Sven Rahmann1, Christine Gräfe. 1. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, D-14195 Berlin, Germany. Sven.Rahmann@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 DeltaG 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 DeltaG depends on all of these parameters simultaneously. RESULTS: We derive analytic results about the distribution of nearest neighbor DeltaG 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 DeltaG values is approximately Gaussian and provide exact formulas for expectation and variance.
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 DeltaG 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 DeltaG depends on all of these parameters simultaneously. RESULTS: We derive analytic results about the distribution of nearest neighbor DeltaG 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 DeltaG values is approximately Gaussian and provide exact formulas for expectation and variance.