Machine learning predicts microRNA target sites

doi:10.1017/CBO9780511541766.018

15 - Machine learning predicts microRNA target sites

from III - Computational biology of microRNAs

Published online by Cambridge University Press: 22 August 2009

Pål Sætrom Jr. and

Ola Snøve

Edited by

Krishnarao Appasani

Foreword by

Sidney Altman and

Victor R. Ambros

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Pål Sætrom Jr.: Affiliation:
Interagon AS Laboratoriesenteret Medisinsk Teknisk Senter NO-7489 Trondheim Norway
Ola Snøve: Affiliation:
Interagon AS Laboratoriesenteret Medisinsk Teknisk Senter NO-7489 Trondheim Norway

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Summary

Introduction

Ceanorhabditis elegans' lin-4 and let-7 were discovered seven years apart in the 1990s (Lee et al., 1993; Wightman et al., 1993; Reinhart et al., 2000). Because of their importance for correct timing in post-embryonic larval development in worms, these non-protein-coding molecules were first referred to as small temporal RNAs, but following the discovery of numerous RNAs with similar characteristics (Lau et al., 2001; Lee and Ambros, 2001; Lagos-Quintana et al., 2001), lin-4 and let-7 have been recognized as the founding members of the microRNA (miRNA) family. The details of the discovery of miRNAs and their involvement in various pathways are described in earlier chapters in Part I of this book.

To enable functional inference, it is important to identify the miRNA targets, and many efforts have been made to solve this problem in the past few years. MicroRNAs are known to regulate gene expression on two levels, namely by translational suppression (Olsen and Ambros, 1999) and mRNA depletion (Yekta et al., 2004). But despite massive resources invested in this problem, we have yet to find more than one human miRNA with assigned target and function (Hornstein et al., 2005). Massive evidence does, however, support the crucial role of miRNAs in accurate and timely regulation of messages, which means that we have to develop new approaches to identify their genetic targets.

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Type: Chapter
Information: MicroRNAs
From Basic Science to Disease Biology
, pp. 210 - 220
DOI: https://doi.org/10.1017/CBO9780511541766.018 [Opens in a new window]
Publisher: Cambridge University Press
Print publication year: 2007

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References

Bentwich, I., Avniel, A., Karov, Y.et al. (2005). Identification of hundreds of conserved and nonconserved human microRNAs. Nature Genetics, 37, 766–770.Google Scholar

Boutla, A., Delidakis, C. and Tabler, M. (2003). Developmental defects by antisense-mediated inactivation of micro-RNAs 2 and 13 in Drosophila and the identification of putative target genes. Nucleic Acids Research, 31, 4973–4980.Google Scholar

Brennecke, J., Hipfner, D. R., Stark, A., Russell, R. B. and Cohen, S. M. (2003). bantam encodes a developmentally regulated miRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell, 113, 25–36.Google Scholar

Brennecke, J., Stark, A., Russell, R. B. and Cohen, S. M. (2005). Principles of microRNA-target recognition. Public Library of Science Biology, 3, e85.Google Scholar

Doench, J. G. and Sharp, P. A. (2004). Specificity of microRNA target selection in translational repression. Genes & Development, 18, 504–511.Google Scholar

Foster, J. A. (2001). Evolutionary computation. Nature Reviews Genetics, 2, 428–436.Google Scholar

Freund, Y. and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119–139.Google Scholar

He, L., Thomson, M., Hemann, M. T.et al. (2005). A microRNA polycistron as a potential human oncogene. Nature, 435, 828–833.Google Scholar

Hornstein, E., Mansfield, J., Yekta, S.et al. (2005). The microRNA miR-196 acts upstream of Hoxb8 and Shh in limb development. Nature, 438, 671–674.Google Scholar

Kiriakidou, M., Nelson, P. T., Kouranov, A.et al. (2004). A combined computational-experimental approach predicts human microRNA targets. Genes & Development, 18, 1165–1178.Google Scholar

Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th IJCAI, pp. 1137–1143.

Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Natural Selection. Cambridge, Massachusetts: MIT Press.

Lagos-Quintana, M., Rauhut, R., Lendeckel, W. and Tuschl, T. (2001). Identification of novel genes coding for small expressed RNAs. Science, 294, 853–858.Google Scholar

Lau, N. C., Lim, L. P., Weinstein, E. G. and Bartel, D. P. (2001). An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science, 294, 858–862.Google Scholar

Lee, R. C. and Ambros, V. (2001). An extensive class of small RNAs in Caenorhabditis elegans. Science, 294, 862–864.Google Scholar

Lee, R. C., Feinbaum, R. and Ambros, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75, 843–854.Google Scholar

Lewis, B. P., Hung, Shih I., Jones-Rhoades, M. W., Bartel, D. P. and Burge, C. B. (2003). Prediction of mammalian microRNA targets. Cell, 115, 787–798.Google Scholar

Lewis, B. P., Burge, C. B. and Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell, 120, 15–20.Google Scholar

Meir, R. and Rätsch, G. (2003). An introduction to boosting and leveraging. In Advanced Lectures on Machine Learning, Mendelson, S. and Smola, A., eds., volume 2600, pp. 118–183. Springer-Verlag.

O'Donnell, K. A., Wentzel, E. A., Zeller, K. I., Dang, C. V. and Mendell, J. T. (2005). c-Myc-regulated microRNAs modulate E2F1 expression. Nature, 435, 839–843.Google Scholar

Olsen, P. H. and Ambros, V. (1999). The lin-4 regulatory RNA controls developmental timing in Caenorhabditis elegans by blocking LIN-14 protein synthesis after the initiation of translation. Developmental Biology, 216, 671–680.Google Scholar

Rajewsky, N. and Socci, N. D. (2004). Computational identification of microRNA targets. Developmental Biology, 267, 529–535.Google Scholar

Rehmsmeier, M., Steffen, P., Höchsmann, M. and Giegerich, R. (2004). Fast and effective prediction of microRNA/target duplexes. RNA, 10, 1507–1517.Google Scholar

Reinhart, B. J., Slack, F. J., Basson, M.et al. (2000). The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature, 403, 901–906.Google Scholar

Sætrom, P. (2004). Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming. Bioinformatics, 20, 3055–3063.Google Scholar

Sætrom, P. and Snøve, O. Jr. (2004). A comparison of siRNA efficacy predictors. Biochemical and Biophysical Research Communications, 321, 247–253.Google Scholar

Sætrom, O., Snøve, O. Jr. and Sætrom, P. (2005a). Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. RNA, 11, 995–1003.Google Scholar

Sætrom, P., Sneve, R., Kristiansen, K. I.et al. (2005b). Predicting non-coding RNA genes in Escherichia coli with boosted genetic programming. Nucleic Acids Research, 33, 3263–3270.Google Scholar

Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B (Methodological), 36, 111–147.Google Scholar

Wightman, B., Ha, I. and Ruvkun, G. (1993). Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 75, 855–862.Google Scholar

Xie, X., Lu, J., Kulbokas, E.et al. (2005). Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals. Nature, 434, 338–345.Google Scholar

Yekta, S., Shih, I. and Bartel, D. P. (2004). MicroRNA-directed cleavage of HOXB8 mRNA. Science, 304, 594–596.Google Scholar

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