Yoon, Byung-Jun and Vaidyanathan, P. P. (2004) HMM with auxiliary memory: a new tool for modeling RNA structures. In: Asilomar Conference on Signals, Systems and Computers, 38th, Pacific Grove, CA, 7-10 November 2004. Vol.2. IEEE , Piscataway, NJ, pp. 1651-1655. ISBN 0780386221 http://resolver.caltech.edu/CaltechAUTHORS:YOOasilo04
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For a long time, proteins have been believed to perform most of the important functions in all cells. However, recent results in genomics have revealed that many RNAs that do not encode proteins play crucial roles in the cell machinery. The so-called ncRNA genes that are transcribed into RNAs but not translated into proteins, frequently conserve their secondary structures more than they conserve their primary sequences. Therefore, in order to identify ncRNA genes, we have to take the secondary structure of RNAs into consideration. Traditional approaches that are mainly based on base-composition statistics cannot be used for modeling and identifying such structures and models with more descriptive power are required. In this paper, we introduce the concept of context-sensitive HMMs, which is capable of describing pairwise interactions between distant symbols. It is demonstrated that the proposed model can efficiently model various RNA secondary structures that are frequently observed.
|Item Type:||Book Section|
|Additional Information:||© 2004 IEEE. Reprinted with Permission. Publication Date: 7-10 Nov. 2004. Posted online: 2005-03-21. Work supported in parts by the ONR grant N00014-99-1-1002 USA and the Microsoft Research fellowship.|
|Subject Keywords:||genetics; hidden Markov models; macromolecules; proteins; statistical analysis; RNA structures modeling; auxiliary memory; base-composition statistics; proteins|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Kristin Buxton|
|Deposited On:||05 Mar 2008|
|Last Modified:||26 Dec 2012 09:51|
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