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ABSTRACT: CRISPR-based screens allow researchers to directly perturb genes at a genome wide scale and measure their association with a chosen phenotype. CRISPR screens promise researchers a higher a sensitivity and specificity than competing technologies like RNAi interference. However, there are major issues that can complicate the statistical analysis of the data arising from these screens. Particularly for CRISPR interference and activation screens, the problem of variable guide efficiency makes it difficult to identify true associations. I will discuss mixture model-based approaches to account for variable guide efficiency in the analysis of CRISPR screening data, how they can improve analysis, and the consequences of how we can think about CRISPR screens.BIO: Daley is postdoctoral scholar at Stanford University with Wing Hung Wong of the Department of Statistics and Stanley Qi of the Department of Bioengineering, working on statistical problems in single cell and CRISPR technologies. He obtained his Ph.D. in Applied Mathematics from the University of Southern California under the tutelage of Andrew D Smith and Michael S. Waterman. He earned both his bachelor’s and master’s degrees from Tulane University.