Operationalizing Genome Editing Across a Broad Range of Genomic and Cellular Targets

Genome Engineering allows the easy manipulation of genomes down to the nucleotide level. CRISPR is a revolutionizing tool for generating custom edited cell and pre-clinical animal models for research. Targeted deep sequencing enables the detection and quantification of low-frequency editing events. However, the large amounts of data generated by targeted deep sequencing can be difficult to interpret and quickly analyze. We have developed a Python-based computer program called CRIS.py that allows the easy analysis of multiple types of editing events. We will show examples of how rapid deep sequence analysis has guided experimental design leading to high-efficiency genome editing in a broad range of applications. Additionally, we will discuss best practices for creating knockin cell and animal models including large insertions using long ssDNA.

Learning Objectives:

1. Explain how NGS analysis enables detection and quantification of CRISPR editing outcomes on a diverse range of sample inputs including cell pools, clonal populations, and animal tissues

2. Recognize CRIS.py is a user friendly NGS analysis pipeline that is open source

3. Discuss best practices for creating knock in cell lines 4. Identify that NGS can be used to genotype both small and large insertion events