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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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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.
Forward genetic screens
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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Ultra-high-throughput mapping of genetic design space.

Kshitij Rai1,2, Ronan W O'Connell1,3, Trenton C Piepergerdes1,3

  • 1Department of Bioengineering, Rice University, Houston, TX, USA.

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Summary

CLASSIC is a new genetic screening platform that uses long- and short-read sequencing to analyze complex gene circuits. This enables high-throughput screening of thousands of designs, accelerating synthetic biology and revealing genetic part composability rules.

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Area of Science:

  • Synthetic Biology
  • Genomics
  • Molecular Biology

Background:

  • High-throughput genetic screens are crucial for understanding sequence-to-function relationships.
  • Current methods struggle with analyzing complex constructs containing multiple, long-range genetic elements.
  • Accelerating synthetic biology requires high-throughput screening of diverse gene circuit designs.

Purpose of the Study:

  • Introduce CLASSIC, a novel platform for high-throughput genetic screening of complex constructs.
  • Enable quantitative assessment of gene circuit pools with diverse genetic part compositions.
  • Reveal genetic part composability rules for data-driven design of complex genetic systems.

Main Methods:

  • CLASSIC combines long- and short-read next-generation sequencing (NGS) modalities.
  • Assesses pools of gene constructs with arbitrary lengths and diverse genetic part compositions.
  • Measures expression profiles of over 10^5 gene circuit designs (5-20 kb) in human cells.

Main Results:

  • CLASSIC enables quantitative assessment of thousands of gene circuit designs in a single experiment.
  • Generated datasets facilitate machine learning models for accurate prediction of circuit behavior.
  • Identified part composability rules governing circuit performance across expansive design landscapes.

Conclusions:

  • CLASSIC significantly enhances the pace and scale of synthetic biology research.
  • Establishes an experimental foundation for data-driven design of complex genetic systems.
  • Overcomes limitations in analyzing multi-kb length scale genetic constructs.