Bioinformatics Advance Access originally published online on December 18, 2008
Bioinformatics 2009 25(4):465-473; doi:10.1093/bioinformatics/btn601
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Prediction of RNA secondary structure using generalized centroid estimators
1Mizuho Information & Research Institute, Inc, 2–3 Kanda-Nishikicho, Chiyoda-ku, Tokyo 101–8443, 2Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2–41–6, Aomi, Koto-ku, Tokyo 135–0064, 3Department of Computational Intelligence and System Science, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226–8503, 4Japan Biological Informatics Consortium (JBIC), 2–45 Aomi, Koto-ku, Tokyo 135–8073 and 5Graduate School of Frontier Sciences, University of Tokyo, 5–1–5 Kashiwanoha, Kashiwa 277–8562, Japan
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Recent studies have shown that the methods for predicting secondary structures of RNAs on the basis of posterior decoding of the base-pairing probabilities has an advantage with respect to prediction accuracy over the conventionally utilized minimum free energy methods. However, there is room for improvement in the objective functions presented in previous studies, which are maximized in the posterior decoding with respect to the accuracy measures for secondary structures.
Results: We propose novel estimators which improve the accuracy of secondary structure prediction of RNAs. The proposed estimators maximize an objective function which is the weighted sum of the expected number of the true positives and that of the true negatives of the base pairs. The proposed estimators are also improved versions of the ones used in previous works, namely CONTRAfold for secondary structure prediction from a single RNA sequence and McCaskill-MEA for common secondary structure prediction from multiple alignments of RNA sequences. We clarify the relations between the proposed estimators and the estimators presented in previous works, and theoretically show that the previous estimators include additional unnecessary terms in the evaluation measures with respect to the accuracy. Furthermore, computational experiments confirm the theoretical analysis by indicating improvement in the empirical accuracy. The proposed estimators represent extensions of the centroid estimators proposed in Ding et al. and Carvalho and Lawrence, and are applicable to a wide variety of problems in bioinformatics.
Availability: Supporting information and the CentroidFold software are available online at: http://www.ncrna.org/software/centroidfold/.
Contact: hamada-michiaki{at}aist.go.jp
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Ivo Hofacker
Received on August 11, 2008; revised on November 5, 2008; accepted on November 14, 2008
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