High-Throughput Analysis of CRISPR-Cas9 Editing Outcomes in Cell and Animal Models Using CRIS.py.

ABSTRACT: Genome editing using the CRISPR-Cas9 platform creates precise modifications in cells and whole organisms. Although knockout (KO) mutations can occur at high frequencies, determining the editing rates in a pool of cells or selecting clones that contain only KO alleles can be a challenge. User-defined knock-in (KI) modifications are achieved at much lower rates, making the identification of correctly modified clones even more challenging. The high-throughput format of targeted next-generation sequencing (NGS) provides a platform allowing sequence information to be gathered from a one to thousands of samples. However, it also poses a challenge in terms of analyzing the large amount of data that is generated. In this chapter, we present and discuss CRIS.py, a simple and highly versatile Python

SUBMITTER: Narina S 

PROVIDER: S-EPMC10427880 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature