Research output: Contribution to journal › Article › peer-review
RnaSPAdes: A de novo transcriptome assembler and its application to RNA-Seq data. / Bushmanova, Elena; Antipov, Dmitry; Lapidus, Alla; Prjibelski, Andrey D.
In: GigaScience, Vol. 8, No. 9, giz100, 18.09.2019.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - RnaSPAdes: A de novo transcriptome assembler and its application to RNA-Seq data
AU - Bushmanova, Elena
AU - Antipov, Dmitry
AU - Lapidus, Alla
AU - Prjibelski, Andrey D.
PY - 2019/9/18
Y1 - 2019/9/18
N2 - 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.
AB - 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.
KW - de novo assembly
KW - RNA-Seq
KW - transcriptome assembly
KW - QUALITY ASSESSMENT
UR - http://www.scopus.com/inward/record.url?scp=85071896681&partnerID=8YFLogxK
UR - https://www.biorxiv.org/content/early/2018/09/18/420208
UR - http://www.mendeley.com/research/rnaspades-novo-transcriptome-assembler-application-rnaseq-data
U2 - 10.1093/gigascience/giz100
DO - 10.1093/gigascience/giz100
M3 - Article
C2 - 31494669
VL - 8
JO - GigaScience
JF - GigaScience
SN - 2047-217X
IS - 9
M1 - giz100
ER -
ID: 36059955