Abstract
Functional RNA elements in post-transcriptional regulation of gene expression are often correlated with distinct RNA stem-loop structures that are both thermodynamically stable and highly well- ordered. Recent Discoveries of microRNA (miRNA) and small regulatory RNAs indicate that there are a large class of small non-coding RNAs having the potential to form a distinct, well-ordered and/or stable stem-loop in numbers of genomes. The distinct RNA structure can be well evaluated by a quantitative measure, the energy difference (E diff ) between the optimal structure folded from the segment and its corresponding optimal restrained structure where all base pairings formed in the original optimal structure are forbidden. In this study, we present an efficient algorithm to compute E diff of local segment by scanning a window along a genomic sequence. The complexity of computational time is O(L ×n 2), where L is the length of the genomic sequence and n is the size of the sliding window. Our results indicate that the known stem-loops folded by miRNA precursors have high normalized E diff scores with highly statistical significance. The distinct well-ordered structures related to the known miRNA can be predicted in a genomic sequence by a robust statistical inference. Our computational method StemED can be used as a general approach for the discovery of distinct stem-loops in genomic sequences.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Simons, R.W., Grunberg-Manago, M. (eds.): RNA Structure and Function. Cold Spring Harbor Lab. Press, New York (1998)
Griffiths-Jones, G.R., van Dongen, S., Bateman, A., Enright, A.J.: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34, D140–144 (2006)
Macke, T.J., Ecker, D.J., Gutell, R.R., Gautheret, D., Case, D.A., Sampath, R.: RNAMotif, an RNA secondary structure definition and search algorithm. Nucleic Acids Res. 29, 4724–4735 (2001)
Le, S.Y., Maizel Jr., J.V., Zhang, K.: An Algorithm for detecting homologues of known structured RNAs in genomes. In: Proceedings of the 2004 IEEE Bioinformatics Conference, CSB2004, Stanford, California, pp. 300–310. IEEE, Los Alamitos (2004)
Klein, R.J., Eddy, S.R.: RSEARCH: Finding homologs of single structured RNA sequences. BMC Bioinformatics 4, 44 (2003)
Gautheret, A., Lambert, A.: Direct RNA motif definition and identification from multiple sequence alignments using secondary structure profiles. J. Mol Biol. 313, 1003–1011 (2001)
Grillo, G., Licciulli, F., Liuni, S., Sbisa, E., Pesole, G.: PatSearch: a program for the detection of patterns and structural motifs in nucleotide sequences. Nucleic Acids Res. 31, 3608–3612 (2003)
Lowe, T.M., Eddy, S.R.: tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997)
Billoud, B., Kontic, M., Viari, A.: Palingol: a declarative programming language to describe nucleic acids’ secondary structures and to scan sequence database. Nucleic Acids Res. 24, 1395–1403 (1996)
Le, S.Y., Chen, J.H., Konings, D., Maizel Jr., J.V.: Discovering well-ordered folding patterns in nucleotide sequences. Bioinformatics 19, 354–361 (2003)
Carter, R.J., Dubchak, I., Holbrook, S.R.: A computational approach to identify genes for functional RNAs in genomic sequences. Nucl. Acids Res. 29, 3928–3938 (2001)
Lim, L.P., Lau, N.C., Weinstein, E.G., Abdelhakim, A., Yekta, M., Rhoades, M.W., Burge, C.B., Bartel, D.P.: The microRNAs of Caenorhabditis elegans. Genes & Development 17, 991–1008 (2003)
Lai, E.C., Tomancak, P., Williams, R.W., Rubin, G.M.: Computational identification of Drosophila microRNA genes. Genome Biology 20, R42.1-R42.20 (2003)
Nam, J.W., Shin, K.R., Han, J., Lee, Y., Kim, V.N., Zhang, B.T.: Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucl. Acids Res. 33, 3570–358 (2005)
Pfeffer, S., Sewer, A., Lagos-Quintana, M., Sheridan, R., Sander, C., Grasser, F.A., van Dyk, L.F., Ho, C.K., Shuman, S., Chien, M., Russo, J.J., Ju, J., Randall, G., Lindenbach, B.D., Rice, C.M., Simon, V., Ho, D.D., Zavolan, M., Tuschl, T.: Identification of microRNAs of the herpesvirus family. Nature Methods 2, 269–276 (2005)
Berezikov, E., Guryev, V., van de Belt, J., Wienholds, E., Plasterk, R.H.A., Cuppen, E.: Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120, 21–24 (2005)
Wang, X., Zhang, J., Li, F., Gu, J., He, T., Zhang, X., Li, Y.: MicroRNA identification based on sequence and structure alignment. Bioinformatics 21, 3610–3614 (2005)
Le, S.Y., Maizel Jr., J.V., Zhang, K.: Finding conserved well-ordered RNA structures in genomic sequences. Int. J. Comp. Intelligence and Applications 4, 417–430 (2004)
Le, S.Y., Zhang, K., Maizel Jr., J.V.: RNA molecules with structure dependent functions are uniquely folded. Nucleic Acids Res. 30, 3574–3582 (2002)
Mathews, D.H., Sabina, J., Zuker, M., Turner, D.H.: Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999)
Zuker, M., Stiegler, P.: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9, 133–148 (1981)
Bennasser, Y., Le, S.Y., Yeung, M.L., Jeang, K.T.: MicroRNAs in human immunodeficiency virus-1 infection. Methods Mol. Biol. 342, 241–252 (2006)
Lex, S.Y., Chen, J.H., Maizel Jr., J.V.: Statistical inference for well-ordered structures in nucleotide sequences. Proc. IEEE Comput. Soc. Bioinform. Conf. 2, 190–196 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Le, SY., Chen, JH. (2007). Statistical Inference on Distinct RNA Stem-Loops in Genomic Sequences. In: Hochreiter, S., Wagner, R. (eds) Bioinformatics Research and Development. BIRD 2007. Lecture Notes in Computer Science(), vol 4414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71233-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-71233-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71232-9
Online ISBN: 978-3-540-71233-6
eBook Packages: Computer ScienceComputer Science (R0)