Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure

BMC Bioinformatics

Volumn 12, Issue , 2011, Pages

  Xiao, Jiamin ^a   Tang, Xiaojing ^a   Li, Yizhou ^a   Fang, Zheng ^a   Ma, Daichuan ^a   He, Yangzhige ^a   Li, Menglong ^a  

^a SICHUAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION EFFICIENCY; FEATURE EXTRACTION METHODS; FEATURE INFORMATION; POST-TRANSCRIPTIONAL; RECEIVER OPERATING CURVES; REPRESENTATION METHOD; SECONDARY STRUCTURES; STEM-LOOP STRUCTURES;

DECISION TREES; FEATURE EXTRACTION; MATHEMATICAL MODELS;

RNA;

MESSENGER RNA; MICRORNA; RNA PRECURSOR;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; CONFORMATION; GENETICS; HUMAN; ISOLATION AND PURIFICATION; METABOLISM; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; HUMANS; MICRORNAS; MODELS, STATISTICAL; NUCLEIC ACID CONFORMATION; RNA PRECURSORS; RNA, MESSENGER;

EID: 79955948344     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-165     Document Type: Article

References (43)

1. Lee Y, Ahn C, Han JJ, Choi H, Kim J, Yim J, Lee J, Provost P, Radmark O, Kim S, Kim VN. The nuclear RNase III Drosha initiates microRNA processing. Nature 2003, 425(6956):415-419. 10.1038/nature01957, 14508493.
2. Bernstein E, Caudy AA, Hammond SM, Hannon GJ. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature 2001, 409(6818):363-366. 10.1038/35053110, 11201747.
3. Hammond SM, Boettcher S, Caudy AA, Kobayashi R, Hannon GJ. Argonaute2, a link between genetic and biochemical analyses of RNAi. Science 2001, 293(5532):1146-1150. 10.1126/science.1064023, 11498593.
4. Hutvagner G, Zamore PD. A microRNA in a multiple-turnover RNAi enzyme complex. Science 2002, 297(5589):2056-2060. 10.1126/science.1073827, 12154197.
5. Vaucheret H, Vazquez F, Crete P, Bartel DP. The action of ARGONAUTE1 in the miRNA pathway and its regulation by the miRNA pathway are crucial for plant development. Gene Deve 2004, 18(10):1187-1197.
6. Lee RC, Feinbaum RL, Ambros V. The C-elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75(5):843-854. 10.1016/0092-8674(93)90529-Y, 8252621.
7. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000, 403(6772):901-906. 10.1038/35002607, 10706289.
8. Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T. Identification of novel genes coding for small expressed RNAs. Science 2001, 294(5543):853-858. 10.1126/science.1064921, 11679670.
9. Lau NC, Lim LP, Weinstein EG, Bartel DP. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 2001, 294(5543):858-862. 10.1126/science.1065062, 11679671.
10. Lee RC, Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science 2001, 294(5543):862-864. 10.1126/science.1065329, 11679672.
11. Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP. MicroRNAs in plants (vol 16, pg 1616, 2002). Gene Deve 2002, 16(17):2313-2313.
12. Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, Bartel DP. Prediction of plant microRNA targets. Cell 2002, 110(4):513-520. 10.1016/S0092-8674(02)00863-2, 12202040.
13. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res 2008, 36:D154-D158. 10.1093/nar/gkn221, 2238936, 17991681.
14. Xue CH, Li F, He T, Liu GP, Li YD, Zhang XG. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. Bmc Bioinformatics 2005, 6:301. 10.1186/1471-2105-6-301, 1351271, 16354298.
15. Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007, 35:W339-W344. 10.1093/nar/gkm368, 1933124, 17553836.
16. Batuwita R, Palade V. microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 2009, 25(8):989-995. 10.1093/bioinformatics/btp107, 19233894.
17. Ng KLS, Mishra SK. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics 2007, 23(11):1321-1330. 10.1093/bioinformatics/btm026, 17267435.
18. Sewer A, Paul N, Landgraf P, Aravin A, Pfeffer S, Brownstein MJ, Tuschl T, van Nimwegen E, Zavolan M. Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics 2005, 6:267. 10.1186/1471-2105-6-267, 1315341, 16274478.
19. Mathews DH, Turner DH. Prediction of RNA secondary structure by free energy minimization. Curr Opin Struct Biol 2006, 16(3):270-278. 10.1016/j.sbi.2006.05.010, 16713706.
20. Kim VN. MicroRNA precursors in motion: exportin-5 mediates their nuclear export. Trends Cell Biol 2004, 14(4):156-159. 10.1016/j.tcb.2004.02.006, 15134074.
