Efficient Algorithms for Regular Expression Constrained Sequence Alignment

Chung, Yun-Sheng; Lu, Chin Lung; Tang, Chuan Yi

doi:10.1007/11780441_35

Cited by 9 publications

(32 citation statements)

References 8 publications

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“…The term (*) is the bottleneck of the algorithm. Since A is non-deterministic, it may contain O(t 2 ) transitions by any letter c. The algorithm of Chung et al (2007b) …”

Section: An Overview Of Previous Workmentioning

confidence: 99%

“…Chung et al (2007b) improved the time complexity of Arslan's algorithm by removing redundant computations which were due to the fact that the computed value is based on two independent optimum calculations, one for each of the compared strings. We next describe Chung et al's algorithm using our own notation, in Eq.…”

Section: An Overview Of Previous Workmentioning

confidence: 99%

“…The algorithm simulates copies of this automaton on alignments, updating state scores, as dictated by the underlying scoring scheme. Chung et al (2007b) proposed an improvement to the above algorithm, yielding O(n 2 t 3 ) time and O(n 2 t 2 ) space complexities, in the general case, by splitting the computation into two steps. This algorithm is described in detail in Section 1.3.…”

Section: Introduction Smentioning

confidence: 99%

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Regular Language Constrained Sequence Alignment Revisited

Kucherov

Pinhas

Ziv-Ukelson

2011

Lecture Notes in Computer Science

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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(n 2 t 4 ) time and O(n 2 t 2 ) 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 2 t 3 ) 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(n 2 t 3 /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.

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“…The term (*) is the bottleneck of the algorithm. Since A is non-deterministic, it may contain O(t 2 ) transitions by any letter c. The algorithm of Chung et al (2007b) …”

Section: An Overview Of Previous Workmentioning

confidence: 99%

Section: An Overview Of Previous Workmentioning

confidence: 99%

Section: Introduction Smentioning

confidence: 99%

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Regular Language Constrained Sequence Alignment Revisited

Kucherov

Pinhas

Ziv-Ukelson

2011

Lecture Notes in Computer Science

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“…But many biologically important motifs such as those listed as regular expressions in the PROSITE [25] database cannot be formulated as constraints according to the convention followed by MuSiC [8]. To solve this issue, Arslan [5] and Chung et al [9] introduced alignment algorithms that accept regular expression constraints. Then, Chung et al proposed RE-MuSiC [8], an extension to their previous work [9], to support multiple sequences and multiple constraints.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

“…To solve this issue, Arslan [5] and Chung et al [9] introduced alignment algorithms that accept regular expression constraints. Then, Chung et al proposed RE-MuSiC [8], an extension to their previous work [9], to support multiple sequences and multiple constraints. In that work, they used sequence motifs found in PROSITE as regular expression constraints to improve the quality of alignments.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

CSA-X: Modularized Constrained Multiple Sequence Alignment

Islam

McQuillan

2017

Algorithms for Computational Biology

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Imposing additional constraints on multiple sequence alignment (MSA) algorithms can often produce more biologically meaningful alignments. Hence, various constrained multiple sequence alignment (CMSA) algorithms have been developed in the literature, where researchers used anchor points, regular expressions, or context-free-grammars to specify the constraints, wherein alignments produced are forced to align around segments that match the constraints.In this thesis, we propose CSA-X, a modularized program of constrained multiple sequence alignment that accepts constraints in the form of regular expressions. It uses an arbitrary underlying multiple sequence alignment program to generate alignments, and is therefore modular. The name CSA-X refers to our proposed program generally, where the letter X is substituted with the name of a (non-constrained) multiple sequence alignment algorithm which is used as underlying MSA engine in the proposed program. We compare the accuracy of our program with another constrained multiple sequence alignment program called RE-MuSiC that similarly uses regular expressions for constraints. In addition, comparisons are also made to the underlying MSA programs (without constraints).The BAliBASE 3.0 benchmark database is used to assess the performance of the proposed program CSA-X, other MSA programs, and CMSA programs considered in this study. Based on the results presented herein, CSA-X outperforms RE-MuSiC, and scores well against the underlying alignment programs. It also shows that the use of regular expression constraints, if chosen well, created from the least conserved region of the correct alignments, improves the alignment accuracy. In this study, ProbCons and T-Coffee are used as the underlying MSA programs in CSA-X, and the accuracy of the alignments are measured in terms of Q score and TC score. On average, CSA-X used with constraints identified from the least conserved regions of the correct alignments achieves results that are 17.65% more for Q score, and 23.7% more for TC score compared to RE-MuSiC. In fact, CSA-X with ProbCons (CSA-PC) achieves a higher score in over 97.9% of the cases for Q score, and over 96.4% of the cases for TC score. In addition, CSA-X with T-Coffee (CSA-TCOF) achieves a higher score in over 97.7% of the cases for Q score, and over 94.8% of the cases for TC score. Furthermore, CSA-X with regular expressions created from the least conserved regions of the correct alignments achieves higher accuracy scores compared to standalone ProbCons and T-Coffee. To measure the statistical significance of CSA-X results, the Wilcoxon rank-sum test and Wilcoxon signed-rank test are performed, and these tests show that CSA-X results for the least conserved regular expression constraint sets from the correct BAliBASE 3.0 alignments are significantly different than those from RE-MuSiC, ProbCons, and T-Coffee.ii Acknowledgements At first, I would like to thank Allah, the most gracious, the most merciful. He has blessed me with lots of bounties that I am una...

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Constrained sequence alignment: A general model and the hardness results

Chung

Tang

2007

Discrete Applied Mathematics

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Imposing constraints is a way to incorporate information into the sequence alignment procedure. In this paper, a general model for constrained alignment is proposed so that analyses admitted are more flexible and that different pattern definitions can be treated in a simple unified way. We give a polynomial time algorithm for pairwise constrained alignment for the generalized formulation, and prove the inapproximability of the problem when the number of sequences can be arbitrary. In addition, previous works deal only with the case that the patterns in the constraint have to occur in the output alignment in the same order as that specified by the input. It is of both theoretical and practical interest to investigate the case when the order is no longer limited. We show that the problem is not approximable even when the number of sequences is two. We also give the NPO-completeness results for the problems with bounds imposed on the objective function value.

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Efficient Algorithms for Regular Expression Constrained Sequence Alignment

Cited by 9 publications

References 8 publications

Regular Language Constrained Sequence Alignment Revisited

Regular Language Constrained Sequence Alignment Revisited

CSA-X: Modularized Constrained Multiple Sequence Alignment

Constrained sequence alignment: A general model and the hardness results

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