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What is Gene Expression?

What is Gene Expression?

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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Related Experiment Video

Updated: May 15, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Predicting gene expression from DNA sequence using deep learning models.

Lucía Barbadilla-Martínez^1,2, Noud Klaassen^1,3, Bas van Steensel^4,5

¹Oncode Institute, Utrecht, The Netherlands.

Nature Reviews. Genetics

|May 13, 2025

Summary

This summary is machine-generated.

Deep learning models accurately predict gene activity from DNA sequences by analyzing epigenome and reporter assay data. These computational tools offer insights into gene regulation and potential applications in biotechnology.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Gene transcription is controlled by DNA elements like promoters and enhancers, influenced by transcription factors.
Predicting gene activity from DNA sequence is challenging due to complex combinatorial logic.

Purpose of the Study:

To discuss deep learning approaches for modeling gene regulation.
To review available training datasets and compare different methodologies.

Main Methods:

Application of deep learning techniques to epigenome mapping data.
Utilizing high-throughput reporter assays for training data generation.

Main Results:

Deep learning models capture gene regulatory grammar with high accuracy.
These models show promise in predicting non-coding variant effects and uncovering regulatory mechanisms.

Conclusions:

Deep learning advances computational modeling of gene regulation.
These methods have significant potential for predicting variant effects, understanding gene regulation, and synthetic biology applications.