Molecular Biotechnology

, 41:180

Informatic Resources for Identifying and Annotating Structural RNA Motifs

Review

Abstract

Post-transcriptional regulation of genes and transcripts is a vital aspect of cellular processes, and unlike transcriptional regulation, remains a largely unexplored domain. One of the most obvious and most important questions to explore is the discovery of functional RNA elements. Many RNA elements have been characterized to date ranging from cis-regulatory motifs within mRNAs to large families of non-coding RNAs. Like protein coding genes, the functional motifs of these RNA elements are highly conserved, but unlike protein coding genes, it is most often the structure and not the sequence that is conserved. Proper characterization of these structural RNA motifs is both the key and the limiting step to understanding the post-transcriptional aspects of the genomic world. Here, we focus on the task of structural motif discovery and provide a survey of the informatics resources geared towards this task.

Keywords

RNA Structure Informatics Bioinformatics Post-transcrption RNA-binding proteins Ribonomics RIP-Chip 

References

  1. 1.
    Abreu-Goodger, C., & Merino, E. (2005). RibEx: A web server for locating riboswitches and other conserved bacterial regulatory elements. Nucleic Acids Research, 33(Web Server issue), W690–W692.CrossRefGoogle Scholar
  2. 2.
    Abreu-Goodger, C., et al. (2004). Conserved regulatory motifs in bacteria: Riboswitches and beyond. Trends in Genetics: TIG, 20(10), 475–479. doi:10.1016/j.tig.2004.08.003.CrossRefGoogle Scholar
  3. 3.
    Anwar, M., Nguyen, T., & Turcotte, M. (2006). Identification of consensus RNA secondary structures using suffix arrays. BMC Bioinformatics, 7, 244. doi:10.1186/1471-2105-7-244.CrossRefGoogle Scholar
  4. 4.
    Bafna, V., & Zhang, S. (2004). FastR: Fast database search tool for non-coding RNA. Proceedings/IEEE Computational Systems Bioinformatics Conference, CSB. IEEE Computational Systems Bioinformatics Conference (pp. 52–61).Google Scholar
  5. 5.
    Bauer, M., Klau, G. W., & Reinert, K. (2007). Accurate multiple sequence-structure alignment of RNA sequences using combinatorial optimization. BMC Bioinformatics, 8, 271. doi:10.1186/1471-2105-8-271.CrossRefGoogle Scholar
  6. 6.
    Berman, H. M., et al. (1992). The nucleic acid database. A comprehensive relational database of three-dimensional structures of nucleic acids. Biophysical Journal, 63(3), 751–759.CrossRefGoogle Scholar
  7. 7.
    Bindewald, E., & Shapiro, B. A. (2006). RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA (New York, N.Y.), 12(3), 342–352. doi:10.1261/rna.2164906.Google Scholar
  8. 8.
    Bindewald, E., et al. (2008). RNAJunction: A database of RNA junctions and kissing loops for three-dimensional structural analysis and nanodesign. Nucleic Acids Research, 36(Database issue), D392–D397. doi:10.1093/nar/gkm842.Google Scholar
  9. 9.
    Busch, A., & Backofen, R. (2006). INFO-RNA—A fast approach to inverse RNA folding. Bioinformatics (Oxford, England), 22(15), 1823–1831. doi:10.1093/bioinformatics/btl194.CrossRefGoogle Scholar
  10. 10.
    Chang, T., et al. (2006). RNAMST: Efficient and flexible approach for identifying RNA structural homologs. Nucleic Acids Research, 34(Web Server issue), W423–W428.CrossRefGoogle Scholar
  11. 11.
    Coventry, A., Kleitman, D. J., & Berger, B. (2004). MSARI: Multiple sequence alignments for statistical detection of RNA secondary structure. Proceedings of the National Academy of Sciences of the United States of America, 101(33), 12102–12107. doi:10.1073/pnas.0404193101.CrossRefGoogle Scholar
  12. 12.
    Dalli, D., et al. (2006). STRAL: Progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time. Bioinformatics (Oxford, England), 22(13), 1593–1599. doi:10.1093/bioinformatics/btl142.CrossRefGoogle Scholar
  13. 13.
    di Bernardo, D., Down, T., & Hubbard, T. (2003). ddbRNA: Detection of conserved secondary structures in multiple alignments. Bioinformatics (Oxford, England), 19(13), 1606–1611. doi:10.1093/bioinformatics/btg229.CrossRefGoogle Scholar
  14. 14.
    Do, C. B., Foo, C., & Batzoglou, S. (2008). A max-margin model for efficient simultaneous alignment and folding of RNA sequences. Bioinformatics (Oxford, England), 24(13), i68–i76. doi:10.1093/bioinformatics/btn177.CrossRefGoogle Scholar
  15. 15.
    Doyle, F., et al. (2008). Bioinformatic tools for studying post-transcriptional gene regulation: The UAlbany TUTR collection and other informatic resources. Methods in Molecular Biology (Clifton, N.J.), 419, 39–52. doi:10.1007/978-1-59745-033-1_3.CrossRefGoogle Scholar
  16. 16.
    Dsouza, M., Larsen, N., & Overbeek, R. (1997). Searching for patterns in genomic data. Trends in Genetics: TIG, 13(12), 497–498. doi:10.1016/S0168-9525(97)01347-4.CrossRefGoogle Scholar
  17. 17.
    Eddy, S. R. (2006). Computational analysis of RNAs. Cold Spring Harbor Symposia on Quantitative Biology, 71, 117–128. doi:10.1101/sqb.2006.71.003.CrossRefGoogle Scholar
  18. 18.
    Gardner, P., & Giegerich, R. (2004). A comprehensive comparison of comparative RNA structure prediction approaches. BMC Bioinformatics, 5(1), 140. doi:10.1186/1471-2105-5-140.CrossRefGoogle Scholar
  19. 19.
    Gautheret, D., & Lambert, A. (2001). Direct RNA motif definition and identification from multiple sequence alignments using secondary structure profiles. Journal of Molecular Biology, 313(5), 1003–1011. doi:10.1006/jmbi.2001.5102.CrossRefGoogle Scholar
  20. 20.
    Griffiths-Jones, S., et al. (2005). Rfam: Annotating non-coding RNAs in complete genomes. Nucleic Acids Research, 33(suppl_1), D121–D124.Google Scholar
  21. 21.
    Griffiths-Jones, S., et al. (2006). miRBase: MicroRNA sequences, targets and gene nomenclature. Nucleic Acids Research, 34(suppl_1), D140–D144.CrossRefGoogle Scholar
  22. 22.
    Hamada, M., et al. (2006). Mining frequent stem patterns from unaligned RNA sequences. Bioinformatics (Oxford, England), 22(20), 2480–2487. doi:10.1093/bioinformatics/btl431.CrossRefGoogle Scholar
  23. 23.
    Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic Acids Research, 31(13), 3429–3431.CrossRefGoogle Scholar
  24. 24.
    Hofacker, I. L. (2004). RNA secondary structure analysis using the Vienna RNA package. Current Protocols in Bioinformatics/Editoral Board, Andreas D. Baxevanis… [et Al, Chapter 12, Unit 12.2].Google Scholar
  25. 25.
    Hofacker, I. L. (2007). RNA consensus structure prediction with RNAalifold. Methods in Molecular Biology (Clifton, N.J.), 395, 527–544.Google Scholar
  26. 26.
    Hofacker, I. L., Bernhart, S. H. F., & Stadler, P. F. (2004). Alignment of RNA base pairing probability matrices. Bioinformatics (Oxford, England), 20(14), 2222–2227.CrossRefGoogle Scholar
  27. 27.
    Holmes, I. (2005). Accelerated probabilistic inference of RNA structure evolution. BMC Bioinformatics, 6, 73.CrossRefGoogle Scholar
  28. 28.
    Horesh, Y., et al. (2007). RNAspa: A shortest path approach for comparative prediction of the secondary structure of ncRNA molecules. BMC Bioinformatics, 8, 366.CrossRefGoogle Scholar
  29. 29.
    Hu, Y. (2003). GPRM: A genetic programming approach to finding common RNA secondary structure elements. Nucleic Acids Research, 31(13), 3446–3449.CrossRefGoogle Scholar
  30. 30.
    Huang, H., et al. (2006). RegRNA: An integrated web server for identifying regulatory RNA motifs and elements. Nucleic Acids Research, 34(Web Server issue), W429–W434.CrossRefGoogle Scholar
  31. 31.
    Jacobs, G. H., et al. (2006). Transterm—extended search facilities and improved integration with other databases. Nucleic Acids Research, 34(Database issue), D37–D40.CrossRefGoogle Scholar
  32. 32.
    Ji, Y., Xu, X., & Stormo, G. D. (2004). A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences. Bioinformatics (Oxford, England), 20(10), 1591–1602.CrossRefGoogle Scholar
  33. 33.
    Katoh, K., & Toh, H. (2008). Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework. BMC Bioinformatics, 9, 212.CrossRefGoogle Scholar
  34. 34.
    Kin, T., Tsuda, K., & Asai, K. (2002). Marginalized kernels for RNA sequence data analysis. Genome Informatics. International Conference on Genome Informatics, 13, 112–122.Google Scholar
  35. 35.
