Computing similarity of run-length encoded strings with affine gap penalty

Jin Wook Kim, Amihood Amir, Gad M. Landau, Kunsoo Park

Research output: Contribution to journalArticlepeer-review

Abstract

The problem of computing the similarity of two run-length encoded strings has been studied for various scoring metrics. Many algorithms have been developed for the longest common subsequence metric and some algorithms for the Levenshtein distance metric and the weighted edit distance metric. In this paper we consider similarity based on the affine gap penalty metric which is a more general and rather complicated scoring metric than the weighted edit distance. To compute the similarity in this model efficiently, we convert the problem into a path problem on a directed acyclic graph and use some properties of maximum paths in this graph. We present an O (n m + n m) time algorithm for computing the similarity of two run-length encoded strings in the affine gap penalty model, where n and m are the lengths of given two strings whose run-length encoded lengths are n and m, respectively.

Original languageEnglish
Pages (from-to)268-282
Number of pages15
JournalTheoretical Computer Science
Volume395
Issue number2-3
DOIs
StatePublished - 1 May 2008

Bibliographical note

Funding Information:
I K. Park’s work was supported by FPR05A2-341 of 21C Frontier Functional Proteomics Project from Korean Ministry of Science & Technology and A. Amir and G. M. Landau’s work was partially supported by the Israel Science Foundation grant 35/05. ∗Corresponding author. Tel.: +82 2 880 8381; fax: +82 2 885 3141. E-mail address: kpark@theory.snu.ac.kr (K. Park).

Keywords

  • Affine gap penalty
  • Run-length encoding
  • Similarity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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