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Related Concept Videos

Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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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.
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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...
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Epistasis 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...

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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge.

Wentao Zhao1, Erchin Serpedin, Edward R Dougherty

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.

EURASIP Journal on Bioinformatics & Systems Biology
|April 25, 2009
PubMed
Summary

This study introduces a new algorithm to identify genes involved in cyclic biological processes by integrating prior knowledge with gene expression analysis. This method enhances the discovery of genes related to cell cycles and circadian rhythms.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Cyclic genes, crucial for processes like cell cycle and circadian rhythm, are typically identified using spectral analysis and statistical tests.
  • Existing methods often use prior biological knowledge solely for evaluation, not for algorithm implementation, and disregard non-stationary data.
  • This limits the comprehensive identification of genes involved in cyclic biological processes.

Purpose of the Study:

  • To develop a novel algorithm for identifying cyclic-process-involved genes.
  • To integrate prior biological knowledge directly into gene expression analysis for improved accuracy.
  • To address limitations of existing methods, including non-stationary data handling.

Main Methods:

  • A new algorithm was developed to identify cyclic genes by integrating prior knowledge with time series gene expression data.
  • The algorithm was applied to gene expression datasets from Saccharomyces cerevisiae and Drosophila melanogaster.
  • Spectral analysis and statistical periodicity detection were used, enhanced by prior knowledge integration.

Main Results:

  • The algorithm successfully identified genes involved in cyclic processes, validated by biological evidence.
  • Discovered genes were confirmed to play roles in cell cycle and circadian rhythm.
  • Dendrograms were used to cluster identified genes and visualize expression patterns.

Conclusions:

  • The proposed novel identification scheme is a valuable technique for uncovering pathways related to cyclic biological processes.
  • Integrating prior knowledge significantly improves the identification of cyclic genes.
  • The method offers a more comprehensive approach to analyzing gene expression data for cyclic patterns.