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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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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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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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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.
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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pathVar: a new method for pathway-based interpretation of gene expression variability.

Laurence de Torrente1, Samuel Zimmerman1, Deanne Taylor2,3

  • 1Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States of America.

Peerj
|June 1, 2017
PubMed
Summary

We introduce pathVar, a novel method for interpreting gene expression variability within biological pathways. This approach aids in understanding cellular phenotypes and regulatory control across various biological contexts.

Keywords:
BioinformaticsCellular heterogeneityFunctional genomicsGene expression variabilitySingle cell analysisTranscriptional regulation

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Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Understanding cellular phenotypes requires identifying regulatory pathways.
  • Changes in gene expression variability can highlight key regulatory genes.
  • Existing methods for analyzing pathway-level gene expression variability are limited.

Purpose of the Study:

  • To present pathVar, a new computational method for functional interpretation of gene expression variability at the pathway and gene set level.
  • To provide a tool for analyzing gene expression variability across diverse biological settings and data types.

Main Methods:

  • pathVar utilizes a multinomial exact test or an asymptotic Chi-squared test.
  • The method is applicable to any gene expression technology platform and experimental design (single or two-group comparisons).
  • Benchmarking involved comparison with average expression analyses and Gene Set Enrichment Analysis (GSEA).

Main Results:

  • pathVar offers functional interpretation of gene expression variability in pathways.
  • The method was applied to diverse datasets including cancer, developmental biology, and neurological disease.
  • Benchmarking demonstrated the utility of pathVar alongside traditional methods for understanding transcriptional regulation.

Conclusions:

  • pathVar enhances the understanding of gene expression variability in pathways.
  • The method provides insights into cellular phenotypes and regulatory mechanisms.
  • Recommendations for selecting variability statistics are provided based on simulations and real data analysis.