Y. Sakakibara, M. Brown, R. Hughey, I. Mian, K. S. der, R. Underwood, and D. Haussler. Recent methods for RNA modeling using stochastic context-free grammars. In Proceedings of 5th Annual Symposium on Combinatorial Pattern Matching (CPM'94), LNCS 807, Springer, pages 289-306, 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... algorithms for computing the distance between two trees [19, 21, 24, 20, 25, 23, 22] Another method for modeling RNA structures is using stochastic context free grammars (SCFGs) which is a generalization of hidden Markov models (HMMs) capturing well the common primary and secondary structures [16, 4]. Notice that the above two methods do not treat basepairs as units of comparison. Instead, they focus on local structures and treat them as subunits. Other work that does not treat base pairs as units includes [5, 2, 12] On the other hand, there are several papers which consider base pairs as ....

Y. Sakakibara, M. Brown, R. Hughey, I. Mian, K. S. der, R. Underwood, and D. Haussler. Recent methods for RNA modeling using stochastic context-free grammars. In Proceedings of 5th Annual Symposium on Combinatorial Pattern Matching (CPM'94), LNCS 807, Springer, pages 289-306, 1994.

....higher. In contrast to the above work, our version of the prediction problem assumes that more information is available in the form of the sequence and the secondary structure of an RNA string that is closely related to the one whose structure is to be predicted. Another related line of work is [6, 15, 16] that uses stochastic context free grammars to model a family of related RNA strings. Other related work appears in [8, 11, 18, 19] In the next section, we describe our results on RNA string matching; We then turn to alignment of RNA strings and its variants in Section 3. In Section 4, we ....

Y. Sakakibara, M. Brown, R. Hughey, I. S. Mian, K. Sjolander, R. C. Underwood and D. Haussler, "Recent methods for RNA modeling using stochastic context-free grammars," Proc. Combinatorial Pattern Matching Conf., LNCS 807, 289-306 (1994).

....Molecular biology suggests another example along these lines, the class of protein secondary structures coded for by RNA. To some approximation the long range correlations between RNA nucleotide base pairs responsible for secondary structure can be modeled by parenthesis matching grammars [28, 27]. Since RNA macromolecules are quantum mechanical objects, constructed by processes that respect atomic and molecular quantum physics, the class of secondary structures coded for by RNA may be more appropriately modeled by the quantum analogs of context free grammars introduced here. In the same ....

Y. Sakakibara, M. Brown, R. Hughey, I.S. Mian, K. Sjolander, R.C. Underwood, and D. Haussler, \Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars." in Combinatorial Pattern Matching, 5th Annual Symposium, M. Crochemore and D. Guseld, eds., Springer-Verlag, Berlin (1994) 289-306.

....With sophisticated parsing techniques used in place of dynamic programming, the method is more amenable to be used by compiler writing specialists. Stochastic context free grammars have been used intensively in the area of RNA folding and modeling of RNA families (Eddy and Durbin, 1994; Sakakibara et al. 1994). Calculating probabilities is just a particular way of scoring. In our framework, a stochastic grammar is expressed by a yield grammar, called with a stochastic evaluation algebra that multiplies probabilities along a derivation, and adds probabilities of alternative derivations. Stochastic ....

Sakakibara, Y., Brown, M., Hughey, R., Mian, I. S., Sjolander, K., Underwood, R. C. and Haussler, D. (1994). Recent methods for RNA modeling using stochastic context-free grammars, (Gusfield, 1994), pp. 289--306.

....simple to represent exactly the biologically sensible structures. We have to resort to more expressive description techniques. 2.3 A regular tree grammar for all feasible structures RNA structures (excluding pseudoknots) can be described by context free grammars. Such a description is used in [20, 26, 28]. Structures, then, are the derivation trees resulting from parsing an RNA sequence [20, 21] Context free grammars clearly define the language of strings derived by the grammar, but the language of derivation trees is implicit. As we shall see, it is much more convenient to use tree grammars ....

Y. Sakakibara, M. Brown, R. Hughey, I.S. Mian, K. Sjolander, R.C. Underwood, and D. Haussler. Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars. In [14], pages 289--306, 1994.

....74 75) Figure 2 illustrates the sort of branched secondary structure that is captured by this grammar. Recently a number of workers have taken advantage of this characterization to develop stochastic forms of such grammars that are useful in recognizing certain orthodox secondary structures [8, 14, 15]. We have also noted previously that, while L h is clearly nondeterministic (it being necessary to guess at the midpoint of the hairpin) L o is, surprisingly, deterministic [18] We now offer the following Griebach normal form grammar that is weakly equivalent to G o , but deterministic: G od : ....

Y. Sakakibara, M. Brown, R. Hughey, I.S. Mian, K. Sjolander, R.C. Underwood, and D. Haussler. Recent methods for RNA modeling using stochastic context-free grammars. In Proceedings of the Asilomar Conference on Combinatorial Pattern Matching, New York, NY, 1994. Springer-Verlag.

....Molecular biology suggests another example along these lines, the class of protein secondary structures coded for by RNA. To some approximation the long range correlations between RNA nucleotide base pairs responsible for secondary structure can be modeled by parenthesis matching grammars [28, 27]. Since RNA macromolecules are quantum mechanical objects, constructed by processes that respect atomic and molecular quantum physics, the class of secondary structures coded for by RNA may be more appropriately modeled by the quantum analogs of context free grammars introduced here. In the same ....

Y. Sakakibara, M. Brown, R. Hughey, I. S. Mian, K. Sjolander, R. C. Underwood, and D. Haussler, "Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars." in Combinatorial Pattern Matching, 5th Annual Symposium, M. Crochemore and D. Gusfield, eds., Springer-Verlag, Berlin (1994) 289-306.

....algorithm called the inside outside algorithm [16] Both the inside and outside variables can be computed in O(n 3 ) time. These variables can be used to estimate the probability of a production being used. A more computationally efficient SCFG parameter estimation technique has been developed [24]. This algorithm is called the Tree Grammar EM training algorithm. This algorithm uses labeled trees as input rather than observation sequences. These labeled trees represent folded sequences which are a set of trees in which the frontier, or leaves, of the tree are known but the internal nodes ....

Y. Sakakibara, M. Brown, R. Hughey, I. S. Mian, K. Sjolander, R. C. Underwood, and D. Haussler. Recent methods for RNA modeling using stochastic context-free grammars. In Proc. Asilomar Conf. on Combinatorial Pattern Matching, New York, NY, 1994. Springer-Verlag.

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