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Open Access Highly Accessed Research article

Systems analysis of quantitative shRNA-library screens identifies regulators of cell adhesion

XiaoDong Huang125, Jean YJ Wang123 and Xin Lu246*

Author Affiliations

1 Division of Biological Sciences, University of California at San Diego, La Jolla, CA 92093, USA

2 Moores Cancer Center, University of California at San Diego, La Jolla, CA 92093, USA

3 Division of Hematology/Oncology, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA

4 Department of Family and Preventive Medicine, University of California at San Diego, La Jolla, CA 92093, USA

5 Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA

6 Abbott Laboratories, 100 Abbott Park Rd, North Chicago, IL 60063, USA

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BMC Systems Biology 2008, 2:49  doi:10.1186/1752-0509-2-49

Published: 13 June 2008

Abstract

Background

High throughput screens with RNA interference technology enable loss-of-function analyses of gene activities in mammalian cells. While the construction of genome-scale shRNA libraries has been successful, results of large-scale screening of those libraries can be difficult to analyze because of the relatively high noise levels and the fact that not all shRNAs in a library are equally effective in silencing gene expression.

Results

We have screened a library consisting of 43,828 shRNAs directed against 8,500 human genes for functions that are necessary in cell detachment induced by a constitutively activated c-Abl tyrosine kinase. To deal with the issues of noise and uncertainty of knockdown efficiencies, we employed an analytical strategy that combines quantitative data analysis with biological knowledge, i.e. Gene Ontology and pathway information, to increase the power of the RNAi screening technique. Using this strategy we found 16 candidate genes to be involved in Abl-induced disruption of cell adhesion, and verified that the knockdown of IL6ST is associated with enhanced cell attachment.

Conclusion

Our results suggest that the power of genome-wide quantitative shRNA screens can be significantly increased when analyzed using a systems biology-based approach to identify functional gene networks.