Linear time algorithms for NP-hard problems restricted to partial k-trees
Discrete Applied Mathematics
Easy problems for tree-decomposable graphs
Journal of Algorithms
On the complexity of finding iso- and other morphisms for partial k-trees
Discrete Mathematics - Topological, algebraical and combinatorial structures; Froli´k's memorial volume
A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth
SIAM Journal on Computing
A branch-and-cut algorithm for multiple sequence alignment
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Tree adjoining grammars for RNA structure prediction
Theoretical Computer Science - Special issue: Genome informatics
Tree Decomposition Based Fast Search of RNA Structures Including Pseudoknots in Genomes
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
A Tree-Decomposition Approach to Protein Structure Prediction
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Rapid protein side-chain packing via tree decomposition
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Profiling and searching for RNA pseudoknot structures in genomes
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Parameterized Complexity
Memory Efficient Algorithms for Structural Alignment of RNAs with Pseudoknots
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An Efficient Alignment Algorithm for Searching Simple Pseudoknots over Long Genomic Sequence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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(k^{t} N^{2}) for the structure of N residues and its tree decomposition of width t. Parameter k, small in nature, is determined by a statistical cutoff for the correspondence between the structure and the sequence. This paper demonstrates a successful application of the algorithm to RNA structure search used for noncoding RNA identification. An application to protein threading is also discussed.