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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Mixture modeling of transcript abundance classes in natural populations.

Wen-Ping Hsieh1, Gisele Passador-Gurgel, Eric A Stone

  • 1Department of Genetics, Gardner Hall, North Carolina State University, Raleigh, North Carolina 27695-7614, USA.

Genome Biology
|June 6, 2007
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Summary

Evolutionary forces shape gene expression variation. Cis- and trans-acting factors, alongside genetic drift and selection, influence transcript abundance distributions within populations.

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Area of Science:

  • Evolutionary Biology
  • Genomics
  • Molecular Biology

Background:

  • Populations diverge in genotype and phenotype due to evolutionary processes like genetic drift, mutation, and natural selection.
  • Understanding how these factors influence transcriptional variation is crucial as genotype maps to phenotype via transcription.
  • This study investigates the distributions and contributions of cis-acting and trans-acting factors to transcript abundance variation.

Purpose of the Study:

  • To explore the distributions of cis-acting and trans-acting factors influencing transcript abundance.
  • To evaluate the relative contributions of these factors to expression variation within populations.
  • To determine if evolutionary processes impact transcriptional variation patterns.

Main Methods:

  • Expression profiling of Drosophila melanogaster adult female heads using cDNA microarrays.
  • Analysis of nearly isogenic lines from two distinct populations (North Carolina and California).
  • Application of mixture modeling to identify transcripts with multimodal abundance distributions.

Main Results:

  • Identified transcripts exhibiting more than one mode of abundance across samples, with distributions skewed toward low-frequency minor classes.
  • Determined that sample sizes of approximately 50 individuals are sufficient for detecting divergent transcript abundance classes.
  • Observed similar patterns in human lymphoblast cell lines, where cis-acting single nucleotide polymorphisms showed a modest contribution to bimodal transcript abundance.

Conclusions:

  • Population surveys of gene expression can quantify sources of transcriptional variation, complementing genetical genomics.
  • Differential transcript expression among individuals arises from a complex interplay of cis-acting and trans-acting factors.
  • Evolutionary factors significantly influence the structure of variation at the transcriptional level.