Minimizing the risk of reporting false positives in large-scale RNAi screens

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Large-scale RNA interference (RNAi)-based analyses, very much as other 'omic' approaches, have inherent rates of false positives and negatives. The variability in the standards of care applied to validate results from these studies, if left unchecked, could eventually begin to undermine the credibility of RNAi as a powerful functional approach. This Commentary is an invitation to an open discussion started among various users of RNAi to set forth accepted standards that would insure the quality and accuracy of information in the large datasets coming out of genome-scale screens. Please visit methagora to view and post comments on this article

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Figure 1: Appropriate experimental controls to minimize risks of misinterpretation of RNAi data due to off-target effects (OTEs).

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