ScreenMill: A freely available software suite for growth measurement, analysis and visualization of high-throughput screen data
1 Columbia University, Dept. of Biological Sciences, New York, NY 10027, USA
2 Columbia University Medical Center, Dept. Genetics & Development, 701 West 168th Street, New York, NY 10032-2704, USA
BMC Bioinformatics 2010, 11:353 doi:10.1186/1471-2105-11-353Published: 28 June 2010
Many high-throughput genomic experiments, such as Synthetic Genetic Array and yeast two-hybrid, use colony growth on solid media as a screen metric. These experiments routinely generate over 100,000 data points, making data analysis a time consuming and painstaking process. Here we describe ScreenMill, a new software suite that automates image analysis and simplifies data review and analysis for high-throughput biological experiments.
The ScreenMill, software suite includes three software tools or "engines": an open source Colony Measurement Engine (CM Engine) to quantitate colony growth data from plate images, a web-based Data Review Engine (DR Engine) to validate and analyze quantitative screen data, and a web-based Statistics Visualization Engine (SV Engine) to visualize screen data with statistical information overlaid. The methods and software described here can be applied to any screen in which growth is measured by colony size. In addition, the DR Engine and SV Engine can be used to visualize and analyze other types of quantitative high-throughput data.
ScreenMill automates quantification, analysis and visualization of high-throughput screen data. The algorithms implemented in ScreenMill are transparent allowing users to be confident about the results ScreenMill produces. Taken together, the tools of ScreenMill offer biologists a simple and flexible way of analyzing their data, without requiring programming skills.