The recent development of genome-wide CRISPR/Cas9 screening technology (?CRISPR screens?) identifies functional genes associated with phenotype of interest in a fast and high-throughput manner. Besides protein-coding genes, novel screening techniques enable the functional interrogation of non- coding elements and genetic interactions. We have developed a series of computational algorithms and softwares for the design, quality control, analysis, visualization and interpretation of CRISPR screens. Among these, the MAGeCK/MAGeCK-VISPR algorithms have been widely used for analyzing screening data. In this proposal, we aim to develop the statistical and computational models to improve the functional interrogation of protein-coding genes, and to extend it to study non-coding elements and genetic interactions. Specifically, we propose to:
Aim 1. Improve functional gene identification from CRISPR screens, from integrating screening data from heterogenous background and viewing the data in a pathway manner;
Aim 2. Develop the design and analysis algorithms for non-coding CRISPR functional studies, and predict functional enhancers across various cell types.
Aim 3. Study genetic interactions from CRISPR screens targeting gene pairs, by modeling this novel type of screening data. At the conclusion of these studies, we will have developed several analysis algorithms for CRISPR screens of various types, facilitating the functional studies of genes, non-coding elements and genetic interactions. These algorithms will be made easy and convenient for experimental biologists to answer important biological questions about the functions of protein-coding genes, non-coding elements and genetic interactions.
Genome-wide CRISPR/Cas9 screening provides a cost-effective, high-throughput approach interrogate the functions of protein-coding genes, non-coding elements and genetic interactions. However, multiple computational challenges exist to model the screening data of various backgrounds and platforms, and to integrate with non-screening data for novel biological discovery. We propose to develop the statistical and computational methods to address these challenges, enabling a systematic study of genes, non-coding elements and genetic interactions in various biological systems and disease types.
