Analyzing long-read CRISPR experiments with CRISPRLungo.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Short-read sequencing has limitations in detecting complex genome editing outcomes like large deletions, insertions, and inversions.
• Existing analysis tools are not optimized for the specific allelic outcomes of gene editing.
• PCR and sequencing errors can complicate accurate variant detection in long-read data.

Purpose of the Study:

• To develop a computational pipeline, CRISPRLungo, for analyzing long-read amplicon sequencing data from gene-edited samples.
• To enable robust detection of small indels and structural variants resulting from gene editing.
• To provide an accessible tool for researchers to analyze complex gene editing events.

Main Methods:

• CRISPRLungo utilizes unique molecular identifier (UMI)-based error correction and statistical filtering.
• The pipeline is designed for long-read amplicon sequencing data from both Oxford Nanopore and PacBio platforms.
• Benchmarking was performed using simulated datasets to compare CRISPRLungo with existing approaches.

Main Results:

• CRISPRLungo demonstrates superior accuracy and read recovery compared to existing methods for analyzing gene editing outcomes.
• The tool successfully identified previously undetected structural variant edits, such as inversions, in published CRISPR datasets.
• CRISPRLungo accurately quantified allele-specific editing outcomes in patient-derived cells with compound heterozygous mutations, even with pseudogene interference.

Conclusions:

• CRISPRLungo provides a robust and accurate method for characterizing complex genome editing-induced DNA sequence changes using long-read sequencing.
• The developed client-side web application makes advanced long-read analysis accessible to researchers without extensive computational expertise.
• CRISPRLungo enhances the ability to detect and quantify gene editing events, facilitating advancements in genomic research and therapeutic development.