Computational prediction of the localization of microRNAs within their pre-miRNA

Nucleic Acids Research

Volumn 41, Issue 15, 2013, Pages 7200-7211

  Leclercq, Mickael ^a   Diallo, Abdoulaye Banire ^b   Blanchette, Mathieu ^a  

^a MCGILL UNIVERSITY   (Canada)

^b UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA; RNA PRECURSOR; TRANSCRIPTOME;

ACCURACY; ALGORITHM; ARTICLE; CLASSIFIER; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MIRDUP CLASSIFIER; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; RNA ANALYSIS; RNA HAIRPIN; RNA SEQUENCE; RNA STABILITY; RNA STRUCTURE;

ANIMALS; COMPUTATIONAL BIOLOGY; INTERNET; INVERTED REPEAT SEQUENCES; MICRORNAS; NUCLEIC ACID CONFORMATION; PLANTS; RNA PRECURSORS; RNA, PLANT; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 84883477922     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkt466     Document Type: Article

References (90)

1. Ambros,V. (1989) A hierarchy of regulatory genes controls a larva-to-adult developmental switch in C. elegans. Cell, 57, 49-57
2. Ruvkun,G. (2001) Molecular biology. Glimpses of a tiny RNA world. Science, 294, 797-799
3. Swami,M. (2010) Small RNAs: an epigenetic silencing influence. Nat. Rev. Genet., 11, 172-173
4. Lee,Y., Kim,M., Han,J., Yeom,K.H., Lee,S., Baek,S.H. and Kim,V.N. (2004) MicroRNA genes are transcribed by RNA polymerase II. EMBO J., 23, 4051-4060
5. Cuperus,J.T., Fahlgren,N. and Carrington,J.C. (2011) Evolution and functional diversification of MIRNA genes. Plant Cell, 23, 431-442
6. Friedlander,M.R., Chen,W., Adamidi,C., Maaskola,J., Einspanier,R., Knespel,S. and Rajewsky,N. (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat. Biotechnol., 26, 407-415
7. Schwarz,D.S., Hutvagner,G., Du,T., Xu,Z., Aronin,N. and Zamore,P.D. (2003) Asymmetry in the assembly of the RNAi enzyme complex. Cell, 115, 199-208
8. Lagos-Quintana,M., Rauhut,R., Yalcin,A., Meyer,J., Lendeckel,W. and Tuschl,T. (2002) Identification of tissue-specific microRNAs from mouse. Curr. Biol., 12, 735-759
9. Lim,L.P., Lau,N.C., Garrett-Engele,P., Grimson,A., Schelter,J.M., Castle,J., Bartel,D.P., Linsley,P.S. and Johnson,J.M. (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature, 433, 769-773
10. Carrington,J.C. and Ambros,V. (2003) Role of microRNAs in plant and animal development : developmental timing. Science, 301, 336-338
11. Cuellar,T.L. and McManus,M.T. (2005) MicroRNAs and endocrine biology. J. Endocrinol., 187, 327-332
12. Suh,M.R., Lee,Y., Kim,J.Y., Kim,S.K., Moon,S.H., Lee,J.Y., Cha,K.Y., Chung,H.M., Yoon,H.S., Moon,S.Y. et al. (2004) Human embryonic stem cells express a unique set of microRNAs. Dev. Biol., 270, 488-498
13. Williams,A.H., Liu,N., van Rooij,E. and Olson,E.N. (2009) MicroRNA control of muscle development and disease. Curr. Opin. Cell Biol., 21, 461-469
14. Chen,J.F., Mandel,E.M., Thomson,J.M., Wu,Q., Callis,T.E., Hammond,S.M., Conlon,F.L. and Wang,D.Z. (2006) The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat. Genet., 38, 228-233
15. Shivdasani,R.A. (2006) MicroRNAs: regulators of gene expression and cell differentiation. Blood, 108, 3646-3653
16. Ambros,V. (2004) The functions of animal microRNAs. Nature, 431, 350-355
17. Brennecke,J., Hipfner,D.R., Stark,A., Russell,R.B. and Cohen,S.M. (2003) Bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell, 113, 25-36
18. Poy,M.N., Eliasson,L., Krutzfeldt,J., Kuwajima,S., Ma,X., Macdonald,P.E., Pfeffer,S., Tuschl,T., Rajewsky,N., Rorsman,P. et al. (2004) A pancreatic islet-specific microRNA regulates insulin secretion. Nature, 432, 226-230
19. Wilfred,B.R., Wang,W.X. and Nelson,P.T. (2007) Energizing miRNA research: A review of the role of miRNAs in lipid metabolism, with a prediction that miR-103/107 regulates human metabolic pathways. Mol. Genet. Metab., 91, 209-217
