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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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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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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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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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Predictability of human differential gene expression.

Megan Crow1, Nathaniel Lim2,3,4, Sara Ballouz1

  • 1Stanley Center for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724.

Proceedings of the National Academy of Sciences of the United States of America
|March 9, 2019
PubMed
Summary
This summary is machine-generated.

Many differential expression (DE) results are not specific to the biological question. A new approach using prior probability of DE reveals that many gene lists reflect generic biological processes, not specific signals.

Keywords:
differential expressionmetaanalysisreplicabilityspecificitytranscriptomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Differential expression (DE) analysis is a cornerstone of molecular biology research, identifying genes that change between biological conditions.
  • However, the specificity of DE results is often not rigorously assessed, potentially leading to misinterpretations, especially in meta-analyses.
  • Generic biological processes can create recurring DE patterns across studies, confounding the identification of condition-specific signals.

Purpose of the Study:

  • To develop and validate a method for assessing the specificity of differential expression results.
  • To investigate the prevalence of nonspecific DE signals across a large number of biological studies.
  • To provide a framework for more nuanced interpretation of gene-phenotype associations.

Main Methods:

  • Analysis of over 600 transcriptomic studies to build a predictor of differential expression based on empirical prior probabilities.
  • Evaluation of predictor performance using the area under the receiver operating characteristic curve (AUC).
  • Comparison of the prior probability predictor against predictors based on gene function, mutation rates, and network features.
  • Application of the DE prior in case studies including breast cancer subtyping, single-cell genomics, and transcriptomic meta-analyses.

Main Results:

  • A predictor based on the empirical prior probability of DE achieved high performance (AUC ≈ 0.8), indicating a substantial proportion of nonspecific DE findings.
  • Predictors utilizing gene function, mutation rates, or network features performed poorly in comparison.
  • Genes related to sex, extracellular matrix, immune system, and stress responses were enriched in the 'DE prior', reflecting shared biological processes.
  • Control studies confirmed these patterns represent genuine biological signals, not technical artifacts or biases.

Conclusions:

  • A significant fraction of reported differential expression results are not specific to the biological condition under investigation.
  • The 'DE prior' highlights common biological processes that can lead to recurring, but nonspecific, DE patterns.
  • Nuanced interpretation of gene associations is crucial, particularly in complex analyses like meta-analysis and subtyping.