Integrated siRNA design based on surveying of features associated with high RNAi effectiveness.
BACKGROUND
Short interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins.


RESULTS
Using a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs).


CONCLUSIONS
The availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies.
Gong
Wuming
W
Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA. wuming@biocompute.umn.edu <wuming@biocompute.umn.edu>
Ren
Yongliang
Y
Xu
Qiqi
Q
Wang
Yejun
Y
Lin
Dong
D
Zhou
Haiyan
H
Li
Tongbin
T
D000465:Algorithms; D001483:Base Sequence; D030561:Databases, Nucleic Acid; D015195:Drug Design; D008969:Molecular Sequence Data; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*; D013329:Structure-Activity Relationship
D034741:RNA, Small Interfering