Abstract
Among the most powerful and commonly used methods for finding new members of non-coding RNA gene families in genomic data are covariance models. The parameters of these models are estimated from the observed position-specific frequencies of insertions, deletions, and mutations in a multiple alignment of known non-coding RNA family members. Since the vast majority of positions in the multiple alignment have no observed changes, yet there is no reason to rule them out, some form of prior is applied to the estimate. Currently, observed-frequency priors are generated from non-family members based on model node type and child node type allowing for some differentiation between priors for loops versus helices and between internal segments of structures and edges of structures. In this work it is shown that parameter estimates might be improved when thermodynamic data is combined with the consensus structure/sequence and observed-frequency priors to create more realistic position-specific priors.
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References
Gesteland, R.F., Cech, T.R., Atkins, J.F.: The RNA World, 3rd edn. Cold Spring Harbor Laboratory Press, New York (2006)
Rivas, E., Eddy, S.R.: Secondary Structure Alone is Generally Not Statistically Significant for the Detection of Noncoding RNAs. Bioinformatics 6, 583–605 (2000)
Burge, C., Karlin, S.: Prediction of Complete Gene Structures in Human Genomic DNA. Journal of Molecular Biology 268, 78–94 (1997)
Nawrocki, E.P., Eddy, S.R.: Query-Dependent Banding (QDB) for Faster RNA Similarity Searches. PLoS Computational Biology 3(3), 540–554 (2007)
Smith, S.F.: Covariance Searches for ncRNA Gene Finding. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 320–326 (2006)
Eddy, S.R.: Hidden Markov Models. Current Opinion in Structural Biology 6, 361–365 (1996)
Eddy, S.R.: Infernal 0.81 User’s Guide (2007), http://infernal.janelia.org/
Griffiths-Jones, S., Moxon, S., Marshall, M., Khanna, A., Eddy, S., Bateman, A.: Rfam: Annotating Non-coding RNAs in Complete Genomes. Nucleic Acids Research 33, D121–D141 (2005)
Eddy, S.R.: The HMMER User’s Guide (2003), http://hmmer.janelia.org/
Finn, R.D., Mistry, J., Schuster-Böckler, B., Griffiths-Jones, S., Hollich, V., Lassmann, T., Moxon, S., Marshall, M., Khanna, A., Durbin, R., Eddy, S.R., Sonnhammer, E.L.L., Bateman, A.: Pfam: Clans, Web Tools and Services. Nucleic Acids Research 34, D247–D251 (2006)
Brown, M., Hughey, R., Krogh, A., Mian, I.S., Sjölander, K., et al.: Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families. In: Conference on Intelligent Systems for Molecular Biology, pp. 47–55 (1993)
Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)
Sjölander, K., Karplus, K., Brown, M., Hughey, R., Krogh, A., Mian, I., Haussler, D.: Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology. Comp. Appl. BioSci. 12, 327–345 (1996)
Freier, S., Kierzek, R., Jaeger, J., Sugimoto, N., Caruthers, M., Neilson, T., Turner, D.: Improved Free-Energy Parameters for Predictions of RNA Duplex Stability. Proc. Nat. Acad. Sci. USA 83, 9373–9377 (1986)
Lobert, P.E., Escriou, N., Ruelle, J., Michiels, T.: A Coding RNA Sequence Acts as a Replication Signal in Cardioviruses. Proc. Nat. Acad. Sci. USA 96, 11560–11565 (1999)
Calin, G.A., Dumitru, C.D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S., Keating, M., Rai, K., Rassenti, L., Kipps, T., Negrini, M., Bullrich, F., Croce, C.M.: Frequent Deletions and Down-Regulation of Micro-RNA Genes miR15 and miR16 at 13q14 in Chronic Lymphocytic Leukemia. Proc. Nat. Acad. Sci. USA 99, 15524–15529 (2002)
Zucker, M.: Computer Prediction of RNA Structure. Methods in Enzymology 180, 262–288 (1989)
Wiese, K.C., Deschênes, A.A., Hendriks, A.G.: RnaPredict–An Evolutionary Algorithm for RNA Secondary Structure Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics 5, 25–41 (2008)
Raghunathan, P.L., Guthrie, C.: A Spliceosomal Recycling Factor that Reanneals U4 and U6 Small Nuclear Ribonucleoprotein Particles. Science 279, 857–860 (1998)
Smith, S.F., Wiese, K.C.: Improved Covariance Model Parameter Estimation Using RNA Thermodynamic Properties. In: International Conference on Bio-Inspired Models of Network, Information, and Computing Systems - Bionetics (2007)
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Smith, S.F., Wiese, K.C. (2008). Integrating Thermodynamic and Observed-Frequency Data for Non-coding RNA Gene Search. In: Priami, C., Dressler, F., Akan, O.B., Ngom, A. (eds) Transactions on Computational Systems Biology X. Lecture Notes in Computer Science(), vol 5410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92273-5_7
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DOI: https://doi.org/10.1007/978-3-540-92273-5_7
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