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Genome-wide RNAi screening is a powerful, yet relatively immature technology that allows investigation into the role of individual genes in a process of choice. Most RNAi screens identify a large number of genes with a continuous gradient in the assessed phenotype. Screeners must then decide whether to examine just those genes with the most robust phenotype or to examine the full gradient of genes that cause an effect and how to identify the candidate genes to be validated. We have used RNAi in Drosophila cells to examine viability in a 384-well plate format and compare two screens, untreated control and treatment. We compare multiple normalization methods, which take advantage of different features within the data, including quantile normalization, background subtraction, scaling, cellHTS2 1, and interquartile range measurement. Considering the false-positive potential that arises from RNAi technology, a robust validation method was designed for the purpose of gene selection for future investigations. In a retrospective analysis, we describe the use of validation data to evaluate each normalization method. While no normalization method worked ideally, we found that a combination of two methods, background subtraction followed by quantile normalization and cellHTS2, at different thresholds, captures the most dependable and diverse candidate genes. Thresholds are suggested depending on whether a few candidate genes are desired or a more extensive systems level analysis is sought. In summary, our normalization approaches and experimental design to perform validation experiments are likely to apply to those high-throughput screening systems attempting to identify genes for systems level analysis.

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Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS–induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into “pathway nodes” qualitatively improved the interactome organization, revealing a highly organized “MMS survival network.” We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis., Author Summary Cellular damage is known to elicit a pleiotropic response, but the relative importance of the constituent components in cell survival is poorly understood. To provide an unbiased identification of the proteins utilized in damage survival, we performed an RNAi survival screen in fly cells with methyl methanesulfonate (MMS). The genes identified are involved in 13 biologically diverse pathways. Comparison with analogous yeast data demonstrated a lack of conservation of the individual MMS survival genes but a conservation of pathways. We went on to demonstrate the MMS responsiveness for five representative pathways in both fly and mouse cells. We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. Incorporation of pathway data in interactome analysis also improved connectivity and, more importantly, revealed a biologically intuitive, highly inter-connected “MMS survival network.” This pathway conservation and inter-connectivity implies extensive interaction between pathways; for diseases such as cancer, such crosstalk may dictate disparate cellular responses not necessarily expected and confound treatments that are not tailored to the individual molecular context.