Local alignment of RNA sequences with arbitrary scoring schemes

Rolf Backofen, Danny Hermelin, Gad M. Landau, Oren Weimann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Local similarity is an important tool in comparative analysis of biological sequences, and is therefore well studied. In particular, the Smith-Waterman technique and its normalized version are two established metrics for measuring local similarity in strings. In RNA sequences however, where one must consider not only sequential but also structural features of the inspected molecules, the concept of local similarity becomes more complicated. First, even in global similarity, computing global sequence-structure alignments is more difficult than computing standard sequence alignments due to the bi-dimensionality of information. Second, one can view locality in two different ways, in the sequential or structural sense, leading to different problem formulations. In this paper we introduce two sequentially-local similarity metrics for comparing RNA sequences. These metrics combine the global RNA alignment metric of Shasha and Zhang [16] with the Smith-Waterman metric [17] and its normalized version [2] used in strings. We generalize the familiar alignment graph used in string comparison to apply also for RNA sequences, and then utilize this generalization to devise two algorithms for computing local similarity according to our two suggested metrics. Our algorithms run in O(m2n lg n) and O(m 2n lg n+n2m) time respectively, where m ≤ n are the lengths of the two given RNAs. Both algorithms can work with any arbitrary scoring scheme.

Original languageEnglish
Title of host publicationCombinatorial Pattern Matching - 17th Annual Symposium, CPM 2006, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540354557, 9783540354550
StatePublished - 2006
Event17th Annual Symposium on Combinatorial Pattern Matching, CPM 2006 - Barcelona, Spain
Duration: 5 Jul 20067 Jul 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th Annual Symposium on Combinatorial Pattern Matching, CPM 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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