Search

A new method is developed that can profile and efficiently search for pseudoknot structures in noncoding RNA genes. It profiles interleaving stems in pseudoknot structures with independent Covariance Model (CM) components. The statistical alignment score for searching is obtained by combining the alignment scores from all CM components. Our experiments show that the model can achieve excellent accuracy on both random and biological data. The efficiency achieved by the method makes it possible to search for the pseudoknot structures in genomes of a variety of organisms. [ABSTRACT FROM AUTHOR]

Computational alignment of a biopolymer sequence (e.g., an RNA or a protein) to a structure is an effective approach to predict and search for the structure of new sequences. To identify the structure of remote homologs, the structure-sequence alignment has to consider not only sequence similarity but also spatially conserved conformations caused by residue interactions, and consequently is computationally intractable. It is difficult to cope with the inefficiency without compromising alignment accuracy, especially for structure search in genomes or large databases. This paper introduces a novel method and a parameterized algorithm for structure-sequence alignment. Both the structure and the sequence are represented as graphs, where in general the graph for a biopolymer structure has a naturally small tree width. The algorithm constructs an optimal alignment by finding in the sequence graph the maximum valued subgraph isomorphic to the structure graph. It has the computational time complexity O(ktN2) for the structure of N residues and its tree decomposition of width t. The parameter k, small in nature, is determined by a statistical cutoff for the correspondence between the structure and the sequence. The paper demonstrates a successful application of the algorithm to developing a fast program for RNA structural homology search. [ABSTRACT FROM AUTHOR]

We developed a new method that can profile and efficiently search for pseudoknot structures in noncoding RNA genes. It profiles interleaving stems in pseudoknot structures with independent Covariance Model (CM) components. The statistical alignment score for searching is obtained by combining the alignment scores from all CM components. Our experiments show that the model can achieve excellent accuracy on both random and biological data. The efficiency achieved by the method makes it possible to search for structures that contain pseudoknot in genomes of a variety of organisms. [ABSTRACT FROM AUTHOR]