ALPINE: A Scalable Pipeline for Comprehensive Classification of Gene-Editing Outcomes from Long-Read Amplicon Sequencing

Clicks: 6
ID: 321935
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #158 of 818 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 818 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
SUMMARY: CRISPR genome editing has enabled precise genetic modification for gene and cell therapies, but edits often produce heterogeneous on-target outcomes, including homology-directed repair (HDR) knock-ins, DNA repair template integrations, and structural variants. Existing tools are frequently limited to short reads or lack viral vector-specific integration categories needed for therapeutic development. Here, we present ALPINE (Amplicon Long-read Pipeline for INtegration Evaluation), a scalable and reproducible pipeline for classifying and quantifying gene-editing outcomes from long-read amplicon sequencing supporting both PacBio HiFi and Oxford Nanopore platforms. ALPINE classifies reads into 10+ categories, including DNA repair vector integration subtypes, and performs variant calling near the gene-edited site with batch, multi-sample reporting. Uniquely, ALPINE can distinguish between cells treated with multiple DNA repair vectors and identify distinct molecular features, such as inverted terminal repeats (ITRs), enabling comprehensive characterization of complex gene editing outcomes. Dual-target benchmarking on simulated datasets demonstrated high accuracy for transgene integration events. Independent validation on public crosslinked-HDR dataset confirmed ALPINE's integration detection capabilities, and application to edited T cell samples demonstrated comprehensive gene-editing outcome profiling. AVAILABILITY: ALPINE is available under MIT license at https://github.com/Maggi-Chen/ALPINE and https://doi.org/10.5281/zenodo.20272510. All analysis scripts and visualization code used in this manuscript are available at https://github.com/Maggi-Chen/ALPINE-manuscript-analysis. Simulated datasets are deposited at Zenodo (https://doi.org/10.5281/zenodo.20260865). Public dataset PRJNA913199 is available through NCBI SRA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7169844532 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yu Chen, Xing-Huang Gao, Athea Vichas, Jianbin Wang, Ryan Golhar, Isaac Neuhaus
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag528
URL
https://doi.org/10.1093/bioinformatics/btag528
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.