Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Progressive multiple alignment with constraints
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
A polyhedral approach to RNA sequence structure alignment
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Parametric Sequence Alignment with Constraints
Constraints
Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Unifying tree decompositions for reasoning in graphical models
Artificial Intelligence
Alignment of RNA base pairing probability matrices
Bioinformatics
Local RNA base pairing probabilities in large sequences
Bioinformatics
AND/OR search spaces for graphical models
Artificial Intelligence
Exploiting tree decomposition and soft local consistency in weighted CSP
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
Aligning DNA and protein sequences is a core technique in molecular biology. Often, it is desirable to include partial prior knowledge and conditions in an alignment. Going beyond prior work, we aim at the integration of such side constraints in free combination into alignment algorithms. The most common and successful technique for efficient alignment algorithms is dynamic programming (DP). However, a weakness of DP is that one cannot include additional constraints without specifically tailoring a new DP algorithm. Here, we discuss a declarative approach that is based on constraint techniques and show how it can be extended by formulating additional knowledge as constraints. We take special care to obtain the efficiency of DP for sequence alignment. This is achieved by careful modeling and applying proper solving strategies. Finally, we apply our method to the scanning for RNA motifs in large sequences. This case study demonstrates how the new approach can be used in real biological problems. A prototypic implementation of the method is available at http://www.bioinf.uni-freiburg.de/Software/CTE-Alignment .