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RNA secondary structure prediction using context-sensitive hidden Markov models

Yoon, Byung-Jun and Vaidyanathan, P. P. (2004) RNA secondary structure prediction using context-sensitive hidden Markov models. In: IEEE International Workshop on Biomedical Circuits and Systems, 2004 (BIOCAS 2004), Singapore, 1-3 December 2004. IEEE , Piscataway, NJ, S2.7.INV-1-S2.7.INV-4. ISBN 0780386655.

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It has been believed for decades, that proteins are responsible for most of the genetically important functions in all cells. Due to this reason, most of the research in molecular biology was focused on identifying genes that encode proteins, and their roles in the genetic network. Recent studies indicate that non-coding RNAs play important roles in various processes. Such ncRNA genes cannot be effectively identified using traditional gene-finders that aim at protein-coding genes. Many ncRNAs conserve their secondary structures as well as their primary sequences, which have to be taken into account when looking for ncRNA genes. In this paper, we propose a new method based on context-sensitive HMMs, which can be used for predicting RNA secondary structure. It is demonstrated that the proposed model can predict the secondary structure very accurately, at a low computational cost.

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Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2004 IEEE. Reprinted with Permission. Publication Date: 1-3 Dec. 2004. Posted online: 2005-06-27. This work was supported in part by the ONR grant N00014-99-1-1002, USA.
Subject Keywords:genetics; hidden Markov models; macromolecules; molecular biophysics; molecular configurations; physiological models; RNA secondary structure prediction; context-sensitive hidden Markov models; molecular biology; noncoding RNA; protein-coding genes
Record Number:CaltechAUTHORS:YOObiocas04
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:9706
Deposited By: Kristin Buxton
Deposited On:05 Mar 2008
Last Modified:08 Nov 2021 21:01

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