20. Miska,E.A., Alvarez-Saavedra,E., Abbott,A.L., Lau,N.C., Hellman,A.B., McGonagle,S.M., Bartel,D.P., Ambros,V.R. and Horvitz,H.R. (2007) Most Caenorhabditis elegans microRNAs are individually not essential for development or viability. PLoS Genet., 3, 2395-2403
21. Clop,A., Marcq,F., Takeda,H., Pirottin,D., Tordoir,X., Bibe,B., Bouix,J., Caiment,F., Elsen,J.M., Eychenne,F. et al. (2006) A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep. Nat. Genet., 38, 813-818
22. Castanotto,D. and Rossi,J.J. (2009) The promises and pitfalls of RNA-interference-based therapeutics. Nature, 457, 426-433
23. Cooper,T.A., Wan,L. and Dreyfuss,G. (2009) RNA and disease. Cell, 136, 777-793
24. Murchison,E.P., Stein,P., Xuan,Z., Pan,H., Zhang,M.Q., Schultz,R.M. and Hannon,G.J. (2007) Critical roles for Dicer in the female germline. Genes Dev., 21, 682-693
25. Osada,H. and Takahashi,T. (2007) MicroRNAs in biological processes and carcinogenesis. Carcinogenesis, 28, 2-12
26. Fujii,H., Chiou,T.J., Lin,S.I., Aung,K. and Zhu,J.K. (2005) A miRNA involved in phosphate-starvation response in Arabidopsis. Curr. Biol., 15, 2038-2043
27. Guy,C.L. (1990) Cold accelimation and freezing stress tolerance: role of protein metabolism. Annu. Rev. Plant Biol., 41, 187-223
28. Jones-Rhoades,M.W. and Bartel,D.P. (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol. Cell, 14, 787-799
29. Saqib,M., Zorb,C. and Schubert,S. (2008) Silicon-mediated improvement in the salt resistance of wheat (Triticum aestivum) results from increased sodium exclusion and resistance to oxidative stress. Funct. Plant Biol., 35, 633-639
30. Sunkar,R., Kapoor,A. and Zhu,J.K. (2006) Posttranscriptional induction of two Cu/Zn superoxide dismutase genes in Arabidopsis is mediated by downregulation of miR398 and important for oxidative stress tolerance. Plant Cell, 18, 2051-2065
31. Van Rooij,E., Sutherland,L.B., Liu,N., Williams,A.H., McAnally,J., Gerard,R.D., Richardson,J.A. and Olson,E.N. (2006) A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proc. Natl Acad. Sci. USA, 103, 18255-18260
32. Nelson,J.A. (2007) Small RNAs and large DNA viruses. N. Engl. J. Med., 357, 2630-2632
33. Pfeffer,S., Sewer,A., Lagos-Quintana,M., Sheridan,R., Sander,C., Grasser,F.A., van Dyk,L.F., Ho,C.K., Shuman,S., Chien,M. et al. (2005) Identification of microRNAs of the herpesvirus family. Nat. Methods, 2, 269-276
34. Sarnow,P., Jopling,C.L., Norman,K.L., Schutz,S. and Wehner,K.A. (2006) MicroRNAs: expression, avoidance and subversion by vertebrate viruses. Nat. Rev. Microbiol., 4, 651-659
35. Berezikov,E., Cuppen,E. and Plasterk,R.H. (2006) Approaches to microRNA discovery. Nat. Genet., 38(Suppl.), S2-S7
36. Li,L., Xu,J., Yang,D., Tan,X. and Wang,H. (2010) Computational approaches for microRNA studies: a review. Mamm. Genome, 21, 1-12
37. Schulte,J.H., Marschall,T., Martin,M., Rosenstiel,P., Mestdagh,P., Schlierf,S., Thor,T., Vandesompele,J., Eggert,A., Schreiber,S. et al. (2010) Deep sequencing reveals differential expression of microRNAs in favorable versus unfavorable neuroblastoma. Nucleic Acids Res., 38, 5919-5928
38. Sunkar,R., Zhou,X., Zheng,Y., Zhang,W. and Zhu,J.K. (2008) Identification of novel and candidate miRNAs in rice by high throughput sequencing. BMC Plant Biol., 8, 25
39. Grey,F., Antoniewicz,A., Allen,E., Saugstad,J., McShea,A., Carrington,J.C. and Nelson,J. (2005) Identification and characterization of human cytomegalovirus-encoded microRNAs. J. Virol., 79, 12095-12099
40. Nam,J.W., Shin,K.R., Han,J.J., Lee,Y., Kim,V.N. and Zhang,B.T. (2005) Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res., 33, 3570-3581
