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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
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Evaluating de Bruijn graph assemblers on 454 transcriptomic data.

Xianwen Ren1, Tao Liu, Jie Dong

  • 1MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Plos One
|December 14, 2012
PubMed
Summary
This summary is machine-generated.

This study compares de novo assembly tools for analyzing transcriptomic data from non-model organisms. Trinity, designed for Illumina, performed best on 454 data, offering guidance for assembler selection.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) revolutionized transcriptomic studies, especially for non-model organisms lacking reference genomes.
  • De novo assembly methods are crucial for analyzing NGS transcriptomic data without a reference genome.
  • De Bruijn graph assemblers (e.g., ABySS, Velvet, Trinity) are efficient for large NGS datasets but often optimized for Illumina/Solexa platforms.

Purpose of the Study:

  • To evaluate and compare the performance of de Bruijn graph-based assemblers on 454 transcriptomic data.
  • To assess the suitability of assemblers developed for Illumina/Solexa platforms for 454 sequencing data.
  • To provide guidance for selecting appropriate assemblers for 454 transcriptomic data analysis.

Main Methods:

  • Comparative performance evaluation of de Bruijn graph assemblers (ABySS, Velvet, Trinity).
  • Testing on both simulated and real 454 pyrosequencing transcriptomic datasets.
  • Benchmarking against the standard 454 assembler, Newbler (overlap-layout-consensus based).

Main Results:

  • Trinity demonstrated superior performance among the evaluated de Bruijn graph assemblers on 454 data.
  • Trinity's performance was comparable to, and in some cases exceeded, the standard 454 assembler Newbler.
  • The study highlights the effectiveness of an Illumina-specialized assembler for 454 data.

Conclusions:

  • Trinity is a highly effective assembler for analyzing 454 transcriptomic data, even without a reference genome.
  • Researchers can confidently use Trinity for de novo transcriptomic assembly of 454 data.
  • This evaluation provides valuable insights for optimizing transcriptomic data analysis in non-model organisms.