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What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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Regulation of Expression at Multiple Steps01:23

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Regulation of Expression Occurs at Multiple Steps02:24

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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Protocol for predicting single- and multiple-dose-dependent gene expression using deep generative learning.

Derek E Bowman1, Vishal Panda2, Daniel Marri3

  • 1Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, USA; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA; College of Osteopathic Medicine, Michigan State University, East Lansing, MI, USA.

STAR Protocols
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

We present scVIDR, a variational autoencoder (VAE) model for analyzing single-cell gene expression under chemical doses. This protocol details training the model and predicting gene expression for dose-response studies.

Keywords:
BioinformaticsGenomicsSystems biology

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity.
  • Modeling gene expression is crucial for understanding cellular responses to stimuli.
  • Existing methods may not fully capture dose-dependent effects in single-cell data.

Purpose of the Study:

  • To introduce single-cell variational inference of dose response (scVIDR), a VAE tailored for scRNA-seq data.
  • To provide a protocol for utilizing scVIDR to model gene expression under varying chemical perturbations.
  • To facilitate the analysis of dose-dependent biological responses at the single-cell level.

Main Methods:

  • Development of scVIDR, a VAE specifically designed for dose-response modeling in scRNA-seq.
  • Utilizing Docker for accessible code and data management of the scVIDR tool.
  • Detailed procedures for training the scVIDR model and predicting gene expression patterns.

Main Results:

  • The scVIDR protocol enables effective modeling of single-cell gene expression.
  • The method allows for the prediction of gene expression changes in response to chemical dose.
  • The use of Docker ensures reproducibility and ease of access to the scVIDR framework.

Conclusions:

  • scVIDR offers a robust computational framework for analyzing dose-dependent chemical perturbations in scRNA-seq data.
  • The protocol provides a practical guide for researchers to implement and apply scVIDR.
  • This approach enhances the understanding of cellular responses to chemical treatments.