Evidence map›Paper›PMID 29989077›Full record

ArticleInternational journal of biological sciences2018

CRISPRMatch: An Automatic Calculation and Visualization Tool for High-throughput CRISPR Genome-editing Data Analysis.

Qi You, Zhaohui Zhong, Qiurong Ren, Fakhrul Hassan, Yong Zhang, Tao Zhang

Abstract read
In one paragraph

Article in International journal of biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–field-weighted citation impact
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

46 citing papers in PubMed.

  1. Article
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  3. Transposase-Assisted Donor Tethering Boosts Large-Fragment HDR in Plants.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Enhanced Genome Editing Activity with Novel Chimeric ScCas9 Variants in Rice.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
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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.

Qi YouJiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Centre for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou 225009, China.
Zhaohui ZhongDepartment of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Qiurong RenDepartment of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Fakhrul HassanDepartment of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Yong ZhangDepartment of Biotechnology, School of Life Science and Technology, Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Tao ZhangJiangsu Key Laboratory of Crop Genetics and Physiology, Co-Innovation Centre for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou 225009, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Custom-designed nucleases, including CRISPR-Cas9 and CRISPR-Cpf1, are widely used to realize the precise genome editing. The high-coverage, low-cost and quantifiability make high-throughput sequencing (NGS) to be an effective method to assess the efficiency of custom-designed nucleases. However, contrast to standardized transcriptome protocol, the NGS data lacks a user-friendly pipeline connecting different tools that can automatically calculate mutation, evaluate editing efficiency and realize in a more comprehensive dataset that can be visualized. Here, we have developed an automatic stand-alone toolkit based on python script, namely CRISPRMatch, to process the high-throughput genome-editing data of CRISPR nuclease transformed protoplasts by integrating analysis steps like mapping reads and normalizing reads count, calculating mutation frequency (deletion and insertion), evaluating efficiency and accuracy of genome-editing, and visualizing the results (tables and figures). Both of CRISPR-Cas9 and CRISPR-Cpf1 nucleases are supported by CRISPRMatch toolkit and the integrated code has been released on GitHub (https://github.com/zhangtaolab/CRISPRMatch).

Indexed as

AnimalsClustered Regularly Interspaced Short Palindromic RepeatsGene EditingHigh-Throughput Nucleotide SequencingHumansautomatic pipelineCRISPRgenome-editing efficiencymutation calculationNGS data

Identifiers

PMID29989077
PMCPMC6036748

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.