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Updated: Oct 30, 2025
10:41
Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
Published on: May 9, 2017
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Pincho: A Modular Approach to High Quality De Novo Transcriptomics
Randy Ortiz1, Priyanka Gera2, Christopher Rivera1
1Department of Biology, St. John's University, Queens, NY 11439, USA.
Genes
|July 2, 2021
Summary
De novo transcriptome assembly for non-model organisms is challenging. Combining multiple transcriptomic assemblers significantly improves reconstruction quality, offering better insights from RNA-seq data.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- De novo transcriptomic reconstructions are crucial for non-model organisms lacking reference genomes.
- Current bioinformatic workflows for transcriptome assembly lack standardization and customization.
- Limited research exists on the synergistic effects of combining multiple assembly tools.
Purpose of the Study:
- To develop a customizable workflow for de novo transcriptomics.
- To evaluate the impact of combining multiple assemblers on transcriptome reconstruction quality.
- To provide guidance for improved transcriptomic assemblies from RNA-seq data.
Main Methods:
- Developed a modular management workflow connecting 25 bioinformatic tools.
- Implemented units for read cleaning, assembly, validation, annotation, and expression analysis.
- Assessed 129 single-, bi-, and tri-assembler combinations with varying k-mer sizes.
Main Results:
- Transcriptome assembly quality significantly increased with bi- and tri-assembler combinations.
- The customizable workflow demonstrated improved reconstruction capabilities.
- Identified optimal assembler combinations for enhanced de novo transcriptomics.
Conclusions:
- Combining multiple transcriptomic assemblers is a superior strategy for de novo reconstruction.
- The developed workflow enhances standardization and customization in transcriptomic analysis.
- This approach improves the analysis of RNA-seq data from non-model biological systems.
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