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13 - Computational approaches to elucidate miRNA biology

from III - Computational biology of microRNAs

Published online by Cambridge University Press:  22 August 2009

Praveen Sethupathy
Affiliation:
University of Pennsylvania Center for Bioinformatics 1407 Blockley Hall/6021 Philadelphia, PA 19104-6021 USA
Molly Megraw
Affiliation:
University of Pennsylvania Center for Bioinformatics 1407 Blockley Hall/6021 Philadelphia, PA 19104-6021 USA
Artemis G. Hatzigeorgiou
Affiliation:
University of Pennsylvania Center for Bioinformatics 1407 Blockley Hall/6021 Philadelphia, PA 19104-6021 USA
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Summary

Introduction

Research in the past decade has revealed that microRNAs (miRNAs) are widespread and that they are likely to underlie an appreciably larger set of disease processes than is currently known. The first miRNAs and their functions were determined via classical genetic techniques. Soon after, a number of miRNAs were discovered experimentally (Lagos-Quintana et al., 2001). However, the characterization of miRNA function remained elusive owing to low-throughput experiments and often indeterminate results, most notably for those miRNAs which have multiple roles in multiple tissues. High-throughput experimental methods for miRNA target identification are the ideal solution, but such methods are not currently available. As a result, computational methods were developed, and are still regularly used, for the purpose of identifying miRNA targets.

Most current target prediction programs require the sequences of known miRNAs. Currently, there are 332 known miRNAs in the human genome. The estimation of the total number of miRNAs varies from publication to publication (Lim et al., 2003; Bentwich et al., 2005). In a recent paper, Bentwich et al. contended that there are at least 500 more miRNAs that are yet to be identified (Bentwich et al., 2005). Despite the number of unknown miRNAs, computational approaches based on features of known miRNAs have been instrumental in the discovery of as-of-yet-unknown miRNAs in the genome. The past few years have witnessed an explosion in information regarding the genomic organization of miRNAs, the biogenesis of miRNAs, the targeting mechanisms of miRNAs, and the regulatory networks in which miRNAs are involved.

Type
Chapter
Information
MicroRNAs
From Basic Science to Disease Biology
, pp. 187 - 198
Publisher: Cambridge University Press
Print publication year: 2007

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References

Adai, A., Johnson, C., Mlothshwa, S.et al. (2005). Computational prediction of miRNAs in Arabidopsis thaliana. Genome Research, 15, 78–91.Google Scholar
Ambros, V., Lee, R. C., Lavanway, A., Williams, P. T. and Jewell, D. (2003). MicroRNAs and other tiny endogenous RNAs in C. elegans. Current Biology, 13, 807–818.CrossRefGoogle Scholar
Ashburner, M., Ball, C. A., Blake, J. A.et al. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25, 25–29.CrossRefGoogle Scholar
Baskerville, S. and Bartel, D. P. (2005). Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA, 11, 241–247.CrossRefGoogle Scholar
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
Brennecke, J., Stark, A., Russell, R. B., and Cohen, S. M. (2005). Principles of microRNA-target recognition. Public Library of Science Biology, 3, 404–418.Google Scholar
Burgler, C. and Macdonald, P. M. (2005). Prediction and verification of microRNA targets by MovingTargets, a highly adaptable prediction method. BMC Genomics, 6, 88.Google Scholar
Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 1–25.Google Scholar
Enright, A. J., John, B., Gaul, U.et al. (2003). MicroRNA targets in Drosophila. Genome Biology, 5, R1.Google Scholar
Grad, Y., Aach, J., Hayes, G. D.et al. (2003). Computational and experimental identification of C. elegans microRNAs. Molecular Cell, 11, 1253–1263.Google Scholar
Grün, D., Wang, Y., Langenberger, D., Gunsalus, K. C. and Rajewsky, N. (2005). MicroRNA target predictions across seven Drosophila species and comparison to mammalian targets. Public Library of Science Computational Biology, 1, 51–66.Google Scholar
Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer-Verlag.CrossRef
Hinrichs, A. S., Karolchik, D., Baertsch, R.et al. (2006). The UCSC Genome Browser Database: update 2006. Nucleic Acids Research, 34, D590–598.Google Scholar
John, B., Enright, A. J., Aravin, A.et al. (2004). Human microRNA targets. Public Library of Science Biology, 2, 1862–1879.Google Scholar
Kent, W. J., Sugnet, C. W., Furey, T. S.et al. (2002). The Human Genome Browser at UCSC. Genome Research, 12, 996–1006.Google Scholar
Kiriakidou, M., Nelson, P., Kouranov, A.et al. (2004). A combined computational-experimental approach predicts human miRNA targets. Genes & Development, 18, 1165–1178.CrossRefGoogle Scholar
Krek, A., Grun, D., Poy, M. N.et al. (2005). Combinatorial microRNA target predictions. Nature Genetics, 37, 495–500.CrossRefGoogle Scholar
Lagos-Quintana, M., Rauhut, R., Lendeckel, W. and Tuschl, T. (2001). Identification of novel genes coding for small expressed RNAs. Science, 194, 797–799.Google Scholar
Lai, E. C., Tomancak, P., Williams, R. W. and Rubin, G. M. (2003). Computational identification of Drosophila microRNA genes. Genome Biology, 4, R42, 1–20.Google Scholar
Lewis, B. P., 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.CrossRefGoogle Scholar
Lim, L. P., Lau, N. C., Weinstein, E. G.et al. (2003). The microRNAs of Caenorhabditis elegans. Genes & Development, 17, 991–1008.Google Scholar
Lindow, M. and Krogh, A. (2005). Computational evidence for hundreds of non-conserved plant microRNAs. BMC Genomics, 6, 119.Google Scholar
Pfeffer, S., Sewer, A., Lagos-Quintana, M.et al. (2005). Identification of microRNAs of the herpesvirus family. Nature Methods, 2, 269–276.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., Hochsmann, M. and Giegerich, R. (2004). Fast and effective prediction of microRNA/target duplexes. RNA, 10, 1507–1517.Google Scholar
Robins, H., Li, Y. and Padgett, R. W. (2005). Incorporating structure to predict microRNA targets. Proceedings of the National Academy of Sciences USA, 102, 4006–4009.Google Scholar
Rusinov, V., Baev, V., Minkov, I. N. and Tabler, M. (2005). MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Research, 33, 696–700.Google Scholar
Saetrom, O., Snove, O. Jr. and Saetrom, P. (2005). Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. RNA, 11, 995–1003.Google Scholar
Sethupathy, P., Corda, B. and Hatzigeorgiou, A. G. (2006). TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA, 12, 192–197.Google Scholar
Sewer, A., Paul, N., Landgraf, P.et al. (2005). Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics, 6, 267.Google Scholar
Stark, A., Brennecke, J., Russell, R. B. and Cohen, S. M. (2003). Identification of Drosophila microRNA targets. Public Library of Science Biology, 1, 1–13.Google Scholar
Tinoco, I. Jr, Borer, P. N., Dengler, B.et al. (1973). Improved estimation of secondary structure in ribonucleic acids. Nature New Biology, 246, 40–41.Google Scholar
Zhang, B. H., Pan, X. P., Wang, Q. L., Cobb, G. P. and Anderson, T. A. (2005). Identification and characterization of new plant microRNAs using EST analysis. Cell Research, 15, 336–360.Google Scholar

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