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Related Concept Videos

Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Pooled CRISPR-Based Genetic Screens in Mammalian Cells
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A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.

Chang Sik Kim1, Jonathan Cairns1, Valentina Quarantotti2

  • 1Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, England.

Plos One
|August 20, 2024
PubMed
Summary

We developed a statistical simulation model to help researchers choose the best computational workflow for analyzing arrayed CRISPR screening data. This tool aids in selecting appropriate normalization and hit-calling methods for functional genomics experiments.

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Area of Science:

  • Functional genomics
  • High-throughput screening
  • Bioinformatics

Background:

  • Arrayed CRISPR screening is a powerful functional genomics technique.
  • Selecting optimal computational workflows for data analysis remains challenging.
  • Existing methods lack systematic guidance for workflow selection.

Purpose of the Study:

  • To develop a statistical simulation model for arrayed CRISPR screening data.
  • To provide a systematic approach for evaluating data analysis workflows.
  • To guide the selection of normalization and hit-calling methods.

Main Methods:

  • Developed a flexible statistical simulation model mimicking CRISPR screening experiments.
  • Simulated effects of gene editing, biological, and technical variations.
  • Utilized the model to assess different normalization and hit-calling strategies.

Main Results:

  • The simulation model effectively mimics arrayed CRISPR screening data generation.
  • Demonstrated the model's utility in guiding the choice of analysis methods.
  • Showcased principled selection of normalization and hit-calling through two examples.

Conclusions:

  • The developed statistical simulation model offers a systematic approach to arrayed CRISPR screening data analysis.
  • This tool assists researchers in making informed decisions for workflow selection.
  • An R package implementing the model is available for broader use.