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Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens

Biometrics Research, Merck Research Laboratories, West Point, Pennsylvania, xiaohua_zhang{at}merck.com

Amy S. Espeseth

RNA Therapeutics, Merck Research Laboratories, West Point, Pennsylvania

Eric N. Johnson

Automated Biotechnology, Merck Research Laboratories, North Wales, Pennsylvania

Jayne Chin

Cardiovascular Diseases, Merck Research Laboratories, Rahway, New Jersey

Adam Gates

Antiviral Research, Merck Research Laboratories, West Point, Pennsylvania

Lyndon J. Mitnaul

Cardiovascular Diseases, Merck Research Laboratories, Rahway, New Jersey

Shane D. Marine

Automated Biotechnology, Merck Research Laboratories, North Wales, Pennsylvania

Jenny Tian

Cardiovascular Diseases, Merck Research Laboratories, Rahway, New Jersey

Eric M. Stec

Automated Biotechnology, Merck Research Laboratories, North Wales, Pennsylvania

Priya Kunapuli

Automated Biotechnology, Merck Research Laboratories, North Wales, Pennsylvania

Dan J. Holder

Biometrics Research, Merck Research Laboratories, West Point, Pennsylvania

Joseph F. Heyse

BARDS, Merck Research Laboratories, West Point, Pennsylvania

Berta Strulovici

Automated Biotechnology, Merck Research Laboratories, North Wales, Pennsylvania

Marc Ferrer

Automated Biotechnology, Merck Research Laboratories, North Wales, Pennsylvania

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. (Journal of Biomolecular Screening 2008:378-389)

Key Words: strictly standardized mean difference • Z factor • quality control • RNAi high-throughput screening • plate design

This version was published on June 1, 2008

Journal of Biomolecular Screening, Vol. 13, No. 5, 378-389 (2008)
DOI: 10.1177/1087057108317145


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