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Swiftly computing center strings.

Hufsky F, Kuchenbecker L, Jahn K, Stoye J, Böcker S - BMC Bioinformatics (2011)

Bottom Line: Then, we describe a novel iterative search strategy that is efficient in practice, where some of our reduction techniques can also be applied.Finally, we present results of an evaluation study for two different data sets from a biological application.Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Lehrstuhl für Bioinformatik, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, Jena, Germany. franziska.hufsky@uni-jena.de.

ABSTRACT

Background: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete.

Results: In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is efficient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application.

Conclusions: We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably.

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Related in: MedlinePlus

Average running times of all instances. Average running times of all instances for first (top) and second (bottom) data set. Running times are depicted in dependency on varying d around dopt. Algorithm Gramm is shown separately for the second data set due to the long running times.
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Figure 4: Average running times of all instances. Average running times of all instances for first (top) and second (bottom) data set. Running times are depicted in dependency on varying d around dopt. Algorithm Gramm is shown separately for the second data set due to the long running times.

Mentions: We concentrate on the computation of center strings for d = dopt and d = dopt - 1, since these are the computationally hard instances (Figure 4). For the parameterized algorithms, worst-case running times grow exponentially in d, and running times of algorithms are also dominated by these cases in practice.


Swiftly computing center strings.

Hufsky F, Kuchenbecker L, Jahn K, Stoye J, Böcker S - BMC Bioinformatics (2011)

Average running times of all instances. Average running times of all instances for first (top) and second (bottom) data set. Running times are depicted in dependency on varying d around dopt. Algorithm Gramm is shown separately for the second data set due to the long running times.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108310&req=5

Figure 4: Average running times of all instances. Average running times of all instances for first (top) and second (bottom) data set. Running times are depicted in dependency on varying d around dopt. Algorithm Gramm is shown separately for the second data set due to the long running times.
Mentions: We concentrate on the computation of center strings for d = dopt and d = dopt - 1, since these are the computationally hard instances (Figure 4). For the parameterized algorithms, worst-case running times grow exponentially in d, and running times of algorithms are also dominated by these cases in practice.

Bottom Line: Then, we describe a novel iterative search strategy that is efficient in practice, where some of our reduction techniques can also be applied.Finally, we present results of an evaluation study for two different data sets from a biological application.Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Lehrstuhl für Bioinformatik, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, Jena, Germany. franziska.hufsky@uni-jena.de.

ABSTRACT

Background: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete.

Results: In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is efficient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application.

Conclusions: We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably.

Show MeSH
Related in: MedlinePlus