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Bioinformatics Advance Access published online on June 16, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth334
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received August 19, 2003
Revised March 19, 2004
Accepted May 15, 2004

Article

Mean and variance of the Gibbs free energy of oligonucleotides in the nearest neighbor model under varying conditions

Sven Rahmann 1* Christine Gräfe 2

1 Dept. of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestr. 73, D-14195 Berlin, Germany; Bioinformatics Program, Dept. of Mathematics and Computer Science, Freie Universität Berlin, Germany
2 Bioinformatics Program, Dept. of Mathematics and Computer Science, Freie Universität Berlin, Germany

* To whom correspondence should be addressed. E-mail: Sven.Rahmann{at}cebitec.uni-bielefeld.de.


  Abstract

Motivation: In order to assess the stability of DNA-DNA hybridizations--for example during PCR primer design or oligonucleotide selection for microarrays--one needs to predict the change in Gibbs free energy {Delta}G during hybridization. The Nearest Neighbor model provides a good compromise between accuracy and computational simplicity for this task.

To determine optimal combinations of reaction parameters (temperature, salt concentration, oligonucleotide length and GC-content), one would like to understand how {Delta}G depends on all of these parameters simultaneously.

Results: We derive analytic results about the distribution of Nearest-Neighbor {Delta}G values for a Bernoulli random sequence model (specified by oligonucleotide length and average GC-content) under given experimental conditions. We find that the distribution of {Delta}G values is approximately Gaussian and provide exact formulas for expectation and variance.


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