Matveeva Olga, Nechipurenko Yury, Rossi Leo, Moore Barry, Saetrom Pål, Ogurtsov Aleksey Y, Atkins John F, Shabalina Svetlana A
Department of Human Genetics, University of Utah, Salt Lake City 84112-5330, USA.
Nucleic Acids Res. 2007;35(8):e63. doi: 10.1093/nar/gkm088. Epub 2007 Apr 10.
Current literature describes several methods for the design of efficient siRNAs with 19 perfectly matched base pairs and 2 nt overhangs. Using four independent databases totaling 3336 experimentally verified siRNAs, we compared how well several of these methods predict siRNA cleavage efficiency. According to receiver operating characteristics (ROC) and correlation analyses, the best programs were BioPredsi, ThermoComposition and DSIR. We also studied individual parameters that significantly and consistently correlated with siRNA efficacy in different databases. As a result of this work we developed a new method which utilizes linear regression fitting with local duplex stability, nucleotide position-dependent preferences and total G/C content of siRNA duplexes as input parameters. The new method's discrimination ability of efficient and inefficient siRNAs is comparable with that of the best methods identified, but its parameters are more obviously related to the mechanisms of siRNA action in comparison with BioPredsi. This permits insight to the underlying physical features and relative importance of the parameters. The new method of predicting siRNA efficiency is faster than that of ThermoComposition because it does not employ time-consuming RNA secondary structure calculations and has much less parameters than DSIR. It is available as a web tool called 'siRNA scales'.
当前文献描述了几种用于设计具有19个完全匹配碱基对和2个核苷酸突出端的高效小干扰RNA(siRNA)的方法。我们使用总计3336个经实验验证的siRNA的四个独立数据库,比较了其中几种方法预测siRNA切割效率的能力。根据受试者工作特征(ROC)和相关性分析,最佳程序是BioPredsi、ThermoComposition和DSIR。我们还研究了在不同数据库中与siRNA效力显著且一致相关的各个参数。通过这项工作,我们开发了一种新方法,该方法利用线性回归拟合,将局部双链稳定性、核苷酸位置依赖性偏好和siRNA双链体的总G/C含量作为输入参数。新方法区分高效和低效siRNA的能力与已确定的最佳方法相当,但与BioPredsi相比,其参数与siRNA作用机制的关系更为明显。这使得能够深入了解参数的潜在物理特征和相对重要性。预测siRNA效率的新方法比ThermoComposition更快,因为它不进行耗时的RNA二级结构计算,并且参数比DSIR少得多。它可以作为一个名为“siRNA scales”的网络工具使用。