Abstract
We describe parallel implementation of the metaheuristic approach to the problem of comparing strings representing DNA sequence. By this approach, one can define a whole new class of metrics on a set of strings; some of this metrics can lead to interesting results when used for string comparison. We propose several heuristics; compare results achieved when using those heuristics and compare parallel and sequential implementation of proposed approach.
Highlights
Determining DNA likeness is a particular case of a more common task of approximate comparison of strings, called fuzzy comparison [1]
We describe parallel implementation of the metaheuristic approach to the problem of comparing strings representing DNA sequence
The fuzzy comparison is used in text processing, Levenshtine metrics is used for error correction, for improvement of text recognition quality and in database search [1]
Summary
Determining DNA likeness is a particular case of a more common task of approximate comparison of strings, called fuzzy comparison [1]. “Fuzzy” means here, which we should be able to determine similar sequences even if there are some errors and distortions, like insertion or deletion of several symbols The amount of such distortions can be used as a metrics on a set of strings, defined as the minimum number of edits needing to transform one string into the other. Algorithms based on dynamic programming (like Hunt-Shimansky, Khirshberg, Wagner-Fisher and other) can provide the exact solution ([1,3,4,5,6]) Such algorithms have quadratic worst-case complexity and are considered too slow when speed is more important than accuracy (like database search). The strings likelihood assertion made by this approach is close to Levenshtein distance, but it is not equal to, nor is an approximation of Levenshtine distance This algorithm allows using multiple alternative metrics on space of chains of nucleotides, depending on used heuristics. This metrics can reflect the likelihood of compared strings, they show different meanings of likelihood itself
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