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-based program for analyzing NGS data for genome-editing outcomes. CRIS.py can be used to analyze sequencing results for any kind of modification or multiplex modifications specified by the user. Moreover, CRIS.py runs on all fastq files found in a directory, thereby concurrently analyzing all uniquely indexed samples. CRIS.py results are consolidated into two summary files, which allows users to sort and filter results and quickly identify the clones (or animals) of greatest interest.

Keywords: CRIS.py; CRISPR-Cas9; Gene modifications; Genome; Genome editing; Genome engineering; Knock-in; Knockout; Next-generation sequencing; Python.