BiNGS!SL-seq: A Bioinformatics Pipeline for the Analysis and Interpretation of Deep Sequencing Genome-Wide Synthetic Lethal Screen
While targeted therapies have shown clinical promise, these therapies are rarely curative for advanced cancers. The discovery of pathways for drug compounds can help to reveal novel therapeutic targets as rational combination therapy in cancer treatment. With a genome-wide shRNA screen using high-throughput genomic sequencing technology, we have identified gene products whose inhibition synergizes with their target drug to eliminate lung cancer cells. In this chapter, we described BiNGS!SL-seq, an efficient bioinformatics workflow to manage, analyze, and interpret the massive synthetic lethal screen data for finding statistically significant gene products. With our pipeline, we identified a number of druggable gene products and potential pathways for the screen in an example of lung cancer cells.
Key wordsNext generation sequencing shRNA Synthetic lethal screen
The authors unreservedly acknowledge the experimental and computational expertise of the BiNGS! Team – James DeGregori, Christopher Porter, Joaquin Espinosa, S. Gail Eckhart, John Tentler, Todd Pitts, Mark Gregory, Matias Casa, Tzu Lip Phang, Dexiang Gao, Hyunmin Kim, Tiejun Tong, and Heather Selby.
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