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NanoTrans: an integrated computational framework for comprehensive transcriptome analyses with Nanopore direct-RNA sequencing

View ORCID ProfileFan Wang, View ORCID ProfileXinxin Zhang, Li Zhang, View ORCID ProfileJing Li, View ORCID ProfileJia-Xing Yue
doi: https://doi.org/10.1101/2022.11.29.518309
Fan Wang
1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yatsen University Cancer Center, Guangzhou, China
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Xinxin Zhang
1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yatsen University Cancer Center, Guangzhou, China
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Li Zhang
1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yatsen University Cancer Center, Guangzhou, China
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  • For correspondence: yuejiaxing@gmail.com
Jing Li
1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yatsen University Cancer Center, Guangzhou, China
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  • For correspondence: yuejiaxing@gmail.com
Jia-Xing Yue
1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yatsen University Cancer Center, Guangzhou, China
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  • For correspondence: yuejiaxing@gmail.com
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Abstract

Summary Nanopore direct-RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, rich tabular and graphic report outputs, as well as automatic installation and configuration support. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrated its utility, effectiveness, and efficacy across a wide range of DRS-based application settings.

Availability and implementation NanoTrans is written in bash, Perl, and R. It is free for use under the MIT license, available at https://github.com/yjx1217/NanoTrans. The key raw data are uploaded to the Research Deposit public platform (www.researchdata.org.cn), with the approval RDD number of RDDXXXXXXXXXXXX.

Contact zhangli{at}sysucc.org.cn; lijing3{at}sysucc.org.cn; yuejiaxing{at}gmail.com

Supplementary information Supplementary data are available at Bioinformatics online.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Abstract typo fixed. Funding info updated. Supplemental files updated.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 01, 2022.
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NanoTrans: an integrated computational framework for comprehensive transcriptome analyses with Nanopore direct-RNA sequencing
Fan Wang, Xinxin Zhang, Li Zhang, Jing Li, Jia-Xing Yue
bioRxiv 2022.11.29.518309; doi: https://doi.org/10.1101/2022.11.29.518309
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NanoTrans: an integrated computational framework for comprehensive transcriptome analyses with Nanopore direct-RNA sequencing
Fan Wang, Xinxin Zhang, Li Zhang, Jing Li, Jia-Xing Yue
bioRxiv 2022.11.29.518309; doi: https://doi.org/10.1101/2022.11.29.518309

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