Improving the accuracy of predicting secondary structure for aligned RNA sequences

Michiaki Hamada, Kengo Sato, Kiyoshi Asai

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

Considerable attention has been focused on predicting the secondary structure for aligned RNA sequences since it is useful not only for improving the limiting accuracy of conventional secondary structure prediction but also for finding non-coding RNAs in genomic sequences. Although there exist many algorithms of predicting secondary structure for aligned RNA sequences, further improvement of the accuracy is still awaited. In this article, toward improving the accuracy, a theoretical classification of state-of-the-art algorithms of predicting secondary structure for aligned RNA sequences is presented. The classification is based on the viewpoint of maximum expected accuracy (MEA), which has been successfully applied in various problems in bioinformatics. The classification reveals several disadvantages of the current algorithms but we propose an improvement of a previously introduced algorithm (CentroidAlifold). Finally, computational experiments strongly support the theoretical classification and indicate that the improved CentroidAlifold substantially outperforms other algorithms.

Original languageEnglish
Pages (from-to)393-402
Number of pages10
JournalNucleic acids research
Volume39
Issue number2
DOIs
Publication statusPublished - 2011 Jan
Externally publishedYes

ASJC Scopus subject areas

  • Genetics

Fingerprint Dive into the research topics of 'Improving the accuracy of predicting secondary structure for aligned RNA sequences'. Together they form a unique fingerprint.

Cite this