Developing next-generation methods for scoring CRISPR screening data

Abstract

Mapping the genotype-to-phenotype connection is a central goal of modern biology. CRISPR screening technology powers mapping efforts by identifying relationships between gene knockouts and conditions of interest, such as another gene knockout or the presence of a drug. Quantitative readouts from CRISPR screening experiments, however, are confounded by several different types of biases which impact the efficacy of genotype-to-phenotype mapping efforts. My contributions to the scientific community consist of several computational tools which address bias in CRISPR screening experiments. The first, Orthrus, addresses biases specific to combinatorial screening experiments which knock out multiple genes simultaneously. The second, Orobas, presents an improved scoring method for chemogenomic screening experiments and substantially reduces false positives compared to the existing state-of-the-art method. Lastly, I detail a novel technique called onion normalization for reducing technical bias in large-scale CRISPR screening experiments. Overall, these contributions serve as a methodological bedrock for the construction of improved genotype-to-phenotype maps from CRISPR screening data.