Regular language constrained sequence alignment revisited

Gregory Kucherov, Tamar Pinhas, Michal Ziv-Ukelson

Research output: Contribution to journalArticlepeer-review


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, the Regular Expression Constrained Sequence Alignment Problem was introduced, which proposed an O(n2t4) time and O(n2t2) space algorithm for solving it, where n is the length of the input strings and t is the number of states in the input non-deterministic automaton. A faster O(n 2t3) time algorithm for the same problem was subsequently proposed. In this article, we further speed up the algorithms for Regular Language Constrained Sequence Alignment by reducing their worst case time complexity bound to O(n2t3/log t). 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 worst case, our simulations show that both approaches are efficient in practice, especially when the input automata are dense.

Original languageEnglish
Pages (from-to)771-781
Number of pages11
JournalJournal of Computational Biology
Issue number5
StatePublished - 1 May 2011


  • Steiner minimal trees
  • dynamic programming
  • finite automaton
  • regular expression
  • sequence alignment
  • straight-line programs

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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