iScreen: Image-Based High-Content RNAi Screening Analysis Tools
Author(s) -
Rui Zhong,
Xiaonan Dong,
Beth Levine,
Yang Xie,
Guanghua Xiao
Publication year - 2014
Publication title -
slas discovery
Language(s) - Uncategorized
Resource type - Journals
eISSN - 2472-5560
pISSN - 2472-5552
DOI - 10.1177/1087057114564348
Subject(s) - high content screening , computer science , visualization , rna interference , computational biology , annotation , genome , throughput , functional genomics , genomics , data mining , artificial intelligence , biology , genetics , wireless , cell , rna , gene , telecommunications
High-throughput RNA interference (RNAi) screening has opened up a path to investigating functional genomics in a genome-wide pattern. However, such studies are often restricted to assays that have a single readout format. Recently, advanced image technologies have been coupled with high-throughput RNAi screening to develop high-content screening, in which one or more cell image(s), instead of a single readout, were generated from each well. This image-based high-content screening technology has led to genome-wide functional annotation in a wider spectrum of biological research studies, as well as in drug and target discovery, so that complex cellular phenotypes can be measured in a multiparametric format. Despite these advances, data analysis and visualization tools are still largely lacking for these types of experiments. Therefore, we developed iScreen (image-Based High-content RNAi Screening Analysis Tool), an R package for the statistical modeling and visualization of image-based high-content RNAi screening. Two case studies were used to demonstrate the capability and efficiency of the iScreen package. iScreen is available for download on CRAN (http://cran.cnr.berkeley.edu/web/packages/iScreen/index.html). The user manual is also available as a supplementary document.
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