DAFS: Simultaneous aligning and folding of RNA sequences via dual decomposition

Kengo Sato, Yuki Kato, Tatsuya Akutsu, Kiyoshi Asai, Yasubumi Sakakibara

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Motivation: It is well known that the accuracy of RNA secondary structure prediction from a single sequence is limited, and thus a comparative approach that predicts a common secondary structure from aligned sequences is a better choice if homologous sequences with reliable alignments are available. However, correct secondary structure information is needed to produce reliable alignments of RNA sequences. To tackle this dilemma, we require a fast and accurate aligner that takes structural information into consideration to yield reliable structural alignments, which are suitable for common secondary structure prediction.Results: We develop DAFS, a novel algorithm that simultaneously aligns and folds RNA sequences based on maximizing expected accuracy of a predicted common secondary structure and its alignment. DAFS decomposes the pairwise structural alignment problem into two independent secondary structure prediction problems and one pairwise (non-structural) alignment problem by the dual decomposition technique, and maintains the consistency of a pairwise structural alignment by imposing penalties on inconsistent base pairs and alignment columns that are iteratively updated. Furthermore, we extend DAFS to consider pseudoknots in RNA structural alignments by integrating IPknot for predicting a pseudoknotted structure. The experiments on publicly available datasets showed that DAFS can produce reliable structural alignments from unaligned sequences in terms of accuracy of common secondary structure prediction.Availability: The program of DAFS and the datasets are available at http://www.ncrna.org/software/dafs/.

Original languageEnglish
Pages (from-to)3218-3224
Number of pages7
JournalBioinformatics
Volume28
Issue number24
DOIs
Publication statusPublished - 2012 Dec 1

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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