In Silico Target-Specific siRNA Design Based on Domain Transfer in Heterogeneous Data

RNA interference via exogenous small interference RNAs (siRNA) is a powerful tool in gene function study and disease treatment. Designing efficient and specific siRNA on target gene remains the key issue in RNAi. Although various in silico models have been proposed for rational siRNA design, most of them focus on the efficiencies of selected siRNAs, while limited effort has been made to improve their specificities targeted on specific mRNAs, which is related to reducing off-target effects (OTEs) in RNAi. In our study, we propose for the first time that the enhancement of target specificity of siRNA design can be achieved computationally by domain transfer in heterogeneous data sources from different siRNA targets. A transfer learning based method i.e., heterogeneous regression (HEGS) is presented for target-specific siRNA efficacy modeling and feature selection. Based on the model, (1) the target regression model can be built by extracting information from related data in other targets/experiments, thus increasing the target specificity in siRNA design with the help of information from siRNAs binding to other homologous genes, and (2) the potential features correlated to the current siRNA design can be identified even when there is lack of experimental validated siRNA affinity data on this target. In summary, our findings present useful instructions for a better target-specific siRNA design, with potential applications in genome-wide high-throughput screening of effective siRNA, and will provide further insights on the mechanism of RNAi.