Identification of real microRNA precursors with a pseudo structure status composition approach

PLoS ONE

Volumn 10, Issue 3, 2015, Pages

  Liu, Bin ^a,b   Fang, Longyun ^a   Liu, Fule ^a   Wang, Xiaolong ^a   Chen, Junjie ^a   Chou, Kuo Chen ^b,c  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b GORDON LIFE SCIENCE INSTITUTE   (United States)

^c KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA; AMINO ACID; NUCLEOTIDE; RNA PRECURSOR; SMALL UNTRANSLATED RNA;

ACCURACY; AMINO ACID COMPOSITION; ARTICLE; COMPARATIVE STUDY; COMPUTER PROGRAM; GENETIC DATABASE; HYDROGEN BOND; IMCRNA EXPSESSC DATABASE; IMCRNA PSESSC DATABASE; MATHEMATICAL MODEL; PREDICTION; PROCESS DEVELOPMENT; PROTEIN SECONDARY STRUCTURE; RNA ANALYSIS; RNA SEQUENCE; RNA STRUCTURE; VALIDATION PROCESS; BIOLOGY; CONFORMATION; GENETICS; HUMAN; HUMAN GENOME; NUCLEOTIDE SEQUENCE; PROCEDURES;

AMINO ACIDS; BASE SEQUENCE; COMPUTATIONAL BIOLOGY; GENOME, HUMAN; HUMANS; MICRORNAS; NUCLEIC ACID CONFORMATION; NUCLEOTIDES; RNA PRECURSORS; RNA, SMALL UNTRANSLATED;

EID: 84926631457     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0121501     Document Type: Article

References (90)

