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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Roast: a tool for reference-free optimization of supertranscriptome assemblies.

Madiha Shabbir¹, Aziz Mithani²

¹Department of Life Sciences, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore, 54792, Pakistan.

BMC Bioinformatics

|January 3, 2024

Summary

ROAST optimizes de novo supertranscriptome assemblies using RNA-seq error signatures. This reference-free tool improves assembly accuracy for non-model organisms without relying on BLAST searches.

Keywords:

Assembly errors Assembly improvement RNA-seq Reference-free optimization Supertranscript Supertranscriptome assembly

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Area of Science:

Bioinformatics
Genomics
Transcriptomics

Background:

De novo transcriptome and supertranscriptome assembly are crucial for organisms lacking reference genomes.
Challenges in supertranscriptome assembly include transcript abundance variation, alternative splicing, and sequencing errors.
Existing assembly tools often produce fragmented or inaccurate contigs, and improvement methods depend on related species data.

Purpose of the Study:

To develop a novel computational tool, ROAST, for optimizing de novo supertranscriptome assemblies.
To provide a reference-free method for improving the accuracy of supertranscriptome assemblies.
To address the limitations of existing assembly improvement tools that rely on BLAST searches.

Main Methods:

ROAST utilizes paired-end RNA-seq data from the Illumina platform.
The tool identifies and corrects assembly errors by analyzing RNA-seq alignment signatures such as soft-clips and unexpected coverage.
It specifically targets errors like fragmented contigs, false chimeras, and local mis-assemblies without using BLAST.

Main Results:

ROAST effectively identifies and fixes various supertranscriptome assembly errors.
Evaluation using simulated and real datasets demonstrates significant improvements in assembly quality.
The tool successfully refines de novo assemblies, enhancing contig accuracy and completeness.

Conclusions:

ROAST offers a robust, reference-free approach for optimizing supertranscriptome assemblies.
The tool is particularly valuable for refining assemblies of non-model organisms.
ROAST enhances the reliability and accuracy of transcriptomic data analysis in the absence of reference genomes.