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Related Concept Videos

DNA Microarrays02:34

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Related Experiment Video

Updated: Nov 21, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Analysis framework and experimental design for evaluating synergy-driving gene expression.

Nadine Schrode1, Carina Seah2, P J Michael Deans2

  • 1Department of Genetics and Genomics, Pamela Sklar Division of Psychiatric Genomics, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Nature Protocols
|January 12, 2021
PubMed
Summary
This summary is machine-generated.

Understanding complex genetic disorders requires analyzing interactions between genetic risk variants and environmental factors. Our new RNA sequencing analysis method, developed for CRISPR studies, resolves additive and synergistic transcriptomic effects from combined perturbations.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Complex genetic disorders arise from intricate interactions between genetic variants and environmental factors.
  • Current analytical methods often fail to capture the non-additive (synergistic) effects of these combined influences.

Purpose of the Study:

  • To present a novel computational framework for resolving distinct additive and synergistic transcriptomic effects from combinatorial perturbations.
  • To provide a method applicable to any RNA sequencing dataset with raw read counts.

Main Methods:

  • Development of a computational pipeline for analyzing RNA sequencing data to identify synergistic interactions.
  • Application of the method to combinatorial manipulation of genetic variants and/or chemical perturbagens.
  • Focus on resolving non-additive effects beyond standard differential expression analysis.

Main Results:

  • The methodology successfully distinguishes between additive and synergistic transcriptomic effects.
  • The approach allows for the quantification of synergy between multiple perturbations.
  • The computational pipeline is accessible and can be run within a day.

Conclusions:

  • This analytic framework and experimental design facilitate the discovery of convergence and synergy in complex disease research.
  • The protocol is applicable to scientists with bioinformatics skills and R programming proficiency.
  • The tool aids in understanding gene and treatment perturbations contributing to complex diseases.