    Kiryu, H., Kin, T., & Asai, K. (2007). Robust prediction of consensus secondary structures using averaged base pairing probability matrices. Bioinformatics (Oxford, England), 23(4), 434–441.CrossRefGoogle Scholar
  36. 36.
    Kiryu, H., et al. (2007). Murlet: A practical multiple alignment tool for structural RNA sequences. Bioinformatics (Oxford, England), 23(13), 1588–1598.CrossRefGoogle Scholar
  37. 37.
    Klein, R. J., & Eddy, S. R. (2003). RSEARCH: Finding homologs of single structured RNA sequences. BMC Bioinformatics, 4, 44.CrossRefGoogle Scholar
  38. 38.
    Knight, R., Birmingham, A., & Yarus, M. (2004). BayesFold: Rational 2 degrees folds that combine thermodynamic, covariation, and chemical data for aligned RNA sequences. RNA (New York, N.Y.), 10(9), 1323–1336.Google Scholar
  39. 39.
    Knudsen, B., & Hein, J. (2003). Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Research, 31(13), 3423–3428.CrossRefGoogle Scholar
  40. 40.
    Lambert, A., et al. (2005). Computing expectation values for RNA motifs using discrete convolutions. BMC Bioinformatics, 6, 118.CrossRefGoogle Scholar
  41. 41.
    Le, S., Maizel, J. V. & Zhang, K. (2004). An algorithm for detecting homologues of known structured RNAs in genomes. Proceedings/IEEE Computational Systems Bioinformatics Conference, CSB. IEEE Computational Systems Bioinformatics Conference (pp. 300–310).Google Scholar
  42. 42.
    Le, S. Y., Zhang, K., & Maizel, J. V. (1995). A method for predicting common structures of homologous RNAs. Computers and Biomedical Research, an International Journal, 28(1), 53–66.CrossRefGoogle Scholar
  43. 43.
    Lestrade, L., & Weber, M. J. (2006). snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs. Nucleic Acids Research, 34(Database issue), D158–D162.CrossRefGoogle Scholar
  44. 44.
    Lindgreen, S., Gardner, P. P., & Krogh, A. (2007). MASTR: Multiple alignment and structure prediction of non-coding RNAs using simulated annealing. Bioinformatics (Oxford, England), 23(24), 3304–3311.CrossRefGoogle Scholar
  45. 45.
    Liu, J., et al. (2005). A method for aligning RNA secondary structures and its application to RNA motif detection. BMC Bioinformatics, 6, 89.CrossRefGoogle Scholar
  46. 46.
    Macke, T. J., et al. (2001). RNAMotif, an RNA secondary structure definition and search algorithm. Nucleic Acids Research, 29(22), 4724–4735.CrossRefGoogle Scholar
  47. 47.
    Matsui, H., Sato, K., & Sakakibara, Y. (2004). Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures. Proceedings/IEEE Computational Systems Bioinformatics Conference, CSB. IEEE Computational Systems Bioinformatics Conference (pp. 290–9).Google Scholar
  48. 48.
    Meyer, I. M., & Miklós, I. (2007). SimulFold: Simultaneously inferring RNA structures including pseudoknots, alignments, and trees using a Bayesian MCMC framework. PLoS Computational Biology, 3(8), e149.CrossRefGoogle Scholar
  49. 49.
    Mignone, F., et al. (2005). UTRdb and UTRsite: A collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucleic Acids Research, 33(suppl_1), D141–D146.Google Scholar
  50. 50.
    Moretti, S., et al. (2007). R-Coffee: A web server for accurately aligning noncoding RNA sequences. Nucleic Acids Research, 36(Web Server issue), W10–W13.Google Scholar
  51. 51.
    Pavesi, G., et al. (2004). RNAProfile: An algorithm for finding conserved secondary structure motifs in unaligned RNA sequences. Nucleic Acids Research, 32(10), 3258–3269.CrossRefGoogle Scholar
  52. 52.
    Pedersen, J. S., et al. (2006). Identification and classification of conserved RNA secondary structures in the human genome. PLoS Computational Biology, 2(4), e33.CrossRefGoogle Scholar
  53. 53.
    Pesole, G., & Liuni, S. (1999). Internet resources for the functional analysis of 5′ and 3′ untranslated regions of eukaryotic mRNAs. Trends in Genetics: TIG, 15(9), 378.CrossRefGoogle Scholar
  54. 54.
    Reeder, J., Reeder, J., & Giegerich, R. (2007). Locomotif: From graphical motif description to RNA motif search. Bioinformatics (Oxford, England), 23(13), i392–i400.CrossRefGoogle Scholar
  55. 55.