1. Lee Y, Kim M, Han J, Yeom K-H, Lee S, et al. (2004) MicroRNAgenes are transcribed byRNApolymerase II. EMBOJ 23: 4051-4060. PMID: 15372072
2. Cai X, Hagedorn CH, Cullen BR (2004) Human microRNAs are processed from capped, polyadenylated transcripts that can also function as mRNAs. RNA 10: 1957-1966. PMID: 15525708
3. Lee Y, Ahn C, Han J, Choi H, Kim J, et al. (2003) The nuclear RNase III Drosha initiates microRNA processing. Nature 425: 415-419. PMID: 14508493
4. Lund E, Guttinger S, Calado A, Dahlberg JE, Kutay U (2004) Nuclear export of microRNA precursors. Science 303: 95-98. PMID: 14631048
5. Yi R, Qin Y, Macara IG, Cullen BR (2003) Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 17: 3011-3016. PMID: 14681208
6. Bohnsack MT, Czaplinski K, Gorlich D (2004) Exportin 5 is a RanGTP-dependent dsRNA-binding protein that mediates nuclear export of pre-miRNAs. RNA 10: 185-191. PMID: 14730017
7. Bernstein E, Caudy AA, Hammond SM, Hannon GJ (2001) Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature 409: 363-366. PMID: 11201747
8. Grishok A, Pasquinelli AE, Conte D, Li N, Parrish S, et al. (2001) Genes and mechanisms related to RNA interference regulate expression of the small temporal RNAs that control C. elegans developmental timing. Cell 106: 23-24. PMID: 11461699
9. Hutvagner G, McLachlan J, Pasquinelli AE, Balint E, Tuschl T, et al. (2001) Acellular function for the RNA-interference enzyme Dicer in the maturation of the let-7 small temporal RNA. Science 293: 834-838. PMID: 11452083
10. Knight SW, Bass BL (2001) Arole for theRNaseIII enzymeDCR-1 in RNA interference and germ line development in Caenorhabditis elegans. Science 293: 2269-2271. PMID: 11486053
11. Nam JW, Shin K-R, Han J, Lee Y, Kim VN, et al. (2005) Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Research 33: 3570-3581. PMID: 15987789
12. Li L, Xu J, Yang D, Tan X, Wang H (2010) Computational approaches for microRNA studies: a review. Mamm Genome 21: 1-12. doi: 10.1007/s00335-009-9241-2 PMID: 20012966
13. Helvik SA, Snøve O, Sætrom P (2007) Reliable prediction of Drosha processing sites improves micro-RNA gene prediction. Bioinformatics 23: 142-149. PMID: 17105718
14. Huang TH, Fan B, Rothschild MF, Hu ZL, Li K, et al. (2007) MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans. BMC Bioinformatics 8: 341. PMID: 17868480
15. Wang Y, Chen X, Jiang W, Li L, Li W, et al. (2011) Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM. Genomics 98: 73-78. doi: 10.1016/j.ygeno.2011.04.011 PMID: 21586321
16. Xue C, Li F, He T, Liu G-P, Li Y, et al. (2005) Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 6: 310. PMID: 16381612
17. Wu Y, Wei B, Liu H, Li T, Rayner S (2011) MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 12: 107. doi: 10.1186/1471-2105-12-107 PMID: 21504621
18. Liu B, Zhang D, Xu R, Xu J, Wang X, et al. (2014) Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 30: 472-479. doi: 10.1093/bioinformatics/btt709 PMID: 24318998
19. Qiu WR, Xiao X (2014) iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 15: 1746-1766. doi: 10.3390/ijms15021746 PMID: 24469313
20. Guo SH, Deng EZ, Xu LQ, Ding H, Lin H, et al. (2014) iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30: 1522-1529. doi: 10.1093/bioinformatics/btu083 PMID: 24504871
21. Jiang P, Wu H, Wang W, Ma W, Sun X, et al. (2007) MiPred: classification of real and pseudo micro-RNA precursors using random forest prediction model with combined features. Nucleic acids research 35: W339-W344. PMID: 17553836
22. Kandaswamy KK, Chou KC, Martinetz T, Moller S, Suganthan PN, et al. (2011) AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. Journal of Theoretical Biology 270: 56-62. doi: 10.1016/j.jtbi.2010.10.037 PMID: 21056045
23. Lin WZ, Fang JA, Xiao X (2011) iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE 6: e24756. doi: 10.1371/journal.pone.0024756 PMID: 21935457
24. Agarwal S, Vaz C, Bhattacharya A, Srinivasan A (2010) Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM). BMC Boinformatics 11: S29.
25. Chou KC, Shen HB (2007) Review: Recent progresses in protein subcellular location prediction. Analytical Biochemistry 370: 1-16. PMID: 17698024
26. Yousef M, Nebozhyn M, Shatkay H, Kanterakis S, Showe L, et al. (2006) Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier. Bioinformatics 22: 1325-1334. PMID: 16543277
27. Brameier M, Wiuf C (2007) Ab initio identification of human microRNAs based on structure motifs. BMC Bioinformatics 8: 478. PMID: 18088431
28. Sewer A, Paul N, Landgraf P, Aravin A, Pfeffer S, et al. (2005) Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics 6: 267. PMID: 16274478
29. Huang C, Zhang R, Chen Z, Jiang Y, Shang Z, et al. (2010) Predict potential drug targets from the ion channel proteins based on SVM. Journal of Theoretical Biology 262: 750-756. doi: 10.1016/j.jtbi.2009.11.002 PMID: 19903486
30. Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60) 43: 246-255. PMID: 11288174
