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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Estimating the strength of expression conservation from high throughput RNA-seq data.

Xun Gu1, Hang Ruan2,3, Jingwen Yang2,4

  • 1Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, USA.

Bioinformatics (Oxford, England)
|May 23, 2019
PubMed
Summary

We developed a new statistical model to estimate gene expression conservation across species. This method helps understand evolutionary patterns by analyzing RNA-seq data more effectively.

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Bioinformatics

Background:

  • Gene expression evolution is often stabilized to maintain optimal levels.
  • Estimating expression conservation across species is challenging due to limited data and complex parameters.

Purpose of the Study:

  • To develop a robust statistical model for quantifying gene expression conservation strength (W) across species.
  • To provide a reliable method for estimating W using high-throughput RNA-sequencing (RNA-seq) data.

Main Methods:

  • Developed a gamma distribution model to describe the variation in expression conservation strength (W).
  • Formulated an empirical Bayesian procedure to estimate W for individual genes using multi-species RNA-seq datasets.

Main Results:

  • Demonstrated the utility of estimated W values in studying evolutionary patterns of gene expression conservation.
  • Case studies confirmed the effectiveness of the developed model and estimation procedure.

Conclusions:

  • The gamma distribution model and empirical Bayesian approach offer a statistically sound method for estimating gene expression conservation.
  • The developed R-package, TreeExp, provides accessible tools for researchers to apply this method.