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Journal of Mathematical Biology

, Volume 56, Issue 1–2, pp 15–49 | Cite as

Computational methods in noncoding RNA research

  • Ariane Machado-Lima
  • Hernando A. del Portillo
  • Alan Mitchell Durham
Article

Abstract

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure.

Keywords

Review Noncoding RNAs Secondary structure prediction Structure comparison Gene finding 

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References

  1. 1.
    Abrahams J.P., van den Berg M., van Batenburg E., Pleij C. (1990). Prediction of RNA secondary structure, including pseudoknotting, by computer simulation. Nucleic Acids Res. 18(10): 3035–3044 Google Scholar
  2. 2.
    Akmaev V.R., Kelley S.T., Stormo G.D. (2000). Phylogenetically enhanced statistical tools for RNA structure prediction. Bioinformatics 16(6): 501–512 Google Scholar
  3. 3.
    Allali J., Sagot M.F. (2005). A new distance for high level RNA secondary structure comparison. Trans. Comput. Biol. Bioinform. 2(1): 3–14 Google Scholar
  4. 4.
    Bafna V., Tang H., Zhang S. (2006). Consensus folding of unaligned RNA sequences revisited. J. Comput. Biol. 13(2): 283–295 MathSciNetGoogle Scholar
  5. 5.
    Bafna, V., Zhang, S.: Fast, R.: Fast database search tool for non-coding RNA. In: Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference (CSB2004) (2004)Google Scholar
  6. 6.
    Barash D. (2004). Second eigenvalue of the Laplacian matrix for predicting RNA conformational switch by mutation. Bioinformatics 20(12): 1861–1869 Google Scholar
  7. 7.
    di Bernardo D., Down T., Hubbard T. (2003). ddbRNA: detection of conserved secondary structures in multiple alignments. Bioinformatics 19(13): 1606–1611 Google Scholar
  8. 8.
    Bernhart S.H., Hofacker I.L., Stadler P.F. (2006). Local RNA base pairing probabilities in large sequences. Bioinformatics 22(5): 614–615 Google Scholar
  9. 9.
    Blackburn, E.H.: Telomerase (1993) The RNA World. Cold Spring Harbor Laboratory Press, New YorkGoogle Scholar
  10. 10.
    Bonhoeffer S., McCaskill J.S., Stadler P.F., Schuster P. (1993). RNA multi-structure landscapes—a study based on temperature dependent partition functions. Eur. Biophys. J. 22(1): 13–24 Google Scholar
  11. 11.
    Bonnet E., Wuyts J., Rouze P., de Peer Y.V. (2004). Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 20(17): 2911–2917 Google Scholar
  12. 12.
    Bouthinon D., Soldano H. (1999). A new method to predict the consensus secondary structure of a set of unaligned RNA sequences. Bioinformatics 15(10): 785–798 Google Scholar
  13. 13.
    Brown J.W. (1999). The ribonuclease P database. Nucleic Acids Res. 27(1): 314 Google Scholar
  14. 14.
    Brown M.P.S. (2000). Small subunit ribosomal RNA modeling using stochastic context-free grammars. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8: 57–66 Google Scholar
  15. 15.
    Brown, M.P.S., Wilson, C.: RNA pseudoknot modeling using intersections of stochastic context free grammars with applications to database search. Pacif Symposium on Biocomputing, pp. 109–125 (1996)Google Scholar
  16. 16.
    Chan C.Y., Lawrence C.E., Ding Y. (2005). Structure clustering features on the Sfold web server. Bioinformatics 21(20): 3926–3928 Google Scholar
  17. 17.
    Chen J.H., Le S.Y., Maizel J.V. (2000). Prediction of common secondary structures of RNAs: a genetic algorithm approach. Nucleic Acids Res. 28(4): 991–999 Google Scholar
  18. 18.
    Chen S.J., Dill K.A. (2000). RNA folding energy landscapes. Proc. Natl Acad. Sci. 97(2): 646–651 Google Scholar
  19. 19.
    Chiang, D., Joshi, A.K.: Formal grammars for estimating partition functions of double-stranded chain molecules. In: Proceedings of HLT 2002, San Diego, March, pp. 63–67 (2002)Google Scholar
  20. 20.
