Evidence map›Paper›PMID 35752289›Full record

ArticleGenomics, proteomics & bioinformatics2023

GREPore-seq: A Robust Workflow to Detect Changes After Gene Editing Through Long-range PCR and Nanopore Sequencing.

Zi-Jun Quan, Si-Ang Li, Zhi-Xue Yang, Juan-Juan Zhao, Guo-Hua Li, Feng Zhang, Wei Wen, Tao Cheng, Xiao-Bing Zhang

Open access · goldAbstract read
In one paragraph

Article in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

10 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Optimization of CRISPR/Cas9 Gene Editing System in Sheep (International journal of molecular sciences · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
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

9 authors at 1 institution in 1 country.

Zi-Jun QuanState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Si-Ang LiState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Zhi-Xue YangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Juan-Juan ZhaoState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Guo-Hua LiState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Feng ZhangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Wei WenState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China. Electronic address: wenwei@ihcams.ac.cn.
Tao ChengState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China; Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin 300020, China; Department of Stem Cell & Regenerative Medicine, Peking Union Medical College, Tianjin 300020, China. Electronic address: chengtao@ihcams.ac.cn.
Xiao-Bing ZhangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China. Electronic address: zhangjianping@ihcams.ac.cn.
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To achieve the enormous potential of gene-editing technology in clinical therapies, one needs to evaluate both the on-target efficiency and unintended editing consequences comprehensively. However, there is a lack of a pipelined, large-scale, and economical workflow for detecting genome editing outcomes, in particular insertion or deletion of a large fragment. Here, we describe an approach for efficient and accurate detection of multiple genetic changes after CRISPR/Cas9 editing by pooled nanopore sequencing of barcoded long-range PCR products. Recognizing the high error rates of Oxford nanopore sequencing, we developed a novel pipeline to capture the barcoded sequences by grepping reads of nanopore amplicon sequencing (GREPore-seq). GREPore-seq can assess nonhomologous end-joining (NHEJ)-mediated double-stranded oligodeoxynucleotide (dsODN) insertions with comparable accuracy to Illumina next-generation sequencing (NGS). GREPore-seq also reveals a full spectrum of homology-directed repair (HDR)-mediated large gene knock-in, correlating well with the fluorescence-activated cell sorting (FACS) analysis results. Of note, we discovered low-level fragmented and full-length plasmid backbone insertion at the CRISPR cutting site. Therefore, we have established a practical workflow to evaluate various genetic changes, including quantifying insertions of short dsODNs, knock-ins of long pieces, plasmid insertions, and large fragment deletions after CRISPR/Cas9-mediated editing. GREPore-seq is freely available at GitHub (https://github.com/lisiang/GREPore-seq) and the National Genomics Data Center (NGDC) BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007293).

Indexed as

CRISPR-Cas SystemsGene EditingNanopore SequencingPolymerase Chain ReactionWorkflowDNA End-Joining RepairHigh-Throughput Nucleotide SequencingHumansCRISPR/Cas9Genetic changeGREPore-seqLong-range PCRNanopore sequencing

Identifiers

PMID35752289
PMCPMC11082256
OpenAlexW4283328461

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.