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RNA Interference Off-target Screening using Principal Component Analysis | Abstract
Journal of Data Mining in Genomics & Proteomics

Journal of Data Mining in Genomics & Proteomics
Open Access

ISSN: 2153-0602

Abstract

RNA Interference Off-target Screening using Principal Component Analysis

Jakob Müller and Michael W. Pfaffl

Off-target effects remain the major problem in any RNAi-knockdown application. Casting cell culture loss-of-function studies evaluated by heat map and principal component analysis (PCA) we realized that the PCA derived plots can clearly visualize off-target effects. Due to the inexistence of off-target effects in our cell culture model we created an in silico data model in order to demonstrate how PCA can be utilized therefore. With the presented in silico modulation it is possible to simulate the impact of various treatments on changing gene expression. Known effects caused by drug treatment or by inserted knockdowns could be clearly separated from unknown off-target effects. By creating various randomized gene expression data sets we demonstrate that PCA can assign more effective an off-target effect compared to a heat map gene regulation pattern.

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