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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial 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...
Coordination of Gene Expression Processes in Bacteria01:29

Coordination of Gene Expression Processes in Bacteria

The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...
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
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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

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Protein Networks02:26

Protein 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.
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Related Experiment Video

Updated: Jul 19, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

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Published on: July 29, 2022

Coexpressionfinder: a new algorithm for finding groups of coexpressed genes.

Sergey I Rogov1, Kuvat T Momynaliev, Vadim M Govorun

  • 1Laboratory of Post-Genome Methods in Biology, Department of Molecular Biology and Genetics, Research Institute of Physico-Chemical Medicine, M.Pirogovskaya 1a, Moscow, Russia. sirogov@yahoo.com

Journal of Bioinformatics and Computational Biology
|September 29, 2006
PubMed
Summary

CoexpressionFinder, a new algorithm, identifies coordinated gene expression patterns from DNA array experiments more effectively than hierarchical clustering. Its parallel design enhances efficiency in finding gene groups with concordant expression changes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Analyzing gene expression data from DNA arrays is crucial for understanding biological processes.
  • Identifying groups of genes with coordinated expression patterns is a key challenge in transcriptomics.

Purpose of the Study:

  • To introduce CoexpressionFinder, a novel algorithm for identifying co-expressed gene groups from DNA array data.
  • To demonstrate CoexpressionFinder's superiority over existing methods like hierarchical clustering.

Main Methods:

  • Development of a new algorithm, CoexpressionFinder, designed for parallel execution.
  • Application of the algorithm to DNA array experimental data to identify gene expression patterns.

Main Results:

  • CoexpressionFinder identifies more complete and internally coordinated groups of gene expression vectors.
  • The algorithm successfully identifies a greater number of genes exhibiting coordinated expression.
  • The parallel processing capability of CoexpressionFinder enhances computational efficiency.

Conclusions:

  • CoexpressionFinder offers an improved approach for analyzing gene expression data.
  • The algorithm provides a more comprehensive and efficient method for discovering co-expressed gene modules.
  • CoexpressionFinder is available as a free Java application for the research community.