HuMiTar: A sequence-based method for prediction of human microRNA targets

详细信息    查看全文

• 作者：Jishou Ruan (1)
  Hanzhe Chen (1)
  Lukasz Kurgan (2)
  Ke Chen (2)
  Chunsheng Kang (3)
  Peiyu Pu (3)
  
• 刊名：Algorithms for Molecular Biology
• 出版年：2008
• 出版时间：December 2008
• 年：2008
• 卷：3
• 期：1
• 全文大小：1007KB
• 参考文献：1. Engels BM, Hutvagner G: Principles and effects of microRNA-mediated post-transcriptional gene regulation. / Oncogene 2006, 25:6163鈥?169. CrossRef
  2. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS: MicroRNA targets in Drosophila. / Genome Biol 2003,5(1):R1. CrossRef
  3. Lewis BP, Shih I, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. / Cell 2003, 115:787鈥?98. CrossRef
  4. Stark A, Brennecke J, Russell RB, Cohen SM: Identification of Drosophila MicroRNA targets. / PLoS Biol 2003,1(3):e397. CrossRef
  5. Doench JG, Sharp PA: Specificity of microRNA target selection in translational repression. / Genes Dev 2004,18(5):504鈥?11. CrossRef
  6. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS: Human microRNA targets. / PLoS Biol 2004,2(11):e363. CrossRef
  7. Kiriakidou M, Nelson P, Kouranov A, Fitziev P, Bouyioukos C, Mourelatos Z, Hatzigeorgiou AG: A combined computational- experimental approach predicts human miR targets. / Genes & Dev 2004, 18:1165鈥?178. CrossRef
  8. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R: Fast and effective prediction of microRNA/target duplexes. / RNA 2004, 10:1507鈥?517. CrossRef
  9. Vella MC, Reinert K, Slack FJ: Architecture of a validated microRNA: target interaction. / Chem Biol 2004, 11:1619鈥?623. CrossRef
  10. Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, Piedade I, Gunsalus KC, Stoffel M, / et al.: Combinatorial microRNA target predictions. / Nat Genet 2005, 37:495鈥?00. CrossRef
  11. Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. / Cell 2005, 120:15鈥?0. CrossRef
  12. Saetrom O, Snove O Jr, Saetrom P: Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. / RNA 2005,11(7):995鈥?003. CrossRef
  13. Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M: Systematic discovery of regulatory motifs in human promoters and 3'UTRs by comparison of several mammals. / Nature 2005, 434:338鈥?45. CrossRef
  14. Watanabe Y, Yachie N, Numata K, Saito R, Kanai A, Tomita M: Computational analysis of microRNA targets in Caenorhabditis elegans. / Gene 2006, 365:2鈥?0. CrossRef
  15. Brennecke J, Stark A, Russell RB, Cohen SM: Principles of MicroRNA-Target Recognition. / PLoS Biol 2005,3(3):e85. CrossRef
  16. Huang JC, Babak T, Corson TW, Chua G, Khan S, Gallie BL, Hughes TR, Blencowe BJ, Frey BJ, Morris QD: Using expression profiling data to identify human microRNA targets. / Nat Methods 2007,4(12):1045鈥?. CrossRef
  17. Rajewsky N: microRNA target predictions in animals. / Nat Genet 2006,38(Suppl):S8-S13. CrossRef
  18. Sethupathy P, Megraw M, Hatzigeorgiou AG: A guide through present computational approaches for the identification of mammalian microRNA targets. / Nat Methods 2006, 3:881鈥?86. CrossRef
  19. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. / Nucleic Acids Res 2006, 34:D140-D144. CrossRef
  20. Kuhn DE, Martin MM, Feldman DS, Terry AV Jr, Nuovo GJ, Elton TS: Experimental validation of miRNA targets. / Methods 2008,44(1):47鈥?4. CrossRef
