Nucleic Acids Research Advance Access originally published online on December 10, 2007
Nucleic Acids Research 2008 36(2):640-647; doi:10.1093/nar/gkm920
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Nucleic Acids Research, 2008, Vol. 36, No. 2 640-647
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Efficient siRNA selection using hybridization thermodynamics
1Department of Biochemistry & Biophysics and 2Department of Biostatistics & Computational Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, NY 14642, USA
*To whom correspondence may be addressed. Tel: 585 275 1734; Fax: 585 275 6007; Email: david_mathews{at}urmc.rochester.edu
Received August 28, 2007. Revised October 8, 2007. Accepted October 9, 2007.
Small interfering RNA (siRNA) are widely used to infer gene function. Here, insights in the equilibrium of siRNA-target hybridization are used for selection of efficient siRNA. The accessibilities of siRNA and target mRNA for hybridization, as measured by folding free energy change, are shown to be significantly correlated with efficacy. For this study, a partition function calculation that considers all possible secondary structures is used to predict target site accessibility; a significant improvement over calculations that consider only the predicted lowest free energy structure or a set of low free energy structures. The predicted thermodynamic features, in addition to siRNA sequence features, are used as input for a support vector machine that selects functional siRNA. The method works well for predicting efficient siRNA (efficacy >70%) in a large siRNA data set from Novartis. The positive predictive value (percentage of sites predicted to be efficient for silencing that are) is as high as 87.6%. The sensitivity and specificity are 22.7 and 96.5%, respectively. When tested on data from different sources, the positive predictive value increased 8.1% by adding equilibrium terms to 25 local sequence features. Prediction of hybridization affinity using partition functions is now available in the RNAstructure software package.
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