alexa RNA Interference Off-target Screening using Principal C
ISSN: 2153-0602

Journal of Data Mining in Genomics & Proteomics
Open Access

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Research Article

RNA Interference Off-target Screening using Principal Component Analysis

Jakob Müller* and Michael W. Pfaffl

Physiology Weihenstephan, Technische Universität München, Research Center for Nutrition and Food Science, Weihenstephaner Berg 3, 85350 Freising, Germany

*Corresponding Author:
Jakob Müller
Physiology Weihenstephan
Technische Universität München
Research Center for Nutrition and Food Science
Weihenstephaner Berg 3, 85350 Freising, Germany
Tel: +498161713867
Fax: +498161714204
E-mail: [email protected]

Received date: March 07, 2012; Accepted date: June 13, 2012; Published date: June 17, 2012

Citation: Müller J, Pfaffl MW (2012) RNA Interference Off-target Screening using Principal Component Analysis. J Data Mining Genomics Proteomics 3:116. doi:10.4172/2153-0602.1000116

Copyright: © 2012 Müller J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

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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