laws. Biochem J 187 (1980), 829–835.
49. 49 Zhou, GP, Deng, MH, An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J 222 (1984), 169–176.
50. 50 Chou, KC, Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady-state systems. Biophys Chem 35 (1990), 1–24.
51. 51 Althaus, IW, Gonzales, AJ, Chou, JJ, Romero, DL, Deibel, MR, Chou, KC, et al. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem 268 (1993), 14875–14880.
52. 52 Althaus, IW, Chou, JJ, Gonzales, AJ, Deibel, MR, Chou, KC, Kezdy, FJ, et al. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 32 (1993), 6548–6554.
53. 53 Chou, KC, Graphic rule for drug metabolism systems. Curr Drug Metab 11 (2010), 369–378.
54. 54 Wu, ZC, Xiao, X, Chou, KC, 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. J Theor Biol 267 (2010), 29–34.
55. 55 Chou, KC, Lin, WZ, Xiao, X, Wenxiang: a web-server for drawing wenxiang diagrams. Natural Science 3 (2011), 862–865.
56. 56 Zhou, GP, The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol 284 (2011), 142–148.
57. 57 Fawcett, JA, An introduction to ROC Analysis. Pattern Recognition Letters 27 (2005), 861–874.
58. 58 Davis, J, Goadrich, M, The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning. ACM, 2006, 233–240.
59. 59 Chou, KC, Shen, HB, Recent progress in protein subcellular location prediction. Anal Biochem 370 (2007), 1–16.
60. 60 Sun, WJ, Li, JH, Liu, S, Wu, J, Zhou, H, Qu, LH, et al. RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data. Nucleic Acids Res 44:D1 (2016), D259–D265.
61. 61 Chou, KC, Shen, HB, Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Commun 357 (2007), 633–640.
62. 62 Fu, L, Niu, B, Zhu, Z, Wu, S, Li, W, CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28 (2012), 3150–3152.
63. 63 Chou, KC, Impacts of bioinformatics to medicinal chemistry. Med Chem 11 (2015), 218–234.
64. 64 Chou, KC, Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 43 (2001), 246–255.
65. 65 Chou, KC, Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21 (2005), 10–19.
66. 66 Du, P, Wang, X, Xu, C, Gao, Y, PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions. Anal Biochem 425 (2012), 117–119.
67. 67 Cao, DS, Xu, QS, Liang, YZ, propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics 29 (2013), 960–962.
68. 68 Lin, SX, Lapointe, J, Theoretical and experimental biology in one —A symposium in honour of Professor Kuo-Chen Chou's 50th anniversary and Professor Richard Giegé’s 40th anniversary of their scientific careers. J Biomedical Science and Engineering (JBiSE) 6 (2013), 435–442.
69. 69 Du, P, Gu, S, Jiao, Y, PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci 15 (2014), 3495–3506.
70. 70 Chen, W, Lei, TY, Jin, DC, Lin, H, Chou, KC, PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem 456 (2014), 53–60.
71. 71 Chen, W, Zhang, X, Brooker, J, Lin, H, Zhang, L, Chou, KC, PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31 (2015), 119–120.
72. 72 Liu, B, Liu, F, Fang, L, Wang, X, Chou, KC, repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics 31 (2015), 1307–1309.
73. 73 Liu, B, Liu, F, Fang, L, Wang, X, Chou, KC, repRNA: a web server for generating various feature vectors of RNA sequences. Mol Genet Genomics 291 (2016), 473–481.
74. 74 Chou, KC, Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics 6 (2009), 262–274.
75. 75 Chen, W, Lin, H, Chou, KC, Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol Biosyst 11 (2015), 2620–2634.
76. 76 Liu, B, Liu, F, Wang, X, Chen, J, Fang, L, Chou, KC, Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43:W1 (2015), W65–W71.
77. 77 Chou, KC, Zhang, CT, Diagrammatization of codon usage in 339 human immunodeficiency virus proteins and its biological implication. AIDS Res Hum Retroviruses 8 (1992), 1967–1976.
78. 78 Zhang, CT, Chou, KC, Analysis of codon usage in 1562 E. Coli protein coding sequences. J Mol Biol 238 (1994), 1–8.
79. 79 Golam Bari, ATM, Rokeya Reaz, M, Jeong, BS, DNA Encoding for Splice Site Prediction in Large DNA Sequence. MATCH Communications in Mathematical and in Computer Chemistry 71 (2014), 241–258.
80. 80 Guo, SH, Deng, EZ, Xu, LQ, Ding, H, Lin, H, Chen, W, et al. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30 (2014), 1522–1529.
81. 81 Liu, B, Fang, L, Wang, S, Wang, X, Li, H, Chou, KC, Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J Theor Biol 385 (2015), 153–159.
82. 82 Qiu, WR, Xiao, X, Chou, KC, iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 15 (2014), 1746–1766.
83. 83 Chou, KC, Cai, YD, Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 277 (2002), 45765–45769.
84. 84 Cai, YD, Zhou, GP, Chou, KC, Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J 84 (2003), 3257–3263.
85. 85 Cristianini, N, Shawe-Taylor, J., An Introduction of Support Vector Machines and Other Kernel-based Learning Methods, 2000, Cambridge University Press, Cambridge, UK.