    Churkin, A., Barash, D.: RNAmute: RNA secondary structure mutation analysis tool. BMC Bioinformatics 7(221) (2006)Google Scholar
  21. 21.
    Clote P. (2005). An efficient algorithm to compute the landscape of locally optimal RNA secondary structures with respect to the Nussinov–Jacobson energy model. J. Comput. Biol. 12(1): 83–101 MathSciNetGoogle Scholar
  22. 22.
    Clote P. (2005). RNALOSS: a web server for RNA locally optimal secondary structures. Nucleic Acids Res. 33: 600–604 Google Scholar
  23. 23.
    Clote P., Ferre F., Kranakis E., Krizanc D. (2005). Structural RNA has lower folding energy than random RNA of the same dinucleotide frequency. RNA 11: 578–591 Google Scholar
  24. 24.
    Cole J.R., Chai B., Marsh T.L., Farris R.J., Wang Q., Kulam S.A., Chandra S., McGarell D.M., Schmidt T.M., Garrity G.M., Tiedje J.M. (2003). The Ribosomal Database Project (RDP-II): previewing a new autoaligner that allows regular updates and the new prokaryotic taxonomy. Nucleic Acids Res. 31(1): 442–443 Google Scholar
  25. 25.
    Cormen T.H., Leiserson C.E., Rivest R.L. (1990). Introduction to Algorithms. MIT Press, Cambridge Google Scholar
  26. 26.
    Coventry, A., Kleitman, D.J., Berger, B.: MSARI: multiple sequence alignments for statistical detection of RNA secondary structure. Proc. Natl Acad. Sci. 101(33), 12, 102–12, 107 (2004)Google Scholar
  27. 27.
    Cupal J., Hofacker I.L., Stadler P.F. (1996). Dynamic programming algorithm for the density of states of RNA secondary structures. Comput. Sci. Biol. 96: 184–186 Google Scholar
  28. 28.
    Danilova L.V., Pervouchine D.D., Favorov A.V., Mironov A.A. (2006). RNAKINETICS: a web server that models secondary structure kinetics of an elongating RNA. J. Bioinform. Comput. Biol. 4(2): 589–596 Google Scholar
  29. 29.
    Ding Y. (2006). Statistical and bayesian approaches to RNA secondary structure prediction. RNA 12: 323–331 Google Scholar
  30. 30.
    Ding Y., Chan C.Y., Lawrence C.E. (2004). Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res. 32(Web Server issue): W135–W141 Google Scholar
  31. 31.
    Ding Y., Lawrence C.E. (2003). A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res. 31(24): 7280–7301 Google Scholar
  32. 32.
    Dirks R.M., Pierce N.A. (2003). A partition function algorithm for nucleic acids secondary structure including pseudoknots. J. Comput. Chem. 24(13): 1664–1677 Google Scholar
  33. 33.
    Dirks R.M., Pierce N.A. (2004). An algorithm for computing nucleic acid base-pairing probabilities including pseudoknots. J. Comput. Chem. 25: 1295–1304 Google Scholar
  34. 34.
    Do C.B., Woods D.A., Batzoglou S. (2006). CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22(14): e90–e98 Google Scholar
  35. 35.
    Dowell, R.D.: RNA structural alignment using stochastic context-free grammars. Ph.D. Thesis (2004)Google Scholar
  36. 36.
    Dowell R.D., Eddy S.R. (2006). Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints. BMC Bioinformatics 7: 400 Google Scholar
  37. 37.
    Eddy S.R. (2001). Non-coding RNA genes and the modern RNA world. Nat. Rev. 2: 919–929 Google Scholar
  38. 38.
    Eddy S.R. (2002). Computational genomics of noncoding RNA genes. Cell 109: 137–140 Google Scholar
  39. 39.
    Eddy S.R. (2002). A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics 3(1): 18 Google Scholar
  40. 40.
    Eddy S.R. (2004). How do RNA folding algorithms work. Nat. Biotechnol. 22(11): 1457–1458 Google Scholar
  41. 41.
    Eddy, S.R., Durbin, R.: RNA sequence analysis using covariance models. Nucleic Acids Res., 2079–2088 (1994)Google Scholar
  42. 42.