21. Zeng Y, Cullen BR. Structural requirements for pre-microRNA binding and nuclear export by Exportin 5. Nucleic Acids Res 2004, 32(16):4776-4785. 10.1093/nar/gkh824, 519115, 15356295.
22. Zhang HD, Kolb FA, Jaskiewicz L, Westhof E, Filipowicz W. Single processing center models for human dicer and bacterial RNase III. Cell 2004, 118(1):57-68. 10.1016/j.cell.2004.06.017, 15242644.
23. Mironov AS, Gusarov I, Rafikov R, Lopez LE, Shatalin K, Kreneva RA, Perumov DA, Nudler E. Sensing small molecules by nascent RNA: A mechanism to control transcription in bacteria. Cell 2002, 111(5):747-756. 10.1016/S0092-8674(02)01134-0, 12464185.
24. Nahvi A, Sudarsan N, Ebert MS, Zou X, Brown KL, Breaker RR. Genetic control by a metabolite binding mRNA. Chem Biol 2002, 9(9):1043-1049. 10.1016/S1074-5521(02)00224-7, 12323379.
25. Schilling O, Langbein I, Muller M, Schmalisch MH, Stulke J. A protein-dependent riboswitch controlling ptsGHI operon expression in Bacillus subtilis: RNA structure rather than sequence provides interaction specificity. Nucleic Acids Res 2004, 32(9):2853-2864. 10.1093/nar/gkh611, 419612, 15155854.
26. Winkler W, Nahvi A, Breaker RR. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature 2002, 419(6910):952-956. 10.1038/nature01145, 12410317.
27. Winkler WC, Cohen-Chalamish S, Breaker RR. An mRNA structure that controls gene expression by binding FMN. Proc Natl Acad Sci USA 2002, 99(25):15908-15913. 10.1073/pnas.212628899, 138538, 12456892.
28. Brouns SJJ, Jore MM, Lundgren M, Westra ER, Slijkhuis RJH, Snijders APL, Dickman MJ, Makarova KS, Koonin EV, van der Oost J. Small CRISPR RNAs guide antiviral defense in prokaryotes. Science 2008, 321(5891):960-964. 10.1126/science.1159689, 18703739.
29. Kurihara Y, Matsui A, Kawashima M, Kaminuma E, Ishida J, Morosawa T, Mochizuki Y, Kobayashi N, Toyoda T, Shinozaki K, Seki M. Identification of the candidate genes regulated by RNA-directed DNA methylation in Arabidopsis. Biochem Biophys Res Commun 2008, 376(3):553-557. 10.1016/j.bbrc.2008.09.046, 18805399.
30. Nakashima A, Takaku H, Shibata HS, Negishi Y, Takagi M, Tamura M, Nashimoto M. Gene silencing by the tRNA maturase tRNase Z(L) under the direction of small-guide RNA. Gene Ther 2007, 14(1):78-85. 10.1038/sj.gt.3302841, 16885998.
31. Li J, Min R, Bonner A, Zhang Z. A probabilistic framework to improve microRNA target prediction by incorporating proteomics data. J Bioinf Comput Biol 2009, 7(6):955-972.
32. Xie FL, Zhang BH. Target-align: a tool for plant microRNA target identification. Bioinformatics 26(23):3002-3003.
33. Le Brigand K, Robbe-Sermesant K, Mari B, Barbry P. MiRonTop: mining microRNAs targets across large scale gene expression studies. Bioinformatics 26(24):3131-3132.
34. Fera D, Kim N, Shiffeldrim N, Zorn J, Laserson U, Gan HH, Schlick T. RAG: RNA-As-Graphs web resource. BMC Bioinformatics 2004, 5:88. 10.1186/1471-2105-5-88, 471545, 15238163.
35. Gan HH, Fera D, Zorn J, Shiffeldrim N, Tang M, Laserson U, Kim N, Schlick T. RAG: RNA-As-Graphs database - concepts, analysis, and features. Bioinformatics 2004, 20(8):1285-1291. 10.1093/bioinformatics/bth084, 14962931.
36. Xiao JM, Li YZ, Wang KL, Wen ZN, Li ML, Zhang LF, Guang XM. In silico method for systematic analysis of feature importance in microRNA-mRNA interactions. BMC Bioinformatics 2009, 10:427. 10.1186/1471-2105-10-427, 3087347, 20015389.
37. Childs L, Nikoloski Z, May P, Walther D. Identification and classification of ncRNA molecules using graph properties. Nucleic Acids Res 2009, 37(9):1-12. 2615629, 19033363.
38. Breiman L. Random forests. Mach Learn 2001, 45(1):5-32.
39. Griffiths-Jones S. Annotating noncoding RNA genes. Annu Rev Genom Hum Genet 2007, 8:279-298.
40. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, et al. Initial sequencing and analysis of the human genome. Nature 2001, 409(6822):860-921. 10.1038/35057062, 11237011.
41. Diao YB, Li ML, Feng ZN, Yin JJ, Pan Y. The community structure of human cellular signaling network. J Theor Biol 2007, 247(4):608-615. 10.1016/j.jtbi.2007.04.007, 17540409.
42. Fang YP, Ma DC, Li ML, Wen ZN, Diao Y B. Investigation of proteins folding rates and their properties of amino acid networks. Chemometrics Intellig Lab Syst 2010, 101:123-129.
43. Csardi G, Nepusz T. The igraph software package for complex network research. Inter J Complex Sys 2006, 1695.