Related Experiment Video
Updated: Aug 16, 2026
07:28
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Computational, integrative, and comparative methods for the elucidation of genetic coexpression networks
Nicole E Baldwin1, Elissa J Chesler, Stefan Kirov
1Department of Computer Science, The University of Tennessee, Knoxville, TN 37996, USA.
Journal of Biomedicine & Biotechnology
|July 28, 2005
Summary
Researchers created genetic coexpression networks using mouse gene expression data to understand complex traits. This approach integrates genetic variation and phenotypes, revealing co-regulated genes and their causal basis.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Gene expression microarray data enables the construction of genetic coexpression network graphs.
- Recombinant inbred mouse strains provide a valuable resource for integrating allelic variation with molecular and higher-order phenotypes.
Purpose of the Study:
- To enhance the quantitative genetic analysis of microarray data.
- To build comprehensive genetic coexpression networks across multiple biological scales.
Main Methods:
- Utilizing mRNA samples from recombinant inbred Mus musculus strains.
- Applying powerful computational algorithms, platforms, and data repositories.
- Employing gene ontology analysis and cis-regulatory element discovery for annotation.
Main Results:
- Network graphs were generated, spanning diverse biological levels.
- Cliques of putatively co-regulated genes were extracted and annotated.
- Quantitative trait locus mapping was used to detect the causal basis for co-regulation.
Conclusions:
- Integrating allelic variation with gene expression data in mice enhances genetic analysis.
- This approach facilitates the discovery of co-regulated genes and their underlying causal mechanisms.
Related Concept Videos
02:26Protein Networks
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:33Combinatorial Gene Control
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
01:09Epistasis Analysis
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
02:54Evolutionary Relationships through Genome Comparisons
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
02:34DNA 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...