Author:
Cowley Glenn S,Weir Barbara A,Vazquez Francisca,Tamayo Pablo,Scott Justine A,Rusin Scott,East-Seletsky Alexandra,Ali Levi D,Gerath William FJ,Pantel Sarah E,Lizotte Patrick H,Jiang Guozhi,Hsiao Jessica,Tsherniak Aviad,Dwinell Elizabeth,Aoyama Simon,Okamoto Michael,Harrington William,Gelfand Ellen,Green Thomas M,Tomko Mark J,Gopal Shuba,Wong Terence C,Li Hubo,Howell Sara,Stransky Nicolas,Liefeld Ted,Jang Dongkeun,Bistline Jonathan,Hill Meyers Barbara,Armstrong Scott A,Anderson Ken C,Stegmaier Kimberly,Reich Michael,Pellman David,Boehm Jesse S,Mesirov Jill P,Golub Todd R,Root David E,Hahn William C
Abstract
Abstract
Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability