Analysing long-read CRISPR experiments with CRISPRLungo.

Authors

Gue-Ho Hwang, Benjamin Vyshedskiy, Timothy Barry, Jing Zeng, Sébastien Levesque, John P Manis, Akiko Shimamura, Daniel E Bauer, Luca Pinello

Published in

Nature biomedical engineering. Aug 20, 2026. Epub Aug 20, 2026.

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-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 datasets, we demonstrate that CRISPRLungo outperforms existing approaches in both accuracy and read recovery. CRISPRLungo supports both Oxford Nanopore and PacBio platforms and identifies previously undetected structural variant edits such as inversions in published CRISPR datasets. To demonstrate allele-specific edit quantification, we applied CRISPRLungo to analyse edited primary cells from a patient harbouring compound heterozygous SBDS mutations, accurately quantifying SBDS editing outcomes despite contaminating reads from the homologous SBDSP1 pseudogene. To maximize accessibility, we developed a fully client-side web application requiring no installation, making advanced long-read analysis accessible to researchers regardless of computational expertise. CRISPRLungo is freely available at https://github.com/pinellolab/CRISPRLungo with a user-friendly web interface available at https://pinellolab.github.io/CRISPRLungo .

PMID:
42624993
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.

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