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Inference of Ancient Polyploidy Using Transcriptome Data.

Jia Li1,2, Yves Van de Peer3, Zhen Li4

  • 1Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.

Methods in Molecular Biology (Clifton, N.J.)
|January 31, 2023
PubMed
Summary

Whole-genome duplications (WGDs) in plants are vital for evolution. Transcriptome data, while differing from genomic data, can reliably detect WGDs, aiding evolutionary studies.

Keywords:
Ancient polyploidyKS age distributionRNA-SeqTranscriptome assemblyWGD

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Area of Science:

  • Plant evolutionary biology
  • Genomics
  • Bioinformatics

Background:

  • Whole-genome duplications (WGDs) significantly contribute to plant biological complexity and adaptation.
  • KS age distributions derived from transcriptome data are widely used to detect WGDs in plants.
  • Comparisons between transcriptome-based and genome-based WGD detection are crucial for validating methods.

Purpose of the Study:

  • To evaluate the reliability of transcriptome-based KS distributions for detecting WGDs across different assembly pipelines.
  • To compare transcriptome-derived KS distributions with genome-based distributions.
  • To identify potential pitfalls and solutions for using transcriptome data in WGD detection.

Main Methods:

  • Implementation of three de novo transcriptome assembly pipelines using Trinity and SOAPdenovo-Trans.
  • Selection of six plant species with available genomic and transcriptomic data.
  • Analysis of KS distributions from assembled transcripts and comparison with genome-predicted genes.

Main Results:

  • Transcriptome-based KS distributions show differences in shape and scale compared to genome-based distributions.
  • Transcriptome data are generally reliable for WGD detection, with exceptions for recent WGD events.
  • Assembly pipeline choice impacts the accuracy of WGD detection from transcriptome data.

Conclusions:

  • Transcriptome data offer a viable approach for identifying plant WGDs, complementing genomic studies.
  • Understanding the discrepancies between transcriptome and genome-based analyses is key to accurate WGD identification.
  • Methodological considerations are important for overcoming limitations in transcriptome-based WGD detection.