title: The effect of regions flanking target site on siRNA potency.
abstract: For a successful RNA interference (RNAi) experiment, selecting the small interference RNA (siRNA) candidates which maximize the knock down effect of the given gene is the critical step. Although various computational approaches have been attempted, the design of efficient siRNA candidates is far from satisfactory yet. In this study, we proposed a novel feature selection algorithm of combined random forest and support vector machine to predict active siRNAs. Using a publically available dataset, we demonstrated that the predictive accuracy would be markedly improved when the context sequence features outside the target site were included. The Pearson correlation coefficient for regression is as high as 0.721, compared to 0.671, 0.668, 0.680, and 0.645, for Biopredsi, i-score, ThermoComposition21 and DSIR, respectively. It revealed that siRNA-target interaction requires appropriate sequence context not only in the target site but also in a broad region flanking the target site.
authors.affiliation: Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China. Electronic address: liliu2010imu@163.com.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.forename: Qian-Zhong
authors.initials: QZ
authors.lastname: Li
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.forename: Yong-Chun
authors.initials: YC
authors.lastname: Zuo
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D008958:Models, Molecular; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000235:genetics*; D012044:Regression Analysis; D017423:Sequence Analysis, RNA; D060388:Support Vector Machine
chemical_list: D034741:RNA, Small Interfering
keywords: Flanking sequence; Regression analysis; Support vector machine; siRNA