title: An accurate and interpretable model for siRNA efficacy prediction.
abstract: BACKGROUND
  The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centre for Computational Biology, Ecole des Mines de Paris, 35 rue Saint-Honoré, 77300 Fontainebleau, France. Jean-Philippe.Vert@ensmp.fr
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Vert
authors.forename: Nicolas
authors.initials: N
authors.lastname: Foveau
authors.forename: Christian
authors.initials: C
authors.lastname: Lajaunie
authors.forename: Yves
authors.initials: Y
authors.lastname: Vandenbrouck
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D003198:Computer Simulation; D015195:Drug Design; D008957:Models, Genetic; D008969:Molecular Sequence Data; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D034741:RNA, Small Interfering