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Evaluating real-life high-throughput screening data
Philip Gribbon1, Richard Lyons, Philip Laflin
1Automated Screening Technologies, Pfizer Global Research and Development, Sandwich, UK.
Journal of Biomolecular Screening
|April 1, 2005
Summary
High-throughput screening (HTS) quality relies on more than just control wells. Analyzing data from multiple perspectives ensures the validity of HTS results.
Area of Science:
- Biochemistry
- Bioinformatics
- Drug Discovery
Background:
- High-throughput screening (HTS) integrates chemistry, biology, IT, and engineering.
- Current HTS data analysis often overemphasizes control wells, potentially overlooking critical variables.
- Ensuring the quality and validity of HTS necessitates a comprehensive data evaluation approach.
Purpose of the Study:
- To highlight the limitations of reductionist data analysis in HTS.
- To advocate for multifaceted data examination in HTS quality control.
- To provide examples of extended HTS data analysis techniques.
Main Methods:
- Review of common HTS data analysis and quality control practices.
- Identification of factors influencing HTS outcomes beyond assay biology.
- Illustrative examples of alternative data analysis viewpoints.
Main Results:
- Reductionist approaches focusing solely on control wells are insufficient for robust HTS quality assurance.
- Numerous variables impact HTS results, requiring broader analytical perspectives.
- Multiple data visualization and analysis strategies can enhance HTS data interpretation.
Conclusions:
- A holistic approach to HTS data analysis is crucial for reliable results.
- Moving beyond control well-centric analysis improves the validity of screening outcomes.
- Comprehensive data review strengthens the overall quality of high-throughput screening processes.