%0 Journal Article %T An accurate and interpretable model for siRNA efficacy prediction %A Jean-Philippe Vert %A Nicolas Foveau %A Christian Lajaunie %A Yves Vandenbrouck %J BMC Bioinformatics %D 2006 %I BioMed Central %R 10.1186/1471-2105-7-520 %X We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions.The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web at http://cbio.ensmp.fr/dsir webciteRNA interference (RNAi) is the process through which a double-stranded RNA (dsRNA) induces gene expression silencing, either by degradation of sequence-specific complementary messenger RNA (mRNA) or by repression of translation [1]. The RNAi pathway was firstly identified in lower organisms (plants, fungi and invertebrates) and led to many successful applications such as genome-wide RNAi screens [2-5]. In mammalian systems, chemically synthesized dsRNA reagents shorter than 30 nt were found to trigger sequence-specific RNAi response without inducing the cell's immune mechanism [6,7]. The use of exogenous small interfering RNAs (siRNAs) for abolishing gene expression has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification [8,9]. Moreover, RNAi represents a promising technology for therapeutic applications against HIV [10], neurodegenerative disorders [11] and can %U http://www.biomedcentral.com/1471-2105/7/520