Optimized models for design of efficient miR30-based shRNAs.

Small hairpin RNAs (shRNAs) became an important research tool in cell biology. Reliable design of these molecules is essential for the needs of large functional genomics projects. To optimize the design of efficient shRNAs, we performed comparative, thermodynamic, and correlation analyses of ~18,000 miR30-based shRNAs with known functional efficiencies, derived from the Sensor Assay project (Fellmann et al., 2011). We identified features of the shRNA guide strand that significantly correlate with the silencing efficiency and performed multiple regression analysis, using 4/5 of the data for training purposes and 1/5 for cross validation. A model that included the position-dependent nucleotide preferences was predictive in the cross-validation data subset (R = 0.39). However, a model, which in addition to the nucleotide preferences included thermodynamic shRNA features such as a thermodynamic duplex stability and position-dependent thermodynamic profile (dinucleotide free energy) was performing better (R = 0.53). Software "miR_Scan" was developed based upon the optimized models. Calculated mRNA target secondary structure stability showed correlation with shRNA silencing efficiency but failed to improve the model. Correlation analysis demonstrates that our algorithm for identification of efficient miR30-based shRNA molecules performs better than approaches that were developed for design of chemically synthesized siRNAs (R(max) = 0.36).