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A flexible cross-platform single-cell data processing pipeline

Author

Listed:
  • Kai Battenberg

    (Center for Integrative Medical Sciences, RIKEN
    Center for Sustainable Resource Science, RIKEN)

  • S. Thomas Kelly

    (Center for Integrative Medical Sciences, RIKEN)

  • Radu Abu Ras

    (Center for Integrative Medical Sciences, RIKEN
    University of Craiova)

  • Nicola A. Hetherington

    (Radboud University)

  • Makoto Hayashi

    (Center for Sustainable Resource Science, RIKEN)

  • Aki Minoda

    (Center for Integrative Medical Sciences, RIKEN
    Radboud University)

Abstract

Single-cell RNA-sequencing analysis to quantify the RNA molecules in individual cells has become popular, as it can obtain a large amount of information from each experiment. We introduce UniverSC ( https://github.com/minoda-lab/universc ), a universal single-cell RNA-seq data processing tool that supports any unique molecular identifier-based platform. Our command-line tool, docker image, and containerised graphical application enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms. We also provide a cross-platform application to run UniverSC via a graphical user interface, available for macOS, Windows, and Linux Ubuntu, negating one of the bottlenecks with single-cell RNA-seq analysis that is data processing for researchers who are not bioinformatically proficient.

Suggested Citation

  • Kai Battenberg & S. Thomas Kelly & Radu Abu Ras & Nicola A. Hetherington & Makoto Hayashi & Aki Minoda, 2022. "A flexible cross-platform single-cell data processing pipeline," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34681-z
    DOI: 10.1038/s41467-022-34681-z
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    References listed on IDEAS

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    1. Johannes W. Bagnoli & Christoph Ziegenhain & Aleksandar Janjic & Lucas E. Wange & Beate Vieth & Swati Parekh & Johanna Geuder & Ines Hellmann & Wolfgang Enard, 2018. "Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    2. Tsukasa Kouno & Jonathan Moody & Andrew Tae-Jun Kwon & Youtaro Shibayama & Sachi Kato & Yi Huang & Michael Böttcher & Efthymios Motakis & