Genome Engineering allows the easy manipulation of genomes down to the nucleotide level. 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.
1. Discuss assays for evaluating on-target genome editing outcomes
2. Explain CRIS.py NGS pipeline
3. Discuss best practices for creating custom genome edited cell lines and animals