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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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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Related Experiment Video

Updated: May 6, 2026

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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Transcriptome-wide characterization of genetic perturbations.

Ajay Nadig1,2,3,4, Joseph M Replogle5,6, Angela N Pogson6

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

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|July 15, 2024
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Summary

We developed TRanscriptome-wide Analysis of Differential Expression (TRADE) to reveal subtle gene expression changes from noisy single-cell CRISPR screen data. TRADE uncovers widespread transcriptional effects, even from subtle genetic perturbations, improving our understanding of gene function.

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Last Updated: May 6, 2026

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Single-cell CRISPR screens, like Perturb-seq, offer large-scale transcriptomic profiling of genetic perturbations.
  • Noisy data from these screens often limits the detection of true biological effects using conventional methods.

Purpose of the Study:

  • Introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework to accurately estimate differential expression effects from noisy gene-level measurements.
  • Develop novel metrics, such as "transcriptome-wide impact," to quantify perturbation effects robustly across varying data depths.

Main Methods:

  • Developed the TRADE statistical framework for analyzing noisy gene expression data from large-scale Perturb-seq experiments.
  • Derived new metrics to estimate the distribution of true differential expression effects and the overall transcriptional impact of perturbations.
  • Applied TRADE to analyze existing and new Perturb-seq datasets, as well as case/control gene expression data for neuropsychiatric conditions.

Main Results:

  • Demonstrated that many true transcriptional effects are missed by conventional analyses but are detectable in aggregate using TRADE.
  • Quantified that typical gene perturbations affect ~45 genes, while essential gene perturbations impact >500 genes in a genome-scale screen.
  • Identified cell-type and dosage-dependent transcriptional effects of genetic perturbations and found transcriptomic correlations exceed genetic correlations in neuropsychiatric conditions.

Conclusions:

  • TRADE provides a robust statistical foundation for analyzing noisy transcriptomic data from genetic screens, revealing widespread gene regulatory effects.
  • The framework enables systematic comparison of genetic perturbation atlases and enhances differential expression analyses across diverse biological contexts.
  • TRADE facilitates a deeper understanding of gene function, perturbation effects, and their implications in complex diseases.