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Updated: May 14, 2026
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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
Published on: May 9, 2017
Comparative analysis of de novo transcriptome assembly.
Kaitlin Clarke1, Yi Yang, Ronald Marsh
1Bioinformatics Core, Department of Pathology, University of North Dakota, Grand Forks, ND 58202, USA.
Science China. Life Sciences
|February 9, 2013
Summary
Transcriptome assembly using de Bruijn graph algorithms faces computational challenges. While Trinity showed good performance, no single assembler consistently produced high-quality, full-length transcripts from RNA-Seq data.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Next-generation sequencing (NGS) generates vast amounts of data, posing computational challenges.
- De Bruijn graph algorithms are effective for genome assembly but their utility in transcriptome assembly is uncertain.
Purpose of the Study:
- To evaluate the performance of five de novo transcriptome assemblers (ABySS, Mira, Trinity, Velvet, Oases).
- To compare assemblers based on de Bruijn graphs and overlap graphs using simulated and real RNA-Seq data.
Main Methods:
- Utilized simulated and real RNA-Seq datasets, including External RNA Control Consortium (ERCC) and human chromosome 22 data.
- Assessed five de novo assemblers: ABySS, Mira, Trinity, Velvet, and Oases.
- Calculated various statistical measures for contigs generated by each assembler.
Main Results:
- Trinity demonstrated relatively good performance on both ERCC and human data but may not consistently yield full-length transcripts.
- ABySS was the fastest assembler but produced low-quality assemblies.
- Mira showed a good contig mapping rate to human chromosome 22 but had unsatisfactory computational speed.
Conclusions:
- Transcriptome assembly from NGS data remains a significant computational challenge.
- Existing assemblers have limitations in terms of speed, quality, or ability to generate full-length transcripts.
- A novel assembler is needed for efficient and accurate transcriptome assembly.
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