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Genetically Modifying CAR T Cells Using a CRISPR-Cas9 System02:55

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The video outlines a process for creating genetically modified CAR T cells through the CRISPR-Cas9 System. Infecting T cells with CRISPR and CAR lentiviruses results in modifications to the target gene and the synthesis of a chimeric antigen receptor or CAR, ultimately leading to the formation of genetically modified CAR T...
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Related Experiment Video

Updated: Jan 20, 2026

Genetically Modifying CAR T Cells Using a CRISPR-Cas9 System
02:55

Genetically Modifying CAR T Cells Using a CRISPR-Cas9 System

671

A benchmark of computational CRISPR-Cas9 guide design methods.

Jacob Bradford1, Dimitri Perrin1

  • 1School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia.

Plos Computational Biology
|August 30, 2019
PubMed
Summary

CRISPR-Cas9 guide design tools vary widely in performance and output. Most tools struggle with whole-genome analysis, highlighting a need for improved computational efficiency and consensus in guide selection for gene editing applications.

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Last Updated: Jan 20, 2026

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Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • CRISPR-Cas9 gene editing relies on effective guide design.
  • Numerous tools exist for designing CRISPR-Cas9 guides, but their performance is not well-characterized.
  • Ensuring guide specificity and efficiency is critical for successful gene editing experiments.

Purpose of the Study:

  • To comprehensively evaluate the performance of 18 CRISPR-Cas9 guide design tools.
  • To assess tools based on computational efficiency, resource requirements, and the quality of guides generated.
  • To identify limitations and areas for improvement in current guide design methodologies.

Main Methods:

  • Implemented a system resource auditing method to monitor tool execution.
  • Tested 18 distinct CRISPR-Cas9 guide design tools.
  • Utilized datasets of increasing size derived from the mouse genome, including collections with experimental validation data.

Main Results:

  • Only five out of 18 tools demonstrated computational performance suitable for whole-genome analysis within reasonable time and resource constraints.
  • Significant variation was observed in the guides identified by different tools, ranging from exhaustive lists to filtered sets based on predicted efficiency.
  • A notable lack of consensus was found among the tools regarding guide selection, with some failing to exclude off-target guides.
  • Tool performance varied across datasets, though relative rankings were partially conserved.

Conclusions:

  • Current CRISPR-Cas9 guide design tools require further development to support rapid whole-genome analysis.
  • Improvements in guide design are likely to necessitate the integration of multiple computational approaches.
  • The lack of consensus among tools underscores the complexity of optimizing guide specificity and efficiency for CRISPR applications.