Minimizing the risk of reporting false positives in large-scale RNAi screens.
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.
Echeverri
Christophe J
CJ
Cenix BioScience GmbH, Tatzberg 47, Dresden, 10307, Germany. echeverri@cenix-bioscience.com
Beachy
Philip A
PA
Baum
Buzz
B
Boutros
Michael
M
Buchholz
Frank
F
Chanda
Sumit K
SK
Downward
Julian
J
Ellenberg
Jan
J
Fraser
Andrew G
AG
Hacohen
Nir
N
Hahn
William C
WC
Jackson
Aimee L
AL
Kiger
Amy
A
Linsley
Peter S
PS
Lum
Lawrence
L
Ma
Yong
Y
Mathey-Prévôt
Bernard
B
Root
David E
DE
Sabatini
David M
DM
Taipale
Jussi
J
Perrimon
Norbert
N
Bernards
René
R
D030541:Databases, Genetic / Q000592:standards; D005189:False Positive Reactions; D005820:Genetic Testing / Q000592:standards*; D010641:Phenotype; D034622:RNA Interference; D012306:Risk; D012680:Sensitivity and Specificity