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Related Concept Videos

Transcription Factors02:16

Transcription Factors

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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...
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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...
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Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
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Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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MultiDCoX: Multi-factor analysis of differential co-expression.

Herty Liany1,2, Jagath C Rajapakse3, R Krishna Murthy Karuturi4,5

  • 1School of Computing, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.

BMC Bioinformatics
|January 4, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces MultiDCoX, a new algorithm for multi-factor differential co-expression analysis. It identifies gene sets and quantifies how factors like genetics influence gene co-expression.

Keywords:
Differential co-expressionGene expressionMulti-factor analysisMultiDCoX

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential co-expression (DCX) analysis identifies changes in gene co-expression across conditions.
  • Existing methods are limited to single-factor analysis, failing to account for multiple influencing factors.
  • Multi-factor analysis is crucial for studies involving genetic markers, clinical variables, and treatments.

Purpose of the Study:

  • To develop a novel algorithm for multi-factor differential co-expression analysis.
  • To address the limitations of existing single-factor DCX methods.
  • To identify gene sets and quantify the influence of multiple factors on co-expression.

Main Methods:

  • Developed MultiDCoX, a novel formulation and greedy search algorithm.
  • Employed simulated data to validate the algorithm's performance.
  • Applied MultiDCoX to a breast cancer dataset.

Main Results:

  • MultiDCoX effectively identifies differentially co-expressed (DCX) gene sets.
  • The algorithm quantifies the influence of individual factors on gene co-expression.
  • Analysis of breast cancer data revealed biologically meaningful DCX gene sets and influencing genetic/clinical factors.

Conclusions:

  • MultiDCoX provides a computationally efficient solution for multi-factor DCX analysis.
  • The method successfully identifies DCX gene sets and factor influences.
  • This approach enhances understanding of complex biological systems with multiple variables.