title: RFRCDB-siRNA: improved design of siRNAs by random forest regression model coupled with database searching.
abstract: Although the observations concerning the factors which influence the siRNA efficacy give clues to the mechanism of RNAi, the quantitative prediction of the siRNA efficacy is still a challenge task. In this paper, we introduced a novel non-linear regression method: random forest regression (RFR), to quantitatively estimate siRNAs efficacy values. Compared with an alternative machine learning regression algorithm, support vector machine regression (SVR) and four other score-based algorithms [A. Reynolds, D. Leake, Q. Boese, S. Scaringe, W.S. Marshall, A. Khvorova, Rational siRNA design for RNA interference, Nat. Biotechnol. 22 (2004) 326-330; K. Ui-Tei, Y. Naito, F. Takahashi, T. Haraguchi, H. Ohki-Hamazaki, A. Juni, R. Ueda, K. Saigo, Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference, Nucleic Acids Res. 32 (2004) 936-948; A.C. Hsieh, R. Bo, J. Manola, F. Vazquez, O. Bare, A. Khvorova, S. Scaringe, W.R. Sellers, A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens, Nucleic Acids Res. 32 (2004) 893-901; M. Amarzguioui, H. Prydz, An algorithm for selection of functional siRNA sequences, Biochem. Biophys. Res. Commun. 316 (2004) 1050-1058) our RFR model achieved the best performance of all. A web-server, RFRCDB-siRNA (http://www.bioinf.seu.edu.cn/siRNA/index.htm), has been developed. RFRCDB-siRNA consists of two modules: a siRNA-centric database and a RFR prediction system. RFRCDB-siRNA works as follows: (1) Instead of directly predicting the gene silencing activity of siRNAs, the service takes these siRNAs as queries to search against the siRNA-centric database. The matched sequences with the exceeding the user defined functionality value threshold are kept. (2) The mismatched sequences are then processed into the RFR prediction system for further analysis.
authors.affiliation: State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, PR China.
authors.forename: Peng
authors.initials: P
authors.lastname: Jiang
authors.forename: Haonan
authors.initials: H
authors.lastname: Wu
authors.forename: Yao
authors.initials: Y
authors.lastname: Da
authors.forename: Fei
authors.initials: F
authors.lastname: Sang
authors.forename: Jiawei
authors.initials: J
authors.lastname: Wei
authors.forename: Xiao
authors.initials: X
authors.lastname: Sun
authors.forename: Zuhong
authors.initials: Z
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D030541:Databases, Genetic; D016247:Information Storage and Retrieval / Q000379:methods; D008957:Models, Genetic; D008969:Molecular Sequence Data; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000235:genetics*; D012044:Regression Analysis; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D034741:RNA, Small Interfering