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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
Published on: May 9, 2017
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Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression
Sufang Wang1, Michael Gribskov1,2
1Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Bioinformatics (Oxford, England)
|February 8, 2017
Summary
De novo transcriptome assembly quality impacts downstream analysis. Trans-ABySS and SOAPdenovo-Trans showed best performance in different metrics, with de novo assembly proving beneficial even with a reference genome.
Area of Science:
- Bioinformatics
- Genomics
- Transcriptomics
Background:
- RNA-Seq is increasingly used for non-model organisms, necessitating de novo transcriptome assembly due to the frequent absence of reference genomes.
- Systematic evaluations of de novo transcriptome assembly quality and its impact on downstream analyses are limited.
Purpose of the Study:
- To systematically evaluate the quality of de novo transcriptome assemblies generated by eight different software programs across various k-mer sizes.
- To assess how de novo assembly quality influences downstream differential gene expression analysis.
- To identify factors contributing to discrepancies in differential gene expression results between reference-guided and de novo assembly approaches.
Main Methods:
- Two RNA-Seq datasets from Arabidopsis thaliana were used.
- Transcriptome assemblies were generated using eight programs (BinPacker, Bridger, IDBA-tran, Oases-Velvet, SOAPdenovo-Trans, SSP, Trans-ABySS, Trinity) with k-mer sizes ranging from 25 to 71.
- Assembly quality was assessed based on reference genome base and gene coverage, transcriptome assembly base coverage, chimera count, and recovered full-length transcripts.
- Differential gene expression analysis was performed and compared between reference-guided and de novo assemblies.
Main Results:
- SOAPdenovo-Trans excelled in base coverage, while Trans-ABySS led in gene coverage and full-length transcript recovery.
- BinPacker and Oases-Velvet produced the most chimeric sequences; IDBA-tran, SOAPdenovo-Trans, Trans-ABySS, and Trinity showed fewer chimeras.
- Approximately 70% of significantly differentially expressed genes (DEGs) were consistent between reference genome and de novo assemblies.
- Incomplete annotation, exon-level differences, transcript fragmentation, and incorrect gene annotation were identified as key reasons for DEG discrepancies.
Conclusions:
- De novo transcriptome assembly quality varies significantly among different software and k-mer settings.
- Trans-ABySS and SOAPdenovo-Trans demonstrated superior performance in specific quality metrics.
- De novo assembly is a valuable approach, even when a reference genome is available, for comprehensive transcriptomic analysis.
- Understanding the limitations and sources of error in de novo assembly is crucial for accurate downstream biological interpretation.
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