TY - GEN

T1 - Regular language constrained sequence alignment revisited

AU - Kucherov, Gregory

AU - Pinhas, Tamar

AU - Ziv-Ukelson, Michal

PY - 2011

Y1 - 2011

N2 - Imposing constraints in the form of a finite automaton or a regular expression is an effective way to incorporate additional a priori knowledge into sequence alignment procedures. With this motivation, Arslan [1] introduced the Regular Language Constrained Sequence Alignment Problem and proposed an O(n 2 t4) time and O(n2 t2) space algorithm for solving it, where n is the length of the input strings and t is the number of states in the non-deterministic automaton, which is given as input. Chung et al. [2] proposed a faster O(n2 t3) time algorithm for the same problem. In this paper, we further speed up the algorithms for Regular Language Constrained Sequence Alignment by reducing their worst case time complexity bound to O(n2 t3/logt). This is done by establishing an optimal bound on the size of Straight-Line Programs solving the maxima computation subproblem of the basic dynamic programming algorithm. We also study another solution based on a Steiner Tree computation. While it does not improve the run time complexity in the worst case, our simulations show that both approaches are efficient in practice, especially when the input automata are dense.

AB - Imposing constraints in the form of a finite automaton or a regular expression is an effective way to incorporate additional a priori knowledge into sequence alignment procedures. With this motivation, Arslan [1] introduced the Regular Language Constrained Sequence Alignment Problem and proposed an O(n 2 t4) time and O(n2 t2) space algorithm for solving it, where n is the length of the input strings and t is the number of states in the non-deterministic automaton, which is given as input. Chung et al. [2] proposed a faster O(n2 t3) time algorithm for the same problem. In this paper, we further speed up the algorithms for Regular Language Constrained Sequence Alignment by reducing their worst case time complexity bound to O(n2 t3/logt). This is done by establishing an optimal bound on the size of Straight-Line Programs solving the maxima computation subproblem of the basic dynamic programming algorithm. We also study another solution based on a Steiner Tree computation. While it does not improve the run time complexity in the worst case, our simulations show that both approaches are efficient in practice, especially when the input automata are dense.

UR - http://www.scopus.com/inward/record.url?scp=79953231638&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-19222-7_39

DO - 10.1007/978-3-642-19222-7_39

M3 - Conference contribution

AN - SCOPUS:79953231638

SN - 9783642192210

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 404

EP - 415

BT - Combinatorial Algorithms - 21st International Workshop, IWOCA 2010, Revised Selected Papers

T2 - 21st International Workshop on Combinatorial Algorithms, IWOCA 2010

Y2 - 26 July 2010 through 28 July 2010

ER -