Computational approaches to elucidate miRNA biology

doi:10.1017/CBO9780511541766.016

13 - Computational approaches to elucidate miRNA biology

from III - Computational biology of microRNAs

Published online by Cambridge University Press: 22 August 2009

Praveen Sethupathy ,

Molly Megraw and

Artemis G. Hatzigeorgiou

Edited by

Krishnarao Appasani

Foreword by

Sidney Altman and

Victor R. Ambros

Show author details

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

DOI: https://doi.org/10.1017/CBO9780511541766.016 [Opens in a new window]

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.CrossRef Google 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.CrossRef Google 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.CrossRef Google 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.CrossRef Google Scholar

Krek, A., Grun, D., Poy, M. N.et al. (2005). Combinatorial microRNA target predictions. Nature Genetics, 37, 495–500.CrossRef Google 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.CrossRef Google 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