31. Chou KC (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21: 10-19. PMID: 15308540
32. Lin SX, Lapointe J (2013) Theoretical and experimental biology in one. J Biomedical Science and Engineering 6: 435-442.
33. Georgiou DN, Karakasidis TE, Nieto JJ, Torres A (2009) Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. Journal of Theoretical Biology 257: 17-26. doi: 10.1016/j.jtbi.2008.11.003 PMID: 19056401
34. Esmaeili M, Mohabatkar H, Mohsenzadeh S (2010) Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. Journal of Theoretical Biology 263: 203-209. doi: 10.1016/j.jtbi.2009.11.016 PMID: 19961864
35. Mohabatkar H (2010) Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein & Peptide Letters 17: 1207-1214. PMID: 20450487
36. Sahu SS, Panda G (2010) A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction. Computational Biology and Chemistry 34: 320-327. doi: 10.1016/j.compbiolchem.2010.09.002 PMID: 21106461
37. Mohammad Beigi M, Behjati M, Mohabatkar H (2011) Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach. Journal of Structural and Functional Genomics 12: 191-197. doi: 10.1007/s10969-011-9120-4 PMID: 22143437
38. Nanni L, Lumini A, Gupta D, Garg A (2012) Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou's pseudo amino acid composition and on evolutionary information. IEEE/ACM Trans Comput Biol Bioinform 9: 467-475. doi: 10.1109/TCBB.2011.117 PMID: 21860064
39. Gupta MK, Niyogi R, Misra M (2013) An alignment-free method to find similarity among protein sequences via the general form of Chou's pseudo amino acid composition. SAR QSAR Environ Res (SAR AND QSAR IN ENVIRONMENTAL RESEARCH) 24: 597-609. doi: 10.1080/1062936X.2013.773378 PMID: 23710804
40. Chen YK, Li KB (2013) Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition. Journal of Theoretical Biology 318: 1-12. doi: 10.1016/j.jtbi.2012.10.033 PMID: 23137835
41. Hajisharifi Z, Piryaiee M, Mohammad Beigi M, Behbahani M, Mohabatkar H (2014) Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test. Journal of Theoretical Biology 341: 34-40. doi: 10.1016/j.jtbi.2013.08.037 PMID: 24035842
42. Mohabatkar H, Beigi MM, Abdolahi K, Mohsenzadeh S (2013) Prediction of Allergenic Proteins by Means of the Concept of Chou's Pseudo Amino Acid Composition and a Machine Learning Approach. Medicinal Chemistry 9: 133-137. PMID: 22931491
43. Xu R, Zhou J, Liu B, He YA, Zou Q, et al. (2014) Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. Journal of Biomolecular Structure & Dynamics (JBSD).
44. Liu B, Xu J, Fan S, Xu R, Jiyun Zhou J, et al. (2015) PseDNA-Pro: DNA-binding protein identification by combining Chou's PseAAC and physicochemical distance transformation. Molecular Informatics 34: 8-17
45. Du P, Gu S, Jiao Y (2014) PseAAC-General: Fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. International Journal of Molecular Sciences 15: 3495-3506. doi: 10.3390/ijms15033495 PMID: 24577312
46. Zhong WZ, Zhou SF (2014) Molecular science for drug development and biomedicine. Intenational Journal of Molecular Sciences 15: 20072-20078. doi: 10.3390/ijms151120072 PMID: 25375190
47. Chen W, Lei TY, Jin DC, Lin H (2014) PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition. Analytical Biochemistry 456: 53-60. doi: 10.1016/j.ab.2014.04.001 PMID: 24732113
48. Liu B, Liu F, Fang L, Wang X (2014) 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:.
49. Chen W, Zhang X, Brooker J, Lin H, Zhang L, et al. (2015) PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31: 119-120. doi: 10.1093/bioinformatics/btu602 PMID: 25231908
50. Chen W, Feng PM, Lin H, Chou KC (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition Nucleic Acids Research 41: e68. doi: 10.1093/nar/gks1450 PMID: 23303794
51. Lin H, Deng EZ, Ding H, Chen W, Chou KC (2014) iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Research 42: 12961-12972. doi: 10.1093/nar/gku1019 PMID: 25361964
52. Liu Z, Xiao X, Qiu WR (2015) iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Analytical Biochemistry.
53. Chou KC (2015) Impacts of bioinformatics to medicinal chemistry. Medicinal Chemistry.
54. Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). Journal of Theoretical Biology 273: 236-247. doi: 10.1016/j.jtbi.2010.12.024 PMID: 21168420
55. Xu Y, Ding J, Wu LY, Chou KC (2013) iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition PLoS ONE 8: e55844. doi: 10.1371/journal.pone.0055844 PMID: 23409062
56. Fan YN, Xiao X, Min JL (2014) iNR-Drug: Predicting the interaction of drugs with nuclear receptors in cellular networking. Intenational Journal of Molecular Sciences 15: 4915-4937. doi: 10.3390/ijms15034915 PMID: 24651462
57. Xu Y, Wen X, Shao XJ, Deng NY (2014) iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. International Journal of Molecular Sciences (IJMS) 15: 7594-7610. doi: 10.3390/ijms15057594 PMID: 24857907
58. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic acids research 39: D152-D157. doi: 10.1093/nar/gkq1027 PMID: 21037258
59. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, et al. (2003) A uniform system for microRNA annotation. RNA 9: 277-279. PMID: 12592000
60. Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22: 1658-1659. PMID: 16731699
61. Hofacker IL (2003) Vienna RNA secondary structure server. Nucleic acids research 31: 3429-3431. PMID: 12824340
62. Bonnet E, Wuyts J, Rouzé P, Van de Peer Y (2004) Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 20: 2911-2917. PMID: 15217813
63. Vapnik V (1998) Statistical Learning Theory: Wiley-Interscience, New York.
64. Chang C, Lin CJ (2009) LIBSVM - A Library for Support Vector Machines. Available: http://wwwcsientuedutw/~cjlin/libsvm/.
65. Chou KC, Cai YD (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. Journal of Biological Chemistry 277: 45765-45769. PMID: 12186861
66. Cristianini N, Shawe-Taylor J (2000) An introduction of Support Vector Machines and other kernel-based learning methodds. Cambridge, UK: Cambridge University Press.
67. Zhou GP, Doctor K (2003) Subcellular location prediction of apoptosis proteins. PROTEINS: Structure, Function, and Genetics 50: 44-48. PMID: 12471598
68. Chen W, Lin H, Feng PM, Ding C, Zuo YC, et al. (2012) iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE 7: e47843. doi: 10.1371/journal.pone.0047843 PMID: 23144709
69. Kong L, Zhang L, Lv J (2014) Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition. J Theor Biol 344: 12-18. doi: 10.1016/j.jtbi.2013.11.021 PMID: 24316044
70. Zakeri P, Moshiri B, Sadeghi M (2011) Prediction of protein submitochondria locations based on data fusion of various features of sequences. Journal of Theoretical Biology 269: 208-216. doi: 10.1016/j.jtbi.2010.10.026 PMID: 21040732
71. Hayat M, Khan A (2011) Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. Journal of Theoretical Biology 271: 10-17. doi: 10.1016/j.jtbi.2010.11.017 PMID: 21110985
72. Dehzangi A, Heffernan R, Sharma A, Lyons J, Paliwal K, et al. (2015) Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC. Journal of Theoretical Biology 364: 284-294. doi: 10.1016/j.jtbi.2014.09.029 PMID: 25264267
73. Chou KC (2001) Using subsite coupling to predict signal peptides. Protein Engineering 14: 75-79. PMID: 11297664
74. Chou KC (2013) Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems 9: 1092-1100. doi: 10.1039/c3mb25555g PMID: 23536215
75. Chou KC (1999) A key driving force in determination of protein structural classes. Biochemical and Biophysical Research Communications (BBRC) 264: 216-224. PMID: 10527868
76. Wang T, Yang J, Shen HB (2008) Predicting membrane protein types by the LLDA algorithm. Protein & Peptide Letters 15: 915-921.
77. Chou KC, Forsen S (1980) Graphical rules for enzyme-catalyzed rate laws. Biochemical Journal 187: 829-835. PMID: 7188428
78. Zhou GP, Deng MH (1984) An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochemical Journal 222: 169-176. PMID: 6477507
79. Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Chou KC, et al. (1993) Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. Journal of Biological Chemistry 268: 6119-6124. PMID: 7681060
80. Chou KC (2010) Graphic rule for drug metabolism systems. Current Drug Metabolism 11: 369-378. PMID: 20446902
81. Wolfram S (1984) Cellular automation as models of complexity. Nature 311: 419-424.
82. Xiao X, Shao SH (2006) A probability cellular automaton model for hepatitis B viral infections. Biochem Biophys Res Comm (BBRC) 342: 605-610.
83. Xiao X, Shao S, Ding Y, Huang Z, Chen X, et al. (2005) An Application of Gene Comparative Image for Predicting the Effect on Replication Ratio by HBV Virus Gene Missense Mutation. Journal of Theoretical Biology 235: 555-565. PMID: 15935173
84. Chou KC, Zhang CT, Maggiora GM (1997) Disposition of amphiphilic helices in heteropolar environments. PROTEINS: Structure, Function, and Genetics 28: 99-108. PMID: 9144795
85. Chou KC, Lin WZ, Xiao X (2011) Wenxiang: a web-server for drawing wenxiang diagrams Natural Science 3: 862-865.
86. Zhou GP (2011) The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. Journal of Theoretical Biology 284: 142-148. doi: 10.1016/j.jtbi.2011.06.006 PMID: 21718705
87. Zhou GP, Huang RB (2013) The pH-Triggered Conversion of the PrP(c) to PrP(sc.). Curr Top Med Chem 13: 1152-1163. PMID: 23647538
88. Wilkinson L, Friendly M (2009) The history of the cluster heat map. The American Statistician 63: 179-184.
89. Liu B, Xu J, Lan X, Xu R, Zhou J, et al. (2014) iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS ONE 9: e106691. doi: 10.1371/journal.pone.0106691 PMID: 25184541
90. Lee JJ, Kim HJ, Kim YJ, Lee S, Hwang JY, et al. (2004) Imatinib induces a cytogenetic response in blast crisis or interferon failure chronic myeloid leukemia patients with e19a2 BCR-ABL transcripts. Leukemia 18: 1539-1540. PMID: 15284852