Literature DB >> 16990807

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

Christophe J Echeverri1, Philip A Beachy, Buzz Baum, Michael Boutros, Frank Buchholz, Sumit K Chanda, Julian Downward, Jan Ellenberg, Andrew G Fraser, Nir Hacohen, William C Hahn, Aimee L Jackson, Amy Kiger, Peter S Linsley, Lawrence Lum, Yong Ma, Bernard Mathey-Prévôt, David E Root, David M Sabatini, Jussi Taipale, Norbert Perrimon, René Bernards.   

Abstract

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.

Mesh:

Year:  2006        PMID: 16990807     DOI: 10.1038/nmeth1006-777

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  195 in total

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8.  ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling.

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