    Fichant G.A., Burks C. (1991). Identifying potential tRNA genes in genomic DNA sequences. J. Mol. Biol. 220: 659–671 Google Scholar
  43. 43.
    Flamm C., Fontana W., Hofacker I.L., Schuster P. (2000). RNA folding at elementary step resolution. RNA 6: 325–338 Google Scholar
  44. 44.
    Higgings D.G., Thompson J.D., Gibson T.J. (1996). Using CLUSTAL for multiple sequence alignments. Methods Enzymol. 266: 383–402 Google Scholar
  45. 45.
    Gan H.H., Fera D., Zorn J., Shiffeldrim N., Tang M., Laserson U., Kim N., Schlick T. (2004). RAG: RNA-As-Graphs database—concepts, analysis, and features. Bioinformatics 20(8): 1285–1291 Google Scholar
  46. 46.
    Gorodkin J., Heyer L.J., Stormo G.D. (1997). Finding the most significant common sequence and structure motifs in a set of RNA sequences. Nucleic Acids Res. 25(18): 3724–3732 Google Scholar
  47. 47.
    Gorodkin J., Stricklin S.L., Stormo G.D. (2001). Discovering common stem-loop motifs in unaligned RNA sequences. Nucleic Acids Res. 29(10): 2135–2144 Google Scholar
  48. 48.
    Greider, C.: Telomerase biochemistry and regulation (1995) In: Telomeres. Cold Spring Harbor Laboratory Press, New YorkGoogle Scholar
  49. 49.
    Griffiths-Jones S., Bateman A., Marshall M., Khanna A., Eddy S.R. (2003). Rfam: an RNA family database. Nucleic Acids Res. 31(1): 439–441 Google Scholar
  50. 50.
    Griffiths-Jones S., Moxon S., Marshall M., Khanna A., Eddy S.R., Bateman A. (2005). Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33: D121–D124 Google Scholar
  51. 51.
    Gulko, B., Haussler, D.: Using multiple alignment and phylogenetic trees to detect RNA secondary structure. Pacific Symposium on Biocomputing, pp. 350–367 (1996)Google Scholar
  52. 52.
    Haebel P., Gutmann S., Ban N. (2004). Dial tm for rescue: tmRNA engages ribosomes stalled on defective mRNAs. Curr. Opin. Struct. Biol. 14: 58–65 Google Scholar
  53. 53.
    Hannon G.J. (2002). RNA interference. Nature 418: 244–251 Google Scholar
  54. 54.
    Havgaard J.H., Lyngso R., Stormo G.D., Gorodkin J. (2005). Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%. Bioinformatics 21(9): 1815–1824 Google Scholar
  55. 55.
    Herbel J., Stadler P.F. (2006). Hairpins in a haystack: recognizing microRNA precursors in comparative genomics data. Bioinformatics 22(14): 197–202 Google Scholar
  56. 56.
    Higgs P.G. (2000). RNA secondary structure: physical and computational aspects. Q. Rev. Biophys. 33(3): 199–253 Google Scholar
  57. 57.
    Hochsmann, M., Toller, T., Giegerich, R., Kurtz, S.: Local similarity in RNA secondary structures. In: Proceedings of the Computational Systems Bioinformatics (CSB 2003), 159–168 (2003)Google Scholar
  58. 58.
    Hochsmann M., Voss B., Giegerich R. (2004). Pure multiple RNA secondary structure alignments: a progressive profile approach. IEEE Trans. Comput. Biol. Bioinform. 1(1): 53–62 Google Scholar
  59. 59.
    Hofacker I.L. (2003). Vienna RNA secondary structure server. Nucleic Acids Res. 31(13): 3429–3431 Google Scholar
  60. 60.
    Hofacker I.L., Benhart S.H.F., Stadler P.F. (2004). Alignment of RNA base pairing probability matrices. Bioinformatics 20(14): 2222–2227 Google Scholar
  61. 61.
    Hofacker I.L., Fekete M., Stadler P.F. (2002). Secondary structure prediction for aligned RNA sequences. J. Mol. Biol. 319: 1059–1066 Google Scholar
  62. 62.
