Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-29T18:29:00.554Z Has data issue: false hasContentIssue false

15 - Machine learning predicts microRNA target sites

from III - Computational biology of microRNAs

Published online by Cambridge University Press:  22 August 2009

By
Pål Sætrom Jr.  and
Ola Snøve
Edited by
Krishnarao Appasani
Foreword by
Sidney Altman  and
Victor R. Ambros
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
Get access

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.

Type
Chapter
Information
MicroRNAs
From Basic Science to Disease Biology
 , pp. 210 - 220
Publisher: Cambridge University Press
Print publication year: 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×