NDLI: A Method to Improve the Universality of SiRNA Design Rules Based on siRNA Efficiency Distribution

Content Provider	IEEE Xplore Digital Library
Author	Dongfang Wang Xiang Chen Fa Zhang Peizhuo Zhang Guohua Liu Xin Guo
Copyright Year	2008
Description	Author affiliation: Yanshan Univ., Qinhuangdao (Guohua Liu) \|\| Inst. of Comput. Technol., Chinese Acad. of Sci., Beijing (Dongfang Wang; Xiang Chen; Fa Zhang) \|\| Suzhou Genepharma Co., Ltd., Suzhou (Peizhuo Zhang) \|\| China Nat. Software & Service Co., Ltd. (Xin Guo)
Abstract	Effectual siRNA design is a pivotal question in RNAi research. However existing siRNA design guidelines are not very consistent with each other, and many have the limitation of universality, that is, many guidelines usually present poor predicting capability on independent siRNA datasets. In order to explore high universal siRNA design rules, first we collected as many siRNA valid data on various genes and species as possible and sampled the training datasets according to the distribution of siRNA efficiency in a nature state based on statistic principle. Then we performed two sets of analysis process in parallel adopting different statistical methods to explore the rules for functional siRNA design. Finally we tested the rules on internal and external test set. Test results indicated the functional siRNA design rules obtained by our analysis method had good predicting capability and universality.
Starting Page	84
Ending Page	87
File Size	173883
Page Count	4
File Format	PDF
ISBN	9781424427274
DOI	10.1109/ISISE.2008.284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers universality efficiency disrutution Effectual siRNA Data analysis Statistical analysis RNA Guidelines Information science Statistical distributions design rules Sampling methods internal and external test set Performance analysis Testing
Content Type	Text
Resource Type	Article

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