  21. Yan X, Chaoa T, Tub K, Zhanga Y, Xieb L, Gonga Y, Yuana J, Qianga B, Peng X: Improving the prediction of human microRNA target genes by using ensemble algorithm. / FEBS Lett 2007,581(8):1587鈥?593. CrossRef
  22. Yousef M, Jung S, Kossenkov AV, Showe LC, Showe MK: Na茂ve Bayes for microRNA target predictions 鈥?machine learning for microRNA targets. / Bioinformatics 2007,23(22):2987鈥?992. CrossRef
  23. Moxon S, Moulton V, Kim JT: A scoring matrix approach to detecting miRNA target sites. / Alg Mol Biol 2008, 3:3. CrossRef
  24. Didiano D, Hobert O: Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. / Nat Struct Mol Biol 2006,13(9):849鈥?1. CrossRef
  25. Sethupathy P, Corda B, Hatziegeorgiou AG: TarBase: A comprehensive database of experimentally supported animal microRNA targets. / RNA 2006, 12:192鈥?97. CrossRef
  26. Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, / et al.: Prediction of plant microRNA targets. / Cell 2002, 110:513鈥?20. CrossRef
  27. Zhang Y: miRU: an automated plant miRNA target prediction server. / Nucleic Acids Res 2005, 33:W701-W704. CrossRef
  28. Gusev Y: Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA. / Methods 2008,44(1):61鈥?2. CrossRef
  29. Adams BD, Furneaux H, White B: The Micro-Ribonucleic Acid (miRNA) miR-206 Targets the Human Estrogen Receptor-伪 (ER伪) and Represses ER伪 Messenger RNA and Protein Expression in Breast Cancer Cell Lines. / Mol Endocrinol 2007,21(5):1132鈥?147. CrossRef
  30. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E: The role of site accessibility in microRNA target recognition. / Nat Genetics 2007, 39:1278鈥?4. CrossRef
  31. Xi Y, Shalgi R, Fodstad O, Pilpel Y, Ju J: Differentially Regulated Micro-RNAs and Actively Translated Messenger RNA Transcripts by Tumor suppressor p53 in Colon Cancer. / Clin Cancer Res 2006,12(7 Pt 1):2014鈥?024. CrossRef
  32. Nielsen CB, Shomron N, Sandberg R, Hornstein E, Kitzman J, Burge CB: Determinants of targeting by endogenous and exogenous microRNAs and siRNAs. / RNA 2007, 13:1894鈥?910. CrossRef
  33. Vasudevan S, Tong Y, Steitz JA: Switching from repression to activation: microRNAs can up-regulate translation. / Science 2007,318(5858):1931鈥?. CrossRef
  34. Rusk N: When microRNAs activate translation. / Nature Methods 2008, 5:1223.
  35. Stark A, Brennecke J, Bushati N, Russell RB, Cohen SM: Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3'UTR evolution. / Cell 2005, 123:1133鈥?6. CrossRef
  36. Long D, Lee R, Williams P, Chan CY, Ambros V, Ding Y: Potent effect of target structure on microRNA function. / Nat Struct Mol Biol 2007, 14:287鈥?4. CrossRef
  37. Grimson A, Kai-How Farth K, Johnston WK, Garrnet-Engele P, Lim LP, Bartel DP: MicroRNA targeting specificity in mammals: determinants beyond seed pairing. / Molecular Cell 2007, 27:91鈥?05. CrossRef
  
• 作者单位：Jishou Ruan (1)
  Hanzhe Chen (1)
  Lukasz Kurgan (2)
  Ke Chen (2)
  Chunsheng Kang (3)
  Peiyu Pu (3)
  
  1. Chern Institute for Mathematics, College of Mathematics and LPMC, Nankai University, Tianjin, PR China
  2. Department of Electrical and Computer Engineering, University of Alberta, Canada
  3. Neuro-oncology laboratory, General Hospital of the Tianjin Medical University, Tianjin, PR China
  
• ISSN：1748-7188

文摘

Background MicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved. Results We present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates. Conclusion The proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.