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http://dx.doi.org/10.5808/GI.2011.9.1.005

Recent Progresses in the Linguistic Modeling of Biological Sequences Based on Formal Language Theory  

Park, Hyun-Seok (Bioinformatics Laboratory, School of Engineering, Ewha Womans University)
Galbadrakh, Bulgan (Bioinformatics Laboratory, School of Engineering, Ewha Womans University)
Kim, Young-Mi (Natural Language Processing Laboratory, School of Natural Science, Huree Institute of Information and Communication Technology)
Abstract
Treating genomes just as languages raises the possibility of producing concise generalizations about information in biological sequences. Grammars used in this way would constitute a model of underlying biological processes or structures, and that grammars may, in fact, serve as an appropriate tool for theory formation. The increasing number of biological sequences that have been yielded further highlights a growing need for developing grammatical systems in bioinformatics. The intent of this review is therefore to list some bibliographic references regarding the recent progresses in the field of grammatical modeling of biological sequences. This review will also contain some sections to briefly introduce basic knowledge about formal language theory, such as the Chomsky hierarchy, for non-experts in computational linguistics, and to provide some helpful pointers to start a deeper investigation into this field.
Keywords
natural language processing; Chomsky hierarchy; bioinformatics; formal language theory;
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1 Rosenblueth, D., Thieffry, D., Huerta, A., Salgado, H., and Collado-Vides, J. (1996). Syntactic recognition of regulatory regions in Escherichia coli. Comput. Appl. Biosci. 12, 415-422.
2 Sakakibara, Y. (2003). Pair Hidden Markov Models on Tree Structures. Bioinformatics 19, 232-240.   DOI
3 Sakakibara, Y. (2005). Grammatical Inference in Bioinformatics Bioinformatics. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27, 1051-1062.   DOI
4 Searls, D.B. (1988). Representing Genetic Information with Formal Grammars. In Proceedings of the 7th National Conference on Artificial Intelligence. 386-391.
5 Hopcroft, J.E., and Ullman, J.D. (1979). Introduction to Automata Theory, Languages, and Computation, Addison-Wesley Publishing, Reading Massachusetts, ISBN 0-201-029880-X.
6 Hulo, N., Bairoch, A., Bulliard, V., Cerutti, L., De Castro E., Langendijk-Genevaux, P.S., Pagni, M., and Sigrist, C.J.A. (2006). The PROSITE database. Nucl. Acids. Res. 34, D227-D230.   DOI
7 Joshi, A.K., Levy, L.S., and Takahashi, M. (1975). Tree adjunct grammars. J. Computer & System Sciences 10, 136-163.   DOI
8 Leung, S.W., Mellish, C., and Robertson, D. (2001). Basic Gene Grammars and DNA-Chart Parser for language processing of Escherichia coli promoter DNA sequences, Bioinformatics 17, 226-236.   DOI
9 Knudsen, B., and Hein, J. (1999). RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics 15, 446-454.   DOI
10 Krogh, A., Brown, M., Mian, I.S., Sjolander, K., and Haussler, D. (1994). Hidden Markov models in computational biology. Applications to protein modeling, J. Mol. Biol. 235, 1501-1531.   DOI
11 Lanctot, J.K., Li, M., and Yang, E.H. (2000). Estimating DNA sequence entropy. In ACMSIAM Symposium on Discrete Algorithms. 409-418.
12 Liew, A.W., Yan, H., and Yang, M. (2005). Pattern recognition techniques for the emerging field of bioinformatics. A review, Pattern Recognition 38, 2055-2073.   DOI
13 Matsui, H., Sato, K., and Sakakibara, Y. (2005). Pair Stochastic Tree Adjoining Grammars for Aligning and Predicting Pseudoknot RNA Structures. Bioinformatics 21, 2611-2617.   DOI
14 Nevill-Manning, C.G., and Witten, I.H. (1997). Compression and explanation using hierarchical grammars. The Computer Journal 40, 103-116.   DOI
15 Coste, F. (2010). Biological Sequences by Grammatical Inference, author manuscript, published in ICGI 2010 Tutorial Day, Valencia: Espagne (http://www.irisa.fr/symbiose/francois_coste)
16 Dowell, R.D., and Eddy, S.R. (2004). Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinformatics 5,
17 Durbin, R., Eddy, S., Krogh, A., and Mitchison, G. (1998). Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge Univ. Press.
18 Dyrka, W., and Nebel, J.C. (2009). A stochastic context-free grammar based framework for analysis of protein sequences. BMC Bioinformatics 10, 323.   DOI
19 Eddy, S.R., and Durbin, R. (1994). RNA sequence analysis using covariance models. Nucl. Acids Res. 22, 2079-2088.   DOI
20 Eddy, S.R. (1998). Profile hidden Markov models. Bioinformatics 14, 755-763.   DOI
21 Gardner, P.P., Daub, J., Tate, J.G., Nawrocki, E.P., Kolbe, D.L., Lindgreen, S., Wilkinson, A.C., Finn, R.D., Gri_ths-Jones, S., Eddy, S.R., and Bateman, A. (2009). Updates to the RNA families database. Nucl. Acids. Res. 37, 136-140.
22 Griffiths-Jones, S., Bateman, A., Marshall, Ml, Khanna, A., and Eddy, S.R. (2003). Rfam: an RNA family database. Nucl. Acids Res. 31, 439-441.   DOI
23 Head, T. (1987). Formal Language Theory and DNA: An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bull. math. Biol. 49, 737-759.   DOI
24 Holmes, I., and Rubin, G. (2002). Pairwise RNA structure comparison with stochastic context-free grammars. In Proceedings of 5th Pacific Symposium on Biocomputing. World Scientific Press, Singapore, pp. 163-174.
