circFL-seq reveals full-length circular RNAs with rolling circular reverse transcription and nanopore sequencing

  1. Zelin Liu
  2. Changyu Tao
  3. Shiwei Li
  4. Minghao Du
  5. Yongtai Bai
  6. Xueyan Hu
  7. Yu Li
  8. Jian Chen
  9. Ence Yang  Is a corresponding author
  1. School of Basic Medical Sciences, Peking University Health Science Center, China
  2. Chinese Institute for Brain Research, Beijing, China

Abstract

Circular RNAs (circRNAs) act through multiple mechanisms via their sequence features to fine-tune gene expression networks. Due to overlapping sequences with linear cognates, identifying internal sequences of circRNAs remains a challenge, which hinders a comprehensive understanding of circRNA functions and mechanisms. Here, based on rolling circular reverse transcription (RCRT) and nanopore sequencing, we developed circFL-seq, a full-length circRNA sequencing method, to profile circRNA at the isoform level. With a customized computational pipeline to directly identify full-length sequences from rolling circular reads, we reconstructed 77,606 high-quality circRNAs from seven human cell lines and two human tissues. circFL-seq benefits from rolling circles and long-read sequencing, and the results showed more than tenfold enrichment of circRNA reads and advantages for both detection and quantification at the isoform level compared to those for short-read RNA sequencing. The concordance of the RT-qPCR and circFL-seq results for the identification of differential alternative splicing suggested wide application prospects for functional studies of internal variants in circRNAs. Moreover, the detection of fusion circRNAs at the omics scale may further expand the application of circFL-seq. Together, the accurate identification and quantification of full-length circRNAs make circFL-seq a potential tool for large-scale screening of functional circRNAs.

Data availability

The circFL-seq and RNA-seq data produced by this study have been deposited in SRA (PRJNA722575). The information of circRNAs detected by circFL-seq is available in the figshare repository (https://doi.org/10.6084/m9.figshare.14265650.v1). The computational software circfull can be accessed from https://github.com/yangence/circfull.

The following data sets were generated
    1. Liu ZL
    (2021) circRNA_circFL_table.xlsx
    Figureshare, doi.org/10.6084/m9.figshare.14265650.v1.
The following previously published data sets were used

Article and author information

Author details

  1. Zelin Liu

    Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3516-3999
  2. Changyu Tao

    Department of Human Anatomy, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Shiwei Li

    Department of Radiation Medicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Minghao Du

    Department of Microbiology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Yongtai Bai

    Department of Radiation Medicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Xueyan Hu

    Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Yu Li

    Chinese Institute for Brain Research, Chinese Institute for Brain Research, Beijing, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Jian Chen

    Chinese Institute for Brain Research, Chinese Institute for Brain Research, Beijing, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  9. Ence Yang

    Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
    For correspondence
    yangence@pku.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9526-2737

Funding

Beijing Municipal Science and Technology Commission of China (7212065,Z181100001518005)

  • Ence Yang

Chinese Institute for Brain Research, Beijing (2020-NKX-XM-01)

  • Ence Yang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Gene W Yeo, University of California, San Diego, United States

Version history

  1. Received: April 15, 2021
  2. Preprint posted: July 5, 2021 (view preprint)
  3. Accepted: October 13, 2021
  4. Accepted Manuscript published: October 14, 2021 (version 1)
  5. Version of Record published: October 26, 2021 (version 2)

Copyright

© 2021, Liu et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Zelin Liu
  2. Changyu Tao
  3. Shiwei Li
  4. Minghao Du
  5. Yongtai Bai
  6. Xueyan Hu
  7. Yu Li
  8. Jian Chen
  9. Ence Yang
(2021)
circFL-seq reveals full-length circular RNAs with rolling circular reverse transcription and nanopore sequencing
eLife 10:e69457.
https://doi.org/10.7554/eLife.69457

Share this article

https://doi.org/10.7554/eLife.69457

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