rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data

• Published in: Gigascience Vol. 8; no. 9
• Main Authors: Bushmanova, Elena, Antipov, Dmitry, Lapidus, Alla, Prjibelski, Andrey D
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.09.2019
• Subjects: Algorithms; Alternative splicing; Animals; Arabidopsis - genetics; Assembly; Caenorhabditis elegans - genetics; Computer applications; Datasets; Gene expression; Gene sequencing; Genes; Genomes; Humans; Isoforms; Mice; Quality assessment; Quality control; Ribonucleic acid; RNA; RNA-Seq; Splicing; Technical Note; Transcriptome; Transcriptomes; Transcriptomics; Zea mays - genetics; RNA-Seq; de novo assembly; transcriptome assembly
• DOI: 10.1093/gigascience/giz100
• ISSN: 2047-217X

The possibility of generating large RNA-sequencing datasets has led to development of various reference-based and de novo transcriptome assemblers with their own strengths and limitations. While reference-based tools are widely used in various transcriptomic studies, their application is limited to the organisms with finished and well-annotated genomes. De novo transcriptome reconstruction from short reads remains an open challenging problem, which is complicated by the varying expression levels across different genes, alternative splicing, and paralogous genes. Herein we describe the novel transcriptome assembler rnaSPAdes, which has been developed on top of the SPAdes genome assembler and explores computational parallels between assembly of transcriptomes and single-cell genomes. We also present quality assessment reports for rnaSPAdes assemblies, compare it with modern transcriptome assembly tools using several evaluation approaches on various RNA-sequencing datasets, and briefly highlight strong and weak points of different assemblers. Based on the performed comparison between different assembly methods, we infer that it is not possible to detect the absolute leader according to all quality metrics and all used datasets. However, rnaSPAdes typically outperforms other assemblers by such important property as the number of assembled genes and isoforms, and at the same time has higher accuracy statistics on average comparing to the closest competitors.