25 Holmes, I. (2005). Accelerated probabilistic inference of RNA structure evolution. BMC Bioinformatics 24, 6-73.
26 Apostolico, A., and Lonardi, S. (2000). Off-line compression by greedy textual substitution. Proceedings of the IEEE. 88, 1733-1744.   DOI
27 Apostolico, A., and Lonardi, S. (2000). Compression of biological sequences by greedy off-line textual substitution. In: Data Compression Conference. 143-153.
28 Baquero, F. (2004). From pieces to patterns: evolutionary engineering in bacterial pathogens. Nat. Rev. Microbiol. 2, 510-518.   DOI
29 Brazma, A., Jonassen, I., Eidhammer, I., and Gilbert, D. (1998). Approaches to the automatic discovery of patterns in biosequences. J. Computat. Biol. 5, 279-305.   DOI
30 Cai, L., Malmberg, R.L., and Wu, Y. (2003). Stochastic Modeling of RNA Pseudoknotted Structures: A Grammatical Approach. Bioinformatics 19, 66-73.   DOI
31 Carrascosa, R., Coste, F., GallZ, M., and Infante-Lopez, G. (2011). Searching for smallest grammars on dna sequences. Journal of Discrete Algorithms, Elsevier (to be published).
32 Cherniavsky, N., and Ladner, R.E. (2004). Grammar-based compression of DNA Sequences. UW CSE Technical Report (TR2007-05-02), presented at the DIMACS Working Group on the Burrows-Wheeler Transform.
33 Chomsky, N. (1957). Syntactic Structures. Mouton and Co.
34 Chuong, B.D., Daniel, A.W., and Serafim, B. (2006). RNA secondary structure prediction without physics-based models. Bioinformatics 22, e90-e98.   DOI
35 Collado-Vides, J. (1992). Grammatical model of the regulation of gene expression. Proc. Natl. Acad. Sci. USA, 89, 9405-9409.   DOI
36 Coste, F., and Kerbellec, G. (2005). A similar fragments merging approach to learn automata on proteins. In Gama, J., Camacho, R., Brazdil, P., Jorge, A., Torgo, L., eds. ECML. Volume 3720 of Lecture Notes in Computer Science., Springer. 522-529.
37 Abe, N., and Mamitsuka, H. (1999). A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars. Proc. 11th Int'l Conf. Machine Learning 3-11.
38 Agarwal, S., Vaz, C., Bhattacharya, A., and Srinivasan, A. (2010). Prediction of novel precursor miRNAs using context- sensitive hidden Markov model (CSHMM). BMC Bioinformatics 11(suppl 1), S29.
39 Searls, D.B. (2002). The language of genes. Nature 420, 211-217.   DOI
40 Searls, D. (1993). The computational linguistics of biological sequences. In Artificial Intelligence and Molecular Biology, chapter 2, Hunter, L., ed. (MIT Press: Boston, MA), pp. 47-120.
41 Sigrist, C.J.A., De Castro, E., Langendijk-Genevaux, P.S., Le Saux, V., Bairoch, A., and Hulo, N. (2005). ProRule: a new database containing functional and structural information on PROSITE profiles. Bioinformatics 21, 4060-4066.   DOI
42 Tsafnat, G., Coiera, E., Partridge, S.R., Schaeffer, J., and Iredell, J.R. (2009). Context-driven discovery of gene cassettes in mobile integrons using a computational grammar. BMC Bioinformatics 10, 281.   DOI
43 Tsafnat, G., Schaeffer, J., Clayphan, A., Iredell, J.R., Partridge, S.R., and Coiera, E. (2011). Computational inference of grammars for larger-than-gene structures from annotated gene sequences. Bioinformatics 27, 791-796.   DOI
44 Uemura, Y., Hasegawa, A., Kobayashi, S., and Yokomori, T. (1999). Tree-Adjoining Grammars for RNA Structure Prediction. Theoretical Computer Science 10, 277-303.
45 Yokomori, T., Ishida, N., and Kobayashi, S. (1994). Learning local languages and its application to protein $\alpha$-chain identification. In: System Sciences, vol.5: Biotechnology Computing, Proceedings of the Twenty-Seventh Hawaii International Conference. 113-122.
46 Peris, P., L'opez, D., Campos, M., and Sempere, J.M. (2006). Protein motif prediction by grammatical inference. In LNCS (LNAI). Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E., eds. (Springer: Heidelberg) 4201, pp. 175-187.
47 Yoon, B.J., and Vaidyanathan, P.P. (2004). RNA secondary structure prediction using context-sensitive hidden Markov models. Proceedings of IEEE International Workshop on Biomedical Circuits and Systems (BioCAS): Dec. 2004, Singapore.   DOI
48 Partridge, S.R., Tsafnat, G., Coiera, E., and Iredell, J. (2009). Gene cassettes and cassette arrays inmobile resistance integrons, FEMS Microbiol. Rev. 33, 757-784.   DOI
49 Pereira, F., and Warren, D. (1980). Definite clause grammars for language analysis. Artif. Intell., 13, 231-278.   DOI
50 Peris, P., L'opez, D., and Campos, M. (2008). IgTM: An algorithm to predict transmembrane domains and topology in proteins. BMC Bioinformatics 9, 367.   DOI
51 Reidys, M., Huang, W.D., Andersen, E., Penner, C., Stadler, F., and Nebel, E. (2011). Topology and prediction of RNA pseudoknots. Bioinformatics advance access, doi:10.1093/bioinformatics/btr090.   DOI
52 Rivas, E., and Eddy, S. (2000). The Language of RNA: A Formal Grammar That Includes Pseudoknots. Bioinformatics 16, 334-340.   DOI
53 Rivas, E., and Eddy, S. (2001). Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics 2, 8.   DOI