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Related Concept Videos

Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
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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.
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What is Gene Expression?01:42

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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
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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
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Related Experiment Video

Updated: May 31, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

An integrative clustering and modeling algorithm for dynamical gene expression data.

Julia Sivriver1, Naomi Habib, Nir Friedman

  • 1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for analyzing noisy gene expression data over time. The method effectively clusters and models cellular responses to stimuli like inflammation and viruses, revealing key regulatory insights.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Gene expression dynamics are critical for cellular responses to stimuli.
  • Analyzing time-course gene expression data presents challenges due to noise and irregular sampling intervals.

Purpose of the Study:

  • To develop a novel algorithm for analyzing time-course gene expression data.
  • To enable robust clustering and dynamic modeling of gene expression responses.
  • To facilitate comparisons of cellular responses to different stimuli at a dynamical level.

Main Methods:

  • Developed an algorithm that integrates clustering of time-course gene expression data with the estimation of dynamic models.
  • The algorithm estimates biologically meaningful parameters for response dynamics.
  • The approach overcomes limitations inherent in separate clustering or modeling tasks.

Main Results:

  • Successfully analyzed dynamical transcriptional responses to inflammation and anti-viral stimuli in mouse dendritic cells.
  • Extracted a concise representation of different dynamical response types.
  • Identified similarities and differences between responses to the two stimuli.
  • Discovered potential regulators of complex transcriptional responses.

Conclusions:

  • The developed algorithm provides a powerful tool for analyzing complex gene expression dynamics.
  • It enables a deeper understanding of cellular responses to various stimuli.
  • The findings offer insights into the regulation of immune responses.