    Hofacker I.L., Fontana W., Stadler P.F., Bonhoeffer L.S., Tacker M., Schuster P. (1994). Fast folding and comparison of RNA secondary structures. Monatsh. Chem. 125: 167–188 Google Scholar
  63. 63.
    Holmes, I.: Accelerated probabilistic inference of RNA struture evolution. BMC Bioinformatics 6(73) (2005). doi:10.1186/1471-2105-6-73Google Scholar
  64. 64.
    Holmes, I., Rubin, G.M.: Pairwise RNA structure comparison with SCFGs. Pacif Symposium on Biocomputing, pp. 163–174 (2002)Google Scholar
  65. 65.
    Huttenhofer A., Schattner P., Polacek N. (2005). Non-coding RNAs: hope or hype. Trends Genet. 21(5): 289–297 Google Scholar
  66. 66.
    James B.D., Olsen G.J., Pace N.R. (1989). Phylogenetic comparative analysis of RNA secondary structure. Methods Enzymol. 180: 227–239 CrossRefGoogle Scholar
  67. 67.
    Ji Y., Xu X., Stormo G.D. (2004). A graph theoretical approach for predicting common RNA secondary structure motifs including psudoknots in unaligned sequences. Bioinformatics 20(10): 1591–1602 Google Scholar
  68. 68.
    Jiang T., Lin G., Ma B., Zhang K. (2002). A general edit distance between RNA structures. J. Comput. Biol. 9: 371–388 Google Scholar
  69. 69.
    Jiang T., Wang L., Zhang K. (1995). Alignment of trees—an alternative to tree edit. Theor. Comput. Sci. 143: 137–148 MATHMathSciNetGoogle Scholar
  70. 70.
    Juan V., Wilson C. (1999). RNA secondary structure prediction based on free energy and phylogenetic analysis. J. Mol. Biol. 289: 935–947 Google Scholar
  71. 71.
    Just W. (2001). Computational complexity of multiple sequence alignment with SP-score. J. Comput. Biol. 8(6): 615–623 MathSciNetGoogle Scholar
  72. 72.
    Keenan R.J., Freymann D.M., Stroud R.M., Walter P. (2001). The signal recognition particle. Annu. Rev. Biochem. 70: 755–775 Google Scholar
  73. 73.
    Klein R.J., Eddy S.R. (2003). RSEARCH: finding homologs of single structured RNA sequences. BMC Bioinformatics 4(1): 44 Google Scholar
  74. 74.
    Klein R.J., Misulovin Z., Eddy S.E. (2002). Noncoding RNA genes identified in AT-rich hyperthermophiles. Proc. Natl Acad. Sci. 99(11): 7542–7547 Google Scholar
  75. 75.
    Knight R., Birmingham A., Yarus M. (2004). BayesFold: rational 2o folds that combine thermodynamic, covariation, and chemical data for aligned RNA sequences. RNA 10: 1323–1336 Google Scholar
  76. 76.
    Knudsen B., Hein J. (1999). RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics 15(6): 446–454 Google Scholar
  77. 77.
    Knudsen B., Hein J. (2003). Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Res. 31(13): 3423–3428 Google Scholar
  78. 78.
    Krogh A., Brown M., Mian I.S., Sjolander K., Haussler D. (1994). Hidden markov models in computational biology—applications to protein modeling. J. Mol. Biol. 235: 1501–1531 Google Scholar
  79. 79.
    Lagos-Quintana M., Rauhut R., Lendeckel W., Tuschl T. (2001). Identification of novel genes coding for small expressed RNAs. Science 294: 853–858 Google Scholar
  80. 80.
    Lai E.C., Tomancak P., Williams R.W., Rubin G.M. (2003). Computational identification of Drosophila microRNA genes. Genome Biol. 4: R42.1–R42.20 Google Scholar
  81. 81.
    Laslett D., Canback B. (2004). ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 32(1): 11–16 Google Scholar
  82. 82.
    Laslett D., Canback B., Andersson S. (2002). BRUCE: a program for the detection of transfer-messenger RNA genes in nucleotide sequences. Nucleic Acids Res. 30(15): 3449–3453 Google Scholar
  83. 83.
    Le S.V., Chen J.H., Currey K.M., Maizel J.V.J. (1988). A program for predicting significant RNA secondary structures. Comput. Appl. Biosci. 4(1): 153–159 Google Scholar
  84. 84.
    Lim L.P., Lau N.C., Weinstein E.G., Abdelhakim A., Yekta S., Rhoades M.W., Burge C.B., Bartel D.P. (2003). The microRNAs of Caenorhabditis elegans. Genes Dev. 17: 991–1008 Google Scholar
  85. 85.
    Liu C., Bai B., Skogerbo G., Cai L., Deng W., Zhang Y., Bu D., Zhao Y., Chen R. (2005). NONCODE: an integrated knowledge database of non-coding RNAs. Nucleic Acids Res. 33: D112–D115 Google Scholar
  86. 86.
    Liu J., Gough J., Rost B. (2006). Distinguishing protein-coding from non-coding RNAs through support vector machines. PLoS Genet. 2(4): e29 Google Scholar
  87. 87.
    Lowe T.M., Eddy S.R. (1997). tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25(5): 955–964 Google Scholar
  88. 88.
    Lowe T.M., Eddy S.R. (1999). A computational screen for methylation guide snoRNAs in yeast. Science 283: 1168–1171 Google Scholar
  89. 89.
    Lowe, T.M.J.: Combining new computational and traditional experimental methods to identify tRNA and snoRNA gene families. Master’s thesis, Washington University (1999)Google Scholar
  90. 90.
    Luck R., Graf S., Steger G. (1999). ConStruct: a tool for thermodynamic controlled prediction of conserved structure. Nucleic Acids Res. 27(21): 4208–4217 Google Scholar
  91. 91.
    Lyngso R.B., Pedersen C.N. (2000). RNA pseudoknot prediction in energy-based models. J. Comput. Biol. 7: 409–427 Google Scholar
  92. 92.
    Mathews D.H. (2005). Predicting a set of minimal free energy RNA secondary structures common to two sequences. Bioinformatics 21(10): 2246–2253 Google Scholar
  93. 93.
    Mathews D.H., Disney M.D., Childs J.L., Schroeder S.J., Zuker M., Turner D.H. (2004). Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. 101(19): 7287–7292 Google Scholar
  94. 94.
    Mathews D.H., Turner D.H. (2002). Dynalign: an algorithm for finding the secondary structure common to two RNA sequences. J. Mol. Biol. 317: 191–203 Google Scholar
  95. 95.
    Mattick, J.S., Makunin, I.V.: Non-coding RNA. Human Mol. Genet. 15(1), 17–29 (2006)Google Scholar
  96. 96.
    McCaskill J.S. (1990). The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 29: 1105–1119 Google Scholar
  97. 97.
    Meyer, I.M., Miklos, I.: Co-transcriptional folding is encoded within RNA genes. BMC Mol. Biol. 5(10) (2004). doi:10.1186/1471-2199-5-10Google Scholar
  98. 98.
    Militello, K.T., Patel, V., Chessler, A.D., Fisher, J.K., Kasper, J.M., Gunasekera, A., Wirth, D.F.: RNA polymerase II synthesizes antisense RNA in Plasmodium falciparum. RNA 11 (2005)Google Scholar
  99. 99.
    Moulton V. (2005). Tracking down noncoding RNAs. Proc. Natl Acad. Sci. 102(7): 2269–2270 Google Scholar
  100. 100.
    Nam J.W., Kim J., Kim S.K., Zhang B.T. (2006). ProMIR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res. 34: 455–458 Google Scholar
  101. 101.
    Notredame C., Brien E.A.O., Higgins D.G. (1997). RAGA: RNA sequence alignment by genetic algorithm. Nucleic Acids Res. 25(22): 4570–4580 Google Scholar
  102. 102.
    Notredame C., Higgins D.G., Heringa J. (2000). T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302(1): 205–217 Google Scholar
  103. 103.
    de Novoa P.G., Williams K.P. (2004). The tmRNA website: reductive evolution of tmRNA in plastids and other endosymbionts. Nucleic Acids Res. 32: D104–D108 Google Scholar
  104. 104.
