Kozak K, Rinn B, Leven O, Emmenlauer M
Carl Gustav Carus University Hospital, Clinic for Neurology, Medical Faculty, Technical University Dresden, Fetscherstraße 74, 01307, Dresden, Germany.
Fraunhofer IWS, Winterbergstraße 28, Dresden, 01277, Germany.
Methods Mol Biol. 2018;1683:131-148. doi: 10.1007/978-1-4939-7357-6_9.
Data analysis and management in high content screening (HCS) has progressed significantly in the past 10 years. The analysis of the large volume of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. In most screening laboratories, HCS has become a standard technology applied routinely to various applications from target identification to hit identification to lead optimization. An HCS data management and analysis infrastructure shared by several research groups can allow efficient use of existing IT resources and ensures company-wide standards for data quality and result generation. This chapter outlines typical HCS workflows and presents IT infrastructure requirements for multi-well plate-based HCS.
在过去十年中，高内涵筛选（HCS）中的数据分析和管理取得了显著进展。对HCS实验中产生的大量数据进行分析是一项重大挑战，目前也是许多筛选项目的瓶颈。在大多数筛选实验室中，HCS已成为一种常规应用于从靶点识别到活性化合物识别再到先导化合物优化等各种应用的标准技术。由多个研究小组共享的HCS数据管理和分析基础设施可以有效利用现有的信息技术资源，并确保全公司范围内的数据质量和结果生成标准。本章概述了典型的HCS工作流程，并介绍了基于多孔板的HCS的信息技术基础设施要求。
