Literature DB >> 10556021

Mean free energy topology for nucleotide sequences of varying composition based on secondary structure calculations.

W K Dawson1, K Yamamoto.   

Abstract

The mean free energy generated from the secondary structure of RNA sequences of varying length and composition has been studied by way of probability theory. The expected boundaries or maximal and minimal values of a given distribution are explored and a method for estimating error as a function of the number of shuffled sequences is also examined. For typical nucleotide sequences found in biologically active organisms, the mean free energy, free energy distributions and errors appear to be scalable in terms of a fixed set of algorithm-dependent parameters and the nucleotide composition of the particular sequence under evaluation. In addition, a general semi-analytical formula for predicting the mean free energy is proposed which, at least to first-order approximation, can be used to rapidly predict the mean free energy of any sequence length and composition of RNA. The general methodology appears to be algorithm independent. The results are expected to provide a reference point for certain types of analysis related to structure of RNA or DNA sequences and to assist in measuring the somewhat related matter of complexity in algorithm development. Some related applications are discussed. Copyright 1999 Academic Press.

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Year:  1999        PMID: 10556021     DOI: 10.1006/jtbi.1999.1018

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  2 in total

1.  Analysis of the conformational energy landscape of human snRNA with a metric based on tree representation of RNA structures.

Authors:  Junji Kitagawa; Yasuhiro Futamura; Kenji Yamamoto
Journal:  Nucleic Acids Res       Date:  2003-04-01       Impact factor: 16.971

2.  Number variation of high stability regions is correlated with gene functions.

Authors:  Yuanhui Mao; Qian Li; Wangtian Wang; Peiquan Liang; Shiheng Tao
Journal:  Genome Biol Evol       Date:  2013       Impact factor: 3.416

  2 in total

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