Evidence map›Paper›PMID 42477873›Full record

ArticleBioinformatics (Oxford, England)2026

ALPINE: a scalable pipeline for comprehensive classification of gene-editing outcomes from long-read amplicon sequencing.

Yu Chen, Xing-Huang Gao, Athea Vichas, Jianbin Wang, Ryan Golhar, Isaac Neuhaus

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yu ChenResearch and Development, Bristol-Myers Squibb Company, Princeton, NJ 08540, United States.
Xing-Huang GaoProduct Development & Supply, Bristol-Myers Squibb Company, Seattle, WA 98109, United States.
Athea VichasProduct Development & Supply, Bristol-Myers Squibb Company, Seattle, WA 98109, United States.
Jianbin WangProduct Development & Supply, Bristol-Myers Squibb Company, Seattle, WA 98109, United States.
Ryan GolharResearch and Development, Bristol-Myers Squibb Company, Princeton, NJ 08540, United States.
Isaac NeuhausResearch and Development, Bristol-Myers Squibb Company, Princeton, NJ 08540, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

summaryCRISPR 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.

Indexed as

Gene EditingHigh-Throughput Nucleotide SequencingSequence Analysis, DNASoftwareCRISPR-Cas SystemsDNA RepairHumans

Identifiers

PMID42477873
PMCPMC13395072

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.