Abstract

RNA interference (RNAi) is a mechanism in living cells that helps determine what genes are active and how active they are. The development of algorithms for RNAi design that produce potent and selective knockdown of targeted genes has led to a great deal of interest in using RNAi to elucidate gene function, to identify novel targets for drug discovery, and to reveal the molecular biological system. The importance of RNAi was further recognized when the Nobel Prize in Medicine and Physiology was awarded to Drs. Fire and Mello in 2006 for their research in this field. The genome-scale RNAi study allows genome-wide loss-of-function screening. One of the major advantages of the genome-scale RNAi researches is their ability to simultaneously interrogate thousands of genes. With the ability of generating a large amount of data per experiment, the genome-scale RNAi researches have led to an explosion in the rate of data generated in recent years. Consequently, one of the most fundamental challenges in the genome-scale RNAi researches is to glean biological significance from mounds of data, which relies on the development and adoption of statistics/bioinformatics methods that are suitable for analyzing RNAi screens. Recently, we have been developing novel analytic methods specifically for quality control and hit selection in genome-scale RNAi screens. In this presentation, I will first briefly introduce the RNAi HTS technology and the general process of analysing RNAi HTS data and then focus on presenting our developed analytic methods and demonstrate how to use them in RNAi high-throughput screens.