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Differential Expression Analysis of Nothobranchius furzeri Transposable Elements from RNA-seq Data.

Bryan Teefy1, Matthew Malone1,2, Bérénice A Benayoun3,4,5,6,7

  • 1Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California 90089, USA.

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We developed three pipelines to analyze transposable element (TE) expression using RNA-seq data in the African turquoise killifish. These methods offer flexibility for various computational resources, aiding TE research in non-model organisms.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Transposable elements (TEs) constitute significant portions of eukaryotic genomes.
  • Analyzing TEs is challenging due to their repetitive nature and high copy numbers.
  • Standard bioinformatic pipelines often require adaptation for TE analysis.

Purpose of the Study:

  • To establish robust pipelines for analyzing transposable element (TE) expression from RNA-seq data.
  • To provide adaptable protocols for non-model organisms, specifically the African turquoise killifish (Nothobranchius furzeri).
  • To offer solutions for varying computational resource availability, including memory constraints.

Main Methods:

  • Development of three distinct bioinformatic pipelines for TE expression analysis.
  • Utilization of standard RNA-seq analysis software combined with TE-specialized tools.
  • Adaptation for both genomic and transcriptomic references, accommodating single-end and paired-end Illumina RNA-seq data.
  • Inclusion of adapter trimming, read alignment, counting, and differential expression analysis steps.

Main Results:

  • Successful implementation of three versatile pipelines for TE expression analysis.
  • Demonstration of protocol applicability using publicly available RNA-seq data from aging killifish.
  • Validation of the pipelines' ability to handle complex genomic data from a non-model organism.

Conclusions:

  • The presented pipelines provide a flexible and robust framework for analyzing transposable element expression in eukaryotic genomes.
  • These methods facilitate TE research in non-model organisms with limited genomic resources or computational power.
  • The protocol enables differential expression analysis of TEs, contributing to a deeper understanding of their role in biological processes like aging.