Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens.
RNA interference (RNAi) not only plays an important role in drug discovery but can also be developed directly into drugs. RNAi high-throughput screening (HTS) biotechnology allows us to conduct genome-wide RNAi research. A central challenge in genome-wide RNAi research is to integrate both experimental and computational approaches to obtain high quality RNAi HTS assays. Based on our daily practice in RNAi HTS experiments, we propose the implementation of 3 experimental and analytic processes to improve the quality of data from RNAi HTS biotechnology: (1) select effective biological controls; (2) adopt appropriate plate designs to display and/or adjust for systematic errors of measurement; and (3) use effective analytic metrics to assess data quality. The applications in 5 real RNAi HTS experiments demonstrate the effectiveness of integrating these processes to improve data quality. Due to the effectiveness in improving data quality in RNAi HTS experiments, the methods and guidelines contained in the 3 experimental and analytic processes are likely to have broad utility in genome-wide RNAi research.
Zhang
Xiaohua Douglas
XD
Biometrics Research, Merck Research Laboratories, West Point, Pennsylvania 19486, USA. xiaohua_zhang@merck.com
Espeseth
Amy S
AS
Johnson
Eric N
EN
Chin
Jayne
J
Gates
Adam
A
Mitnaul
Lyndon J
LJ
Marine
Shane D
SD
Tian
Jenny
J
Stec
Eric M
EM
Kunapuli
Priya
P
Holder
Dan J
DJ
Heyse
Joseph F
JF
Strulovici
Berta
B
Ferrer
Marc
M
D016632:Apolipoprotein A-I / Q000235:genetics; D001709:Biotechnology / Q000379:methods* / Q000592:standards; D016678:Genome; D016174:Hepacivirus / Q000235:genetics; D011786:Quality Control; D034622:RNA Interference; D012107:Research Design / Q000592:standards
D016632:Apolipoprotein A-I