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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

Enumeration scheme for all strings s. Enumeration scheme for all strings s with Hamming distance at most 3 to a bit string s1 = 0 ... 0 of length 5.
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Figure 1: Enumeration scheme for all strings s. Enumeration scheme for all strings s with Hamming distance at most 3 to a bit string s1 = 0 ... 0 of length 5.

Mentions: We enumerate the mismatch positions for d mismatches in s1 (and therefore the center string candidates s), which is equivalent to generating all binary numbers of length m with d bits set to 1, in reverse order (Figure 1). For every s we check its Hamming distance to the remaining strings s2, s3,..., sk. Rather than computing these distances anew for each candidate, we update the Hamming distances derived from the previous candidate s'. We do this by increasing or decreasing the distances to reflect the changed positions.


Swiftly computing center strings.

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

Enumeration scheme for all strings s. Enumeration scheme for all strings s with Hamming distance at most 3 to a bit string s1 = 0 ... 0 of length 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Enumeration scheme for all strings s. Enumeration scheme for all strings s with Hamming distance at most 3 to a bit string s1 = 0 ... 0 of length 5.
Mentions: We enumerate the mismatch positions for d mismatches in s1 (and therefore the center string candidates s), which is equivalent to generating all binary numbers of length m with d bits set to 1, in reverse order (Figure 1). For every s we check its Hamming distance to the remaining strings s2, s3,..., sk. Rather than computing these distances anew for each candidate, we update the Hamming distances derived from the previous candidate s'. We do this by increasing or decreasing the distances to reflect the changed positions.

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