41. Nam,J.W., Kim,J., Kim,S.K. and Zhang,B.T. (2006) ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res., 34, W455-W458
42. Xue,C.H., Li,F., He,T., Liu,G.P., Li,Y.D. and Zhang,X.G. (2005) Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 6, 310-317
43. Sewer,A., Paul,N., Landgraf,P., Aravin,A., Pfeffer,S., Brownstein,M.J., Tuschl,T., van Nimwegen,E. and Zavolan,M. (2005) Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics, 6, 267
44. Jiang,P., Wu,H., Wang,W., Ma,W., Sun,X. and Lu,Z. (2007) MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res., 35, W339-W344
45. Oulas,A., Boutla,A., Gkirtzou,K., Reczko,M., Kalantidis,K. and Poirazi,P. (2009) Prediction of novel microRNA genes in cancerassociated genomic regionsa combined computational and experimental approach. Nucleic Acids Res., 37, 3276-3287
46. Batuwita,R. and Palade,V. (2009) microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics, 25, 989-995
47. Kadri,S., Hinman,V. and Benos,P.V. (2009) HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models. BMC Bioinformatics, 10, S35
48. Thieme,C.J., Gramzow,L., Lobbes,D. and TheiBen,G. (2011) SplamiR: prediction of spliced miRNAs in plants. Bioinformatics, 27, 1215-1223
49. Bonnet,E., Wuyts,J., Rouze,P. and Van de Peer,Y. (2004) Detection of 91 potential conserved plant microRNAs in Arabidopsis thaliana and Oryza sativa identifies important target genes. Proc. Natl Acad. Sci. USA, 101, 11511-11516
50. Ding,J.D., Zhou,S.G. and Guan,J.H. (2010) MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features. BMC Bioinformatics, 11, S11
51. Griffiths-Jones,S., Grocock,R.J., van Dongen,S., Bateman,A. and Enright,A.J. (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res., 34, D140-D144
52. Griffiths-Jones,S., Saini,H.K., van Dongen,S. and Enright,A.J. (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res., 36, D154-D158
53. Lindow,M., Jacobsen,A., Nygaard,S., Mang,Y. and Krogh,A. (2007) Intragenomic matching reveals a huge potential for miRNA-mediated regulation in plants. PLoS Comput. Biol., 3, e238
54. Friedlander,M.R., Mackowiak,S.D., Li,N., Chen,W. and Rajewsky,N. (2012) miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res., 40, 37-52
55. An,J., Lai,J., Lehman,M.L. and Nelson,C.C. (2013) miRDeep: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res., 41, 727-737
56. Mathelier,A. and Carbone,A. (2010) MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics, 26, 2226-2234
57. Hackenberg,M., Rodriguez-Ezpeleta,N. and Aransay,A.M. (2011) miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res., 39, W132-W138
58. Hackenberg,M., Sturm,M., Langenberger,D., Falcon-Perez,J.M. and Aransay,A.M. (2009) miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res., 37, W68-W76
59. Fahlgren,N., Jogdeo,S., Kasschau,K.D., Sullivan,C.M., Chapman,E.J., Laubinger,S., Smith,L.M., Dasenko,M., Givan,S.A., Weigel,D. et al. (2010) MicroRNA gene evolution in Arabidopsis lyrata and Arabidopsis thaliana. Plant cell, 22, 1074-1089
60. Breakfield,N.W., Corcoran,D.L., Petricka,J.J., Shen,J., Sae-Seaw,J., Rubio-Somoza,I., Weigel,D., Ohler,U. and Benfey,P.N. (2012) High-resolution experimental and computational profiling of tissue-specific known and novel miRNAs in Arabidopsis. Genome Res., 22, 163-176
61. Wang,X., Zhang,J., Li,F., Gu,J., He,T., Zhang,X. and Li,Y. (2005) MicroRNA identification based on sequence and structure alignment. Bioinformatics, 21, 3610-3614
62. Gkirtzou,K., Tsamardinos,I., Tsakalides,P. and Poirazi,P. (2010) MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One, 5, e11843
