Improving the accuracy of predicting secondary structure for aligned RNA sequences

Michiaki Hamada, Kengo Sato, Kiyoshi Asai

研究成果: Article

32 引用 (Scopus)

抄録

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.

元の言語English
ページ(範囲)393-402
ページ数10
ジャーナルNucleic Acids Research
39
発行部数2
DOI
出版物ステータスPublished - 2011 1
外部発表Yes

Fingerprint

Untranslated RNA
Computational Biology

ASJC Scopus subject areas

  • Genetics

これを引用

Improving the accuracy of predicting secondary structure for aligned RNA sequences. / Hamada, Michiaki; Sato, Kengo; Asai, Kiyoshi.

:: Nucleic Acids Research, 巻 39, 番号 2, 01.2011, p. 393-402.

研究成果: Article

Hamada, Michiaki ; Sato, Kengo ; Asai, Kiyoshi. / Improving the accuracy of predicting secondary structure for aligned RNA sequences. :: Nucleic Acids Research. 2011 ; 巻 39, 番号 2. pp. 393-402.
@article{6a2539374d76404ea71e257b3d9fa502,
title = "Improving the accuracy of predicting secondary structure for aligned RNA sequences",
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.",
author = "Michiaki Hamada and Kengo Sato and Kiyoshi Asai",
year = "2011",
month = "1",
doi = "10.1093/nar/gkq792",
language = "English",
volume = "39",
pages = "393--402",
journal = "Nucleic Acids Research",
issn = "0305-1048",
publisher = "Oxford University Press",
number = "2",

}

TY - JOUR

T1 - Improving the accuracy of predicting secondary structure for aligned RNA sequences

AU - Hamada, Michiaki

AU - Sato, Kengo

AU - Asai, Kiyoshi

PY - 2011/1

Y1 - 2011/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=79551480068&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79551480068&partnerID=8YFLogxK

U2 - 10.1093/nar/gkq792

DO - 10.1093/nar/gkq792

M3 - Article

C2 - 20843778

AN - SCOPUS:79551480068

VL - 39

SP - 393

EP - 402

JO - Nucleic Acids Research

JF - Nucleic Acids Research

SN - 0305-1048

IS - 2

ER -