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Efficient Parameterized Algorithm for Biopolymer StructureSequence Alignment
 In Proceedings of Workshop on Algorithms for Bioinformatics
, 2005
"... Abstract. 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 structuresequence alignment has to consider not only sequence si ..."
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Abstract. 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 structuresequence 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 structuresequence 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(k t N 2) 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. 1
Fast and Accurate Search for Noncoding RNA Pseudoknot Structures in Genomes
"... Motivation: Searching genomes for noncoding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknotfree structures, ncRNA search can be effective based on the covariance model and CYKtype dynamic programming. However, the computational difficulty ..."
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Motivation: Searching genomes for noncoding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknotfree structures, ncRNA search can be effective based on the covariance model and CYKtype dynamic programming. However, the computational difficulty in aligning an RNA sequence to a pseudoknot has prohibited fast and accurate search of arbitrary RNA structures. Our previous work introduced a graph model for RNA pseudoknots and proposed to solve the structuresequence alignment by graph optimization. Given k candidate regions in the target sequence for each of the n stems in the structure, we could compute best alignment in time O(k t n) based upon a tree width t decomposition of the structure graph. However, to implement this method to programs that can routinely perform fast yet accurate RNA pseudoknot searches, we need novel heuristics to ensure that,