rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data
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Abstract
Background
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.
Results
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.
Conclusions
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.
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Now published in GigaScience doi: 10.1093/gigascience/giz100
Elena Bushmanova 1Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteDmitry Antipov 1Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteAlla Lapidus 1Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this site
A version of this preprint has been published in the …
Now published in GigaScience doi: 10.1093/gigascience/giz100
Elena Bushmanova 1Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteDmitry Antipov 1Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteAlla Lapidus 1Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, RussiaFind this author on Google ScholarFind this author on PubMedSearch for this author on this site
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giz100 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101881 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101882 Reviewer 3: http://dx.doi.org/10.5524/REVIEW.101883 Reviewer 4: http://dx.doi.org/10.5524/REVIEW.101884
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