St. Jude Children's Research Hospital, Department of Cell & Molecular Biology, Memphis, TN, USA.
St. Jude Children's Research Hospital, Center for Advanced Genome Engineering, Memphis, TN, USA.
Methods Mol Biol. 2023;2631:155-182. doi: 10.1007/978-1-0716-2990-1_6.
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.
使用 CRISPR-Cas9 平台进行基因组编辑可以在细胞和整个生物体中实现精确的修饰。虽然敲除 (KO) 突变可以以高频率发生，但确定细胞群中的编辑率或选择仅包含 KO 等位基因的克隆可能是一个挑战。用户定义的敲入 (KI) 修饰的发生率要低得多，这使得正确修饰的克隆的鉴定更加具有挑战性。靶向下一代测序 (NGS) 的高通量格式提供了一个平台，允许从一个到数千个样本中收集序列信息。然而，它在分析生成的大量数据方面也提出了挑战。在本章中，我们介绍并讨论了 CRIS.py，这是一个简单而功能强大的基于 Python 的程序，用于分析用于基因组编辑结果的 NGS 数据。CRIS.py 可用于分析用户指定的任何类型的修饰或多重修饰的测序结果。此外，CRIS.py 可在目录中找到的所有 fastq 文件上运行，从而同时分析所有唯一索引的样本。CRIS.py 的结果被合并到两个摘要文件中，这允许用户对结果进行排序和筛选，并快速识别最感兴趣的克隆（或动物）。
