LAUSR

CRISPR-Cas9 technology allows the creation of user-defined genomic modifications in cells and whole organisms. However, quantifying editing rates in pools of cells or identifying correctly edited clones is tedious. Targeted next-generation sequencing provides a high-throughput platform for optimizing editing reagents and identifying correctly modified clones, but the large amount of data produced can be difficult to analyze. Here, we present CRIS.py, a simple and highly versatile python-based program which concurrently analyzes next-generation sequencing data for both knock-out and multiple user-specified knock-in modifications from one or many edited samples. Compared to available NGS analysis programs for CRISPR based-editing, CRIS.py has many advantages: (1) the ability to analyze from one to thousands of samples at once, (2) the capacity to check each sample for multiple sequence modifications, including those induced by base-editors, (3) an output in an easily searchable file format enabling users to quickly sort through and identify correctly targeted clones.