    Nussinov R., Pieczenik G., Griggs J.R., Kleitman D.J. (1978). Algorithms for loop matchings. SIAM J. Appl. Math. 35(1): 68–82 MATHMathSciNetGoogle Scholar
  105. 105.
    Pavesi A., Conterio F., Bolchi A., Dieci G., Ottonello S. (1994). Identification of new eukaryotic tRNA genes in genomic DNA databases by a multistep weight matrix analysis of transcriptional control regions. Nucleic Acids Res. 22(7): 1247–1256 Google Scholar
  106. 106.
    Pedersen J.S., Meyer I.M., Forsberg R., Simmonds P., Hein J. (2004). A comparative method for finding and folding RNA secondary structures withing protein-coding regions. Nucleic Acids Res. 32(16): 4925–4936 Google Scholar
  107. 107.
    Perriquet O., Touzet H., Dauchet M. (2003). Finding the common structure shared by two homologous RNAs. Bioinformatics 19(1): 108–116 Google Scholar
  108. 108.
    Piccinelli P., Rosenblad M.A., Samuelsson T. (2005). Identification and analysis of ribonuclease P and MRP RNA in a broad range of eukaryotes. Nucleic Acids Res. 33(14): 4485–4495 Google Scholar
  109. 109.
    Pipas J.M., McMahon J.E. (1975). Method for predicting RNA secondary structure. Proc. Natl Acad. Sci. 72(6): 2017–2021 Google Scholar
  110. 110.
    Reeder, J., Giegerich, R.: Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics. BMC Bioinformatics 5(104) (2004)Google Scholar
  111. 111.
    Reeder J., Hochsmann M., Rehmsmeier M., Voss B., Giegerich R. (2006). Beyond mfold: recent advances in RNA bioinformatics. J. Biotechnol. 124(1): 41–55 Google Scholar
  112. 112.
    Regalia M., Rosenblad M.A., Samuelsson T. (2002). Prediction of signal recognition particle RNA genes. Nucleic Acids Res. 30(15): 3368–3377 Google Scholar
  113. 113.
    Reis E.M., Louro R., Nakaya H.I., Verjovski-Almeida S. (2005). As antisense RNA gets intronic. OMICS 9(1): 2–12 Google Scholar
  114. 114.
    Reis E.M., Nakaya H.I., Louro R., Canavez F.C., Flatschart A.V., Almeida G.T., Egidio C.M., Paquola A.C., Machado A.A., Festa F., Yamamoto D., Alvarenga R., da Silva C.C., Brito G.C., Simon S.D., Moreira-Filho C.A., Leite K.R., Camara-Lopes L.H., Campos F.S., Gimba E., Vignal G.M., El-Dorry H., Sogayar M.C., Barcinski M.A., da Silva A.M., Verjovski-Almeida S. (2004). Antisense intronic non-coding RNA levels correlate to the degree of tumor differentiation in prostate cancer. Oncogene 23(39): 6684–6692 Google Scholar
  115. 115.
    Ren J., Rastegari B., Condon A., Hoos H. (2005). HotKnots: heuristic prediciton of RNA secondary structures including pseudoknots. RNA 11: 1419–1504 Google Scholar
  116. 116.
    Rivas E. (2005). Evolutionary models for insertions and deletions in a probabilistic modeling framework. BMC Bioinformatics 6: 63 Google Scholar
  117. 117.
    Rivas E., Eddy S.R. (1999). A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 285: 2053–2068 Google Scholar
  118. 118.
    Rivas E., Eddy S.R. (2000). Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs. Bioinformatics 16(7): 583–605 Google Scholar
  119. 119.
    Rivas E., Eddy S.R. (2001). Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics 2(1): 8 Google Scholar
  120. 120.
    Rosenblad M.A., Gorodkin J., Knudsen B., Zwieb C., Samuelsson T. (2003). SRPDB: signal recognition particle database. Nucleic Acids Res. 31(1): 363–364 Google Scholar
  121. 121.
    Ruan J., Stormo G.D., Zhang W. (2004). ILM: a web server for predicting RNA secondary structures with pseudoknots. Nucleic Acids Res. 32(Web Server issue): W146–W149 Google Scholar
  122. 122.
    Ruan J., Stormo G.D., Zhang W. (2004). An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots. Bioinformatic 20(1): 58–66 Google Scholar
  123. 123.
    Sakakibara, Y., Brown, M.: The application of stochastic context-free grammars to folding, aligning and modeling homologous RNA sequences (1993). Techn. Rep. UCSC-CRL-94-14Google Scholar
  124. 124.
    Sakakibara, Y., Brown, M., Hughey, R., Mian, I.S., Sjolander, K., Underwood, R.C., Haussler, D.: Stochastic context-free grammars for tRNA modeling. Nucleic Acids Res., 5112–5120 (1994)Google Scholar
  125. 125.
    Sankoff D. (1985). Simultaneous solution of the RNA folding, alignment and protosequence problems. SIAM J. Appl. Math. 45(5): 810–825 MATHMathSciNetGoogle Scholar
  126. 126.
    Schattner P. (2002). Searching for RNA genes using base-composition statistics. Nucleic Acids Res. 30(9): 2076–2082 Google Scholar
  127. 127.
    Schattner P., Brooks A.N., Lowe T.M. (2005). The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs. Nucleic Acids Res. 33: 686–689 Google Scholar
  128. 128.
    Schattner P., Decatur W.A., Davis C.A., Ares M.J., Fournier M.J., Lowe T.M. (2004). Genome-wide searching for pseudouridylation guide snoRNAs: analysis of Saccharomyces cerevisiae genome. Nucleic Acids Res. 32(14): 4281–4296 Google Scholar
  129. 129.
    Schmitz M., Steger G. (1996). Description of RNA folding by simulated annealing. J. Mol. Biol. 255: 254–266 Google Scholar
  130. 130.
    Sczyrba A., Kruger J., Mersch H., Kurtz S., Giegerich R. (2003). RNA-related tools on the Bielefeld bioinformatics server. Nucleic Acids Res. 31(13): 3767–3770 Google Scholar
  131. 131.
    Searls D.B. (2002). The language of genes. Nature 420: 211–217 Google Scholar
  132. 132.
    Shapiro B.A., Zhang K. (1990). Comparing multiple RNA secondary structures using tree comparisons. Comput. Appl. Biosci. 6(4): 309–318 Google Scholar
  133. 133.
    Siebert S., Backofen R. (2005). MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons. Bioinformatics 21(16): 3352–3359 Google Scholar
  134. 134.
    Steffen P., Voss B., Rehmsmeier M., Reeder J., Giegerich R. (2006). RNAshapes: an integrated RNA analysis package based on abstract shapes. Bioinformatics 22(4): 500–503 Google Scholar
  135. 135.
    Storz G. (2002). An expandind universe of noncoding RNAs. Science 296: 1260–1263 Google Scholar
  136. 136.
    Tabaska J.E., Cary R.B., Gabow H.N., Stormo G.D. (1998). An RNA folding method capable of identifying pseudoknots and base triples. Bioinformatics 14(8): 691–699 Google Scholar
  137. 137.
    Taneda A. (2005). Cofolga: a genetic algotithm for finding the common folding of two RNAs. Comput. Biol. Chem. 29: 111–119 MATHGoogle Scholar
  138. 138.
    Tinoco I.J., Uhlenbeck O.C., Levine M.D. (1971). Estimation of secondary structure in ribonucleic acids. Nature 230(5293): 362–367 Google Scholar
  139. 139.
    Touzet H., Perriquet O. (2004). CARNAC: folding families of related RNAs. Nucleic Acids Res. 32: W142–W145 Google Scholar
  140. 140.
    Tsui V., Macke T., Case D.A. (2003). A novel method for finding tRNA genes. RNA 9: 507–517 Google Scholar
  141. 141.
    Turner D.H., Sugimoto N. (1988). RNA structure prediction. Annu. Rev. Biophys. Biophys. Chem. 17: 167–192 Google Scholar
  142. 142.
    Underwood, R.C.: Stochastic Context-Free Grammars for Modeling Three Spliceosomal Small Nuclear Ribonucleic Acids. Master’s thesis, Baskin Center for Computer Engineering and Information Sciences, University of California (1994)Google Scholar
  143. 143.
    Uzilov, A.V., Keegan, J.M., Mathews, D.H.: Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change. BMC Bioinformatics 7 (2006). doi:10.1186/1417-2105-7-173Google Scholar
  144. 144.
    Voss, B., Giegerich, R., Rehmsmeier, M.: Complete probabilistic analysis of RNA shapes. BMC Biology 4(5) (2006)Google Scholar
  145. 145.
    Wang C., Ding C., Meraz R.F., Holbrook S.R. (2006). PSoL: a positive sample only learning algorithm for finding ncRNA genes. Bioinformatics 22(21): 2590–2596 Google Scholar
  146. 146.
    Wang X., Zhang J., Li F., Gu J., He T., Zhang X., Li Y. (2005). MicroRNA identification based on sequence and structure alignment. Bioinformatics 21(18): 3610–3614 Google Scholar
  147. 147.
    Washiet S., Hofacker I.L. (2004). Consensus folding of aligned sequences as a new measure for the detection of functional RNAs by comparative genomics. J. Mol. Biol. 342: 19–30 Google Scholar
  148. 148.
    Washietl S., Hofacker I.L., Stadler P.F. (2005). Fast and reliable prediction of noncoding RNAs. Proc. Natl Acad. Sci. 102(7): 2454–2459 Google Scholar
  149. 149.
    Waterman M.S., Smith T.F. (1978). RNA secondary structure: a complete mathematical analysis. Math. Biosci. 42: 257–266 MATHGoogle Scholar
  150. 150.
    Weinberg Z., Ruzzo W.L. (2004). Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy. Bioinformatics 20(suppl 1): i334–i341 Google Scholar
  151. 151.
    Weinberg Z., Ruzzo W.L. (2006). Sequence-based heuristics for faster annotation of non-coding RNA families. Bioinformatics 22(1): 35–39 Google Scholar
  152. 152.
    Workman C., Krogh A. (1999). No evidence that mRNAs have lower folding free energies than random sequences with the same dinucleotide distribution. Nucleic Acids Res. 27(24): 4816–4822 Google Scholar
  153. 153.
    Wuchty S., Fontana W., Hofacker I.L., Schuster P. (1999). Complete suboptimal folding of RNA and the stability of secondary structure. Biopolymers 49: 145–165 Google Scholar
  154. 154.
    Yang, J.H., Zhang, X.C., Huang, Z.P., Zhou, H., Huang, M.B., Zhang, S., Chen, Y.Q., Qu, L.H.: snoSeeker: an advanced computational package for screening of guide and orphan sno RNA genes in the human genome. Nucleic Acids Res (2006). doi:10.1093/nar/gkl672Google Scholar
  155. 155.
    Yang Z., Zhu Q., Luo K., Zhou Q. (2001). The 7SK small nuclear RNA inhibits the CDK9/cyclin T1 kinase to control transcription. Nature 414: 317–322 Google Scholar
  156. 156.
    Ying X., Luo H., Luo J., Li W. (2004). RDfolder: a web server for prediction of RNA secondary structure. Nucleic Acids Res. 32(Web Server issue): W150–W153 Google Scholar
  157. 157.
    Zuker M. (1989). On finding all suboptimal foldings of an RNA molecule. Science 244: 48–52 MathSciNetGoogle Scholar
  158. 158.
    Zuker M. (2003). Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31(13): 3406–3415 Google Scholar
  159. 159.
    Zuker, M., Mathews, D.H., Turner, D.H.: Algorithms and thermodynamics for RNA secondary structure prediction: a practical guide. RNA Biochem. Biotechnol. 11–43 (1999)Google Scholar
  160. 160.
    Zuker M., Stiegler P. (1981). Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9(1): 133–148 Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Ariane Machado-Lima
    • 1
  • Hernando A. del Portillo
    • 2
    • 3
  • Alan Mitchell Durham
    • 1
  1. 1.Institute of Mathematics and StatisticsUniversity of Sao PauloSao PauloBrazil
  2. 2.Institute of Biomedical SciencesUniversity of Sao PauloSao PauloBrazil
  3. 3.Barcelona Centre for International Health Research CRESIBBarcelonaSpain

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