63. Xuan,P., Guo,M.Z., Huang,Y.C., Li,W.B. and Huang,Y.F. (2011) MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs. PLoS One, 6, e27422
64. He,C., Li,Y.X., Zhang,G., Gu,Z., Yang,R., Li,J., Lu,Z.J., Zhou,Z.H., Zhang,C. and Wang,J. (2012) MiRmat: mature microRNA sequence prediction. PLoS One, 7, e51673
65. Yang,J.S., Phillips,M.D., Betel,D., Mu,P., Ventura,A., Siepel,A.C., Chen,K.C. and Lai,E.C. (2011) Widespread regulatory activity of vertebrate microRNA species. RNA, 17, 312-326
66. Hofacker,I.L., Fontana,W., Stadler,P.F., Bonhoeffer,L.S., Tacker,M. and Schuster,P. (1994) Fast folding and comparison of RNA secondary structures. Monatsh. Chem., 125, 167-188
67. Hall,M., Frank,E., Holmes,G., Pfahringer,B., Reutemann,P. and Witten,I.H. (2009) The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl., 11, 10-18
68. Dash,M. and Liu,H. (1997) Feature selection for classification. Intell. Data Anal., 1, 131-156
69. Hong,S.J. (1997) Use of contextual information for feature ranking and discretization. IEEE Trans. Knowl. Data Eng., 9, 718-730
70. Robnik-Sikonja,M. and Kononenko,I. (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn., 53, 23-69
71. Quinlan,J.R. (1986) Induction of decision trees. Mach. Learn., 1, 81-106
72. Shannon,C.E., Weaver,W., Blahut,R.E. and Hajek,B. (1949) The Mathematical Theory of Communication. Univ of Illinois Pr, USA, p
73. Breiman,L. (2001) Random forests. Mach. Learn., 45, 5-32
74. Freund,Y. and Schapire,R. (1997) A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139
75. Osman,I.H. and Kelly,J.P. (1996) Meta-heuristics: Theory and Applications. Springer, Germany
76. Cortes,C. and Vapnik,V. (1995) Support-vector networks. Mach. Learn., 20, 273-297
77. Chang,C.C. and Lin,C.J. (2001) LIBSVM: A Library for Support Vector Machines. [Computer Program]. ACM Transactions on Intelligent Systems and Technology (TIST), USA
78. Quinlan,J.R. (1993) C4.5: Programs for Machine Learning, Vol Morgan kaufmann, USA
79. Matthews,B.W. (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 405, 442-451
80. Zhou,J., Foster,D.P., Stine,R.A. and Ungar,L.H. (2006) Streamwise feature selection. J. Mach. Learn. Res., 7, 1861-1885
81. Robnik-Sikonja,M. (2004) Improving random forests. Lect. Notes Comput. Sc., 3201, 359-370
82. Guyon,I., Weston,J., Barnhill,S. and Vapnik,V. (2002) Gene selection for cancer classification using support vector machines. Mach. Learn., 46, 389-422
83. Zhang,B.H., Pan,X.P., Cox,S.B., Cobb,G.P. and Anderson,T.A. (2006) Evidence that miRNAs are different from other RNAs. Cell. Mol. Life Sci., 63, 246-254
84. Cohen,W.W. (1995) Proceedings of the Twelfth International Conference on Machine Learning. Lake Tahoe, California
85. Collobert,R. and Bengio,S. (2004) Proceedings of the twenty-first international conference on Machine learning. ACM, USA, p
86. Le Cessie,S. and Van Houwelingen,J. (1992) Ridge estimators in logistic regression. Appl. Stat., 191-201
87. Siepel,A., Bejerano,G., Pedersen,J.S., Hinrichs,A.S., Hou,M., Rosenbloom,K., Clawson,H., Spieth,J., Hillier,L.W., Richards,S. et al. (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res., 15, 1034-1050
88. Krol,J., Sobczak,K., Wilczynska,U., Drath,M., Jasinska,A., Kaczynska,D. and Krzyzosiak,W.J. (2004) Structural features of MicroRNA (miRNA) precursors and their relevance to miRNA biogenesis and small interfering RNA/Short hairpin RNA design. J. Biol. Chem., 279, 42230-42239
89. Mendes,N.D., Freitas,A.T. and Sagot,M.F. (2009) Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res., 37, 2419-2433
90. Park,J.E., Heo,I., Tian,Y., Simanshu,D.K., Chang,H., Jee,D., Patel,D.J. and Kim,V.N. (2011) Dicer recognizes the 50 end of RNA for efficient and accurate processing. Nature, 475, 201-205.