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Genetic Screens

Genetic Screens

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 result in visible changes...

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Related Experiment Video

Updated: Jun 21, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

Using relative importance methods to model high-throughput gene perturbation screens.

Ying Jin¹, Naren Ramakrishnan, Lenwood S Heath

¹Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA. jiny@cs.vt.edu

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference

|August 1, 2009

Summary

This study introduces a new method for predicting gene functions by modeling phenotype relationships, improving accuracy for genes without known interactions. This approach enhances understanding of gene-phenotype connections.

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Last Updated: Jun 21, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

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High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Published on: March 3, 2015

Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

High-throughput gene perturbation screens generate vast data for understanding gene-phenotype relationships.
Current 'guilt by association' methods struggle with genes lacking known interactions.
Accurate gene function prediction is crucial for biological research.

Purpose of the Study:

To develop a novel computational approach for predicting gene functions.
To address limitations of existing methods for genes with sparse interaction data.
To improve the modeling of complex gene-phenotype relationships.

Main Methods:

Developed a method to model relationships between phenotypes using 'relative importance'.
Utilized derived phenotype relationships for phenotype prediction.
Applied the approach to S. cerevisiae deletion mutant and C. elegans knock-down datasets.

Main Results:

Achieved improved accuracy in phenotype prediction compared to existing methods.
Demonstrated the method's effectiveness on yeast and worm genetic datasets.
Provided new insights into the relationships between different biological phenotypes.

Conclusions:

The proposed method offers a more robust way to predict gene functions, especially for uncharacterized genes.
This approach enhances the interpretability of gene-phenotype networks.
The findings contribute to a deeper understanding of biological systems through advanced computational modeling.