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A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in

Yonas I Tekle1, Fiona C Wood2

  • 1Spelman College, 350 Spelman Lane Southwest, Atlanta, GA, 30314, USA. ytekle@spelman.edu.

BMC Evolutionary Biology
|November 18, 2018
PubMed
Summary
This summary is machine-generated.

Transcriptome sequencing effectively resolves cryptic species diversity in microbial eukaryotes. This method aids in identifying new species and understanding evolutionary histories, even with minimal sample requirements.

Keywords:
AmoebozoaBioinformaticsCryptic speciesDNA barcodePhylogenomicsRNA-seq

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Area of Science:

  • Microbial Eukaryote Evolution
  • Genomics and Bioinformatics
  • Cryptic Species Research

Background:

  • Transcriptome sequencing is a cost-effective tool for microbial eukaryote evolutionary studies.
  • Its application in resolving cryptic species diversity remains underexplored.
  • Discordances in morphology and genetics necessitate advanced analytical methods.

Purpose of the Study:

  • To evaluate the utility of transcriptome data in identifying cryptic species diversity.
  • To develop a bioinformatic pipeline for analyzing transcriptome data in amoeboid microbes.
  • To address discordances between genetic and morphological species identification.

Main Methods:

  • Development of a species comparison bioinformatic pipeline tailored for transcriptome data.
  • Analysis of single-copy genes across different culturing and sequencing methods.
  • Assessment of intra- and interspecific genetic divergences.

Main Results:

  • Transcriptome data consistently delineated taxa above the species level, including cryptic species.
  • Over 95% of analyzed genes showed low divergence (<2%) within species and between cryptic species.
  • The approach successfully resolved cryptic species, identified misidentifications, and discovered new species.

Conclusions:

  • Transcriptome data is a robust method for resolving cryptic species diversity in microbial eukaryotes.
  • The developed pipeline effectively handles transcriptome data challenges in amoeboid microbes.
  • Identified barcode markers offer potential for future evolutionary lineage studies.