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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
General Transcription Factors01:30

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...

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Related Experiment Video

Updated: Jun 27, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.

Carlos Prieto1, Alberto Risueño, Celia Fontanillo

  • 1Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CIC-IBMCC, CSIC/USAL), Salamanca, Spain.

Plos One
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a robust method for creating a reliable human gene coexpression network from healthy tissues, minimizing noise. The resulting network maps gene interactions and functional relationships for further research.

Related Experiment Videos

Last Updated: Jun 27, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Genome-wide expression analysis using microarrays is crucial for identifying coexpression patterns.
  • Existing human studies often use heterogeneous datasets and lack robust statistical methods to address technical noise.
  • Accurate estimation of errors in gene expression data is frequently not provided.

Purpose of the Study:

  • To develop a robust method for constructing a confident human gene coexpression network.
  • To eliminate pathological and technical noise from genome-wide expression data.
  • To provide a validated resource for analyzing human gene interactions.

Main Methods:

  • Utilized a controlled set of normal-healthy human tissues for gene expression analysis.
  • Implemented robust normalization, signal calculation, and parametric/non-parametric correlation coefficients.
  • Employed random cross-validations and estimated statistical accuracy and data coverage.
  • Calculated true positive rates via biological pathway assignment to define error levels.

Main Results:

  • Generated a confident human gene coexpression network with 3327 gene-nodes and 15841 coexpression-links.
  • Demonstrated significant improvement over previously published gene coexpression datasets.
  • Identified coherent biological modules within the network, sharing common transcription factors.
  • Revealed that major network regions involve nuclear/mitochondrial metabolism, with over 60% essential/house-keeping genes.
  • Discovered novel gene associations and functionally contextualized unknown genes.

Conclusions:

  • Stable and reliable human gene coexpression networks are essential for understanding gene interactions at an omic scale.
  • The validated networks are made publicly available for further community research.
  • The network provides a valuable map of gene interactions and functional relationships.