    Rivas, E., & Eddy, S. R. (2001). Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics, 2, 8.CrossRefGoogle Scholar
  56. 56.
    Rocheleau, L., & Pelchat, M. (2006). The subviral RNA Database: A toolbox for viroids, the hepatitis delta virus and satellite RNAs research. BMC Microbiology, 6, 24.CrossRefGoogle Scholar
  57. 57.
    Ruan, J., Stormo, G. D., & Zhang, W. (2004). An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots. Bioinformatics (Oxford, England), 20(1), 58–66.CrossRefGoogle Scholar
  58. 58.
    Sakakibara, Y. (2003). Pair hidden Markov models on tree structures. Bioinformatics (Oxford, England), 19(Suppl 1), i232–i240.CrossRefGoogle Scholar
  59. 59.
    Sakakibara, Y., et al. (2007). Stem kernels for RNA sequence analyses. Journal of Bioinformatics and Computational Biology, 5(5), 1103–1122.CrossRefGoogle Scholar
  60. 60.
    Siebert, S., & Backofen, R. (2005). MARNA: Multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons. Bioinformatics (Oxford, England), 21(16), 3352–3359.CrossRefGoogle Scholar
  61. 61.
    Steffen, P., et al. (2006). RNAshapes: An integrated RNA analysis package based on abstract shapes. Bioinformatics (Oxford, England), 22(4), 500–503.CrossRefGoogle Scholar
  62. 62.
    Tabei, Y., et al. (2007). A fast structural multiple alignment method for long RNA sequences. BMC Bioinformatics, 9, 33.CrossRefGoogle Scholar
  63. 63.
    Thébault, P., et al. (2006). Searching RNA motifs and their intermolecular contacts with constraint networks. Bioinformatics (Oxford, England), 22(17), 2074–2080.CrossRefGoogle Scholar
  64. 64.
    Touzet, H. (2007). Comparative analysis of RNA genes: The caRNAc software. Methods in Molecular Biology (Clifton, N.J.), 395, 465–474.Google Scholar
  65. 65.
    Veksler-Lublinsky, I., et al. (2007). A structure-based flexible search method for motifs in RNA. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology, 14(7), 908–926.Google Scholar
  66. 66.
    Washietl, S., Hofacker, I. L., & Stadler, P. F. (2005). Fast and reliable prediction of noncoding RNAs. Proceedings of the National Academy of Sciences of the United States of America, 102(7), 2454–2459.CrossRefGoogle Scholar
  67. 67.
    Will, S., et al. (2007). Inferring noncoding RNA families and classes by means of genome-scale structure-based clustering. PLoS Computational Biology, 3(4), e65.CrossRefGoogle Scholar
  68. 68.
    Wilm, A., Higgins, D. G., & Notredame, C. (2007). R-Coffee: A method for multiple alignment of non-coding RNA. Nucleic Acids Research, 36(9), e52.CrossRefGoogle Scholar
  69. 69.
    Wilm, A., Linnenbrink, K., & Steger, G. (2007). ConStruct: Improved construction of RNA consensus structures. BMC Bioinformatics, 9, 219.CrossRefGoogle Scholar
  70. 70.
    Xie, J., et al. (2007). Sno/scaRNAbase: A curated database for small nucleolar RNAs and cajal body-specific RNAs. Nucleic Acids Research, 35(Database issue), D183–D187.CrossRefGoogle Scholar
  71. 71.
    Xu, X., Ji, Y., & Stormo, G. D. (2007). RNA sampler: A new sampling based algorithm for common RNA secondary structure prediction and structural alignment. Bioinformatics (Oxford, England), 23(15), 1883–1891.CrossRefGoogle Scholar
  72. 72.
    Xue, C., & Liu, G. (2007). RScan: Fast searching structural similarities for structured RNAs in large databases. BMC Genomics, 8, 257.CrossRefGoogle Scholar
  73. 73.
    Yao, Z., Weinberg, Z., & Ruzzo, W. L. (2006). CMfinder—A covariance model based RNA motif finding algorithm. Bioinformatics (Oxford, England), 22(4), 445–452.CrossRefGoogle Scholar
  74. 74.
    Zhang, S., et al. (2005). Searching genomes for noncoding RNA using FastR. IEEE/ACM Transactions on Computational Biology and Bioinformatics/IEEE, ACM, 2(4), 366–379.CrossRefGoogle Scholar
  75. 75.
    Zhou, Y., et al. (2007). GISSD: Group I Intron Sequence and Structure Database. Nucleic Acids Research, 36(Database issue), D31–D37.CrossRefGoogle Scholar

Copyright information

© Humana Press 2008

Authors and Affiliations

  1. 1.Gen*NY*Sis Center for Excellence in Cancer Genomics, Department of Biomedical Sciences, School of Public HealthUniversity at Albany-SUNYRensselaerUSA

Personalised recommendations