Computational methods to support high-content screening: from compound selection and data analysis to postulating target hypotheses

Abstract

Background: Computational support for high-content screening (HCS) is of paramount importance at several stages of the process: from the selection of compounds, to the image and data analysis all the way to hit identification and analysis of mechanisms of action. Method: Here, we describe computational approaches to improve the benefit gained from HCS, such as compound selection, image analysis and algorithms to further process and explore HCS data. We describe the current challenges in these areas and state our expectations for the field. Conclusion: At present there are no standard approaches for correction, normalization, analysis or visualization of HCS data. Thus, the information-rich data sets provided by HCS are exploited to only a limited extent. To overcome this shortcoming, a thorough comparison and evaluation of different tools is needed.

Keywords::

• compound selection
• data management
• high-content screening
• image analysis
• multiparametric data analysis
• phenotypic screening

Acknowledgments

A Kümmel thanks the Education Office of NIBR for a postdoctoral fellowship. This study was partly performed within the framework of Top Institute Pharma project: number D1-105 (A.B.). The authors thank Jeremy L Jenkins and Daniel W Young for their comments on the manuscript.

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