A fast ab-initio method for predicting miRNA precursors in genomes

Nucleic Acids Research

Volumn 40, Issue 11, 2012, Pages

  Tempel, Sébastien ^a   Tahi, Fariza ^a  

^a UNIVERSITY OF EVRY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA; MICRORNA PRECURSOR; UNCLASSIFIED DRUG;

AB INITIO CALCULATION; ALGORITHM; ARTICLE; CALCULATION; CONTROLLED STUDY; GENOME; INTERMETHOD COMPARISON; PREDICTION; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; RNA SEQUENCE; RNA STRUCTURE; STRUCTURE ANALYSIS; ACCURACY; DATA EXTRACTION; GENE SEQUENCE; GENETIC ALGORITHM; GENETIC DATABASE; GENETIC MODEL; GENOME ANALYSIS; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION; SENSITIVITY AND SPECIFICITY; SEQUENCE HOMOLOGY; VALIDATION PROCESS;

ALGORITHMS; ANIMALS; DATA INTERPRETATION, STATISTICAL; GENOMICS; HUMANS; MICE; MICRORNAS; NUCLEIC ACID CONFORMATION; RNA PRECURSORS; SOFTWARE;

EID: 84862162317     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gks146     Document Type: Article

References (34)

1. Bartel, D. (2004) MicroRNAs: genomics, biogenesis, mechanism and function. Cell, 116, 281-197.
2. He, L. and Hannon, G. (2004) microRNAs: small RNAs with a big role in gene regulation. Nat. Rev. Genet, 5, 522-531. (Pubitemid 38868508)
3. Lee, Y., Kim, M., Han, J., Yeom, K., Lee, S., Baek, S. and Kim, V. (2004) microRNA genes are transcribed by RNA polymerase II. EMBO J., 23, 4051-4060. (Pubitemid 39472426)
4. Wang, Y., Stricker, H.M., Gou, D. and Liu, L. (2007) microRNA: past and present. Front. Biosci., 12, 2316-2329.
5. Lai, E.C., Tomancak, P., Williams, R.W. and Rubin, G.M. (2003) Computational identification of Drosophila microRNA genes. Genome Biol., 4, R42.
6. Huang, T.H., Fan, B., Rothschild, M.F., Hu, Z.L., Li, K. and Zhao, S.H. (2007) MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans. BMC Bioinformatics, 8, 341.
7. Hertel, J. and Stadler, P.F. (2006) Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data. Bioinformatics, 22, e197-e202. (Pubitemid 44288287)
8. Yousef, M., Nebozhyn, M., Shatkay, H., Kanterakis, S., Showe, L.C. and Showe, M.K. (2006) Combining multi-species genomic data for microRNA identification using a Näve Bayes classifier. Bioinformatics, 22, 1325-1334. (Pubitemid 43985268)
9. Terai, G., Komori, T., Asai, K. and Kin, T. (2007) miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity. RNA, 13, 2081-2090. (Pubitemid 350127550)
10. 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. (Pubitemid 351149713)
11. 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. (Pubitemid 41264095)
12. Legendre, M., Lambert, A. and Gautheret, D. (2005) Profile-based detection of microRNA precursors in animal genomes. Bioinformatics, 21, 841-845. (Pubitemid 40467902)
13. Hofacker, I.L., Fontana, W., Stadler, P.F., Bonhoeffer, S., Tacker, M. and Schuster, P. (1994) Fast folding and comparison of RNA secondary structures. Monatshefte f. Chemie, 125, 167-188.
14. Hofacker, I.L., Priwitzer, B. and Stadler, P.F. (2004) Prediction of locally stable RNA secondary structures for genome-wide surveys. Bioinformatics, 20, 186-190. (Pubitemid 38195676)
15. Zuker, M. (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res., 31, 3406-3415. (Pubitemid 37442169)
16. Markham, N.R. and Zuker, M. (2005) DINAMelt web server for nucleic acid melting prediction. Nucleic Acids Res., 33, W577-W581. (Pubitemid 44529988)
17. Xue, C., Li, F., He, T., Liu, G.P., Li, Y. and Zhang, X. (2005) Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 6, 310.
18. Chang, D.T., Wang, C.C. and Chen, J.W. (2008) Using a kernel density estimation based classifier to predict species-specific microRNA precursors. BMC Bioinformatics, 9, 12.
19. 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.
20. Batuwita, R. and Palade, V. (2009) microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics, 25, 989-995.
21. Kwang Loong, S.N.G. and Mishra, S.K. (2007) Unique folding of precursor microRNAs: Quantitative evidence and implications for de novo identification. RNA, 13, 170-187. (Pubitemid 46147894)
22. 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.
23. 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.
24. Tyagi, S., Vaz, C., Gupta, V., Bhatia, R., Maheshwari, S., Srinivasan, A. and Bhattacharya, A. (2008) CID-miRNA: a web server for prediction of novel miRNA precursors in human genome. Biochem. Biophys. Res. Comm., 372, 831-834.
25. Wu, Y., Wei, B., Liu, H., Li, T. and 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.
26. Brameier, M. and Wiuf, C. (2007) Ab initio identification of human microRNAs based on structure motifs. BMC Bioinformatics, 8, 478.
27. Xu, Y., Zhou, X. and Zhang, W. (2008) MicroRNA prediction with a novel ranking algorithm based on random walks. Bioinformatics, 24, 50-58.
28. Kumar, S., Ansari, F.A. and Scaria, V. (2009) Prediction of viral microRNA precursors based on human microRNA precursor sequence and structural features. Virol J., 6, 129.
29. Grundhoff, A., Sullivan, C.S. and Ganem, D. (2006) A combined computational and microarray-based approach identifies novel microRNAs encoded by human gamma-herpesviruses. RNA, 12, 733-750.
30. Joachims, T. (1999) Making large-scale support vector machine learning practical. MIT Press, 11, 169-184.
31. Engelen, S. and Tahi, F. (2010) Tfold: efficient in silico prediction of non-coding RNA secondary structures. Nucleic Acids Res., 38, 2453-2466.
32. Tempel, S., Rousseau, C., Tahi, F. and Nicolas, J. (2010) ModuleOrganizer: detecting modules in families of transposable elements. BMC Bioinformatics, 11, 474.
33. Helvik, S.A., Snove, O.J. and Saetrom, P. (2007) Reliable prediction of Drosha processing sites improves microRNA gene prediction. Bioinformatics, 23, 142-149. (Pubitemid 46189602)
34. Van der Burgt, A., Fiers, M.W.J.E., Nap, J.P. and Van Ham, R.C.H.J. (2009) In silico miRNA prediction in metazoan genomes: balancing between sensitivity and specificity. BMC Genomics, 10, 204.