HCS road: an enterprise system for integrated HCS data management and analysis.
The effective analysis and interpretation of high-content screening (HCS) data requires joining results to information on experimental treatments and controls, normalizing data, and selecting hits or fitting concentration-response curves. HCS data have unique requirements that are not supported by traditional high-throughput screening databases, including the ability to designate separate positive and negative controls for different measurements in multiplexed assays; the ability to capture information on the cell lines, fluorescent reagents, and treatments in each assay; the ability to store and use individual-cell and image data; and the ability to support HCS readers and software from multiple vendors along with third-party image analysis tools. To address these requirements, the authors developed an enterprise system for the storage and processing of HCS images and results. This system, HCS Road, supports target identification, lead discovery, lead evaluation, and lead profiling activities. A dedicated client supports experimental design, data review, and core analyses and displays images together with results for assay development, hit assessment, and troubleshooting. Data can be exported to third-party applications for further analysis and exploration. HCS Road provides a single source for high-content results across the organization, regardless of the group or instrument that produced them.
Jackson
Donald
D
Applied Genomics Group, Applied Biotechnology Department, Bristol-Myers Squibb Research and Development, Princeton, NJ, USA. donald.jackson@bms.com
Lenard
Michael
M
Zelensky
Alexander
A
Shaikh
Mohammad
M
Scharpf
James V
JV
Shaginaw
Richard
R
Nawade
Mahesh
M
Agler
Michele
M
Cloutier
Normand J
NJ
Fennell
Myles
M
Guo
Qi
Q
Wardwell-Swanson
Judith
J
Zhao
Dandan
D
Zhu
Yingjie
Y
Miller
Christopher
C
Gill
James
J
D003628:Database Management Systems; D057166:High-Throughput Screening Assays / Q000379:methods*; D034622:RNA Interference; D013223:Statistics as Topic