Analyzing long-read CRISPR experiments with CRISPRLungo.

ABSTRACT: Long-read sequencing can characterize complex genome editing-induced DNA sequence changes such as large deletions, insertions, and inversions that are difficult to detect using short-read sequencing. However, PCR amplification and sequencing errors complicate accurate variant detection, and existing analysis tools are not optimized for gene editing specific allelic outcomes. Here we present CRISPRLungo, a computational pipeline specifically designed for long-read amplicon sequencing of gene edited samples. CRISPRLungo incorporates unique molecular identifier (UMI)-based error correction and statistical filtering to distinguish true editing events from background noise, enabling robust detection of small indels and structural variants. Through systematic benchmarking using simulated dataset

SUBMITTER: Hwang GH 

PROVIDER: S-EPMC12633380 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature