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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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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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No description available
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Chromatin Position Affects Gene Expression02:35

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
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LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data.

Maureen A Sartor1, George D Leikauf, Mario Medvedovic

  • 1Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, USA.

Bioinformatics (Oxford, England)
|November 29, 2008
PubMed
Summary

We developed LRpath, a logistic regression method for identifying gene sets in microarray data. This approach offers improved statistical power and robust performance compared to existing methods for analyzing differential gene expression.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Analyzing gene expression data is crucial for understanding biological pathways.
  • Current statistical methods for identifying enriched pathways in microarray data have limitations.
  • The optimal approach for pathway enrichment analysis remains an open question.

Purpose of the Study:

  • To introduce a novel logistic regression-based method, LRpath, for identifying biological pathways enriched with differentially expressed genes.
  • To compare the performance of LRpath against existing methods like Fisher's exact test.
  • To provide a robust tool for the analysis and interpretation of microarray data.

Main Methods:

  • Developed a logistic regression model (LRpath) to assess gene set enrichment.
  • Related the odds of gene set membership to differential gene expression significance.
  • Calculated adjusted P-values for statistical significance.
  • Compared LRpath with other methods using simulations and real breast cancer datasets.

Main Results:

  • LRpath demonstrated robust behavior and improved statistical power compared to alternative methods.
  • Simulation studies and analysis of breast cancer datasets showed concordant results.
  • The method effectively identifies biological pathways enriched or depleted of differentially expressed transcripts.

Conclusions:

  • LRpath is a powerful and reliable tool for pathway enrichment analysis in microarray data.
  • The method is applicable to experiments with multiple sample types.
  • LRpath provides valuable insights for researchers selecting appropriate statistical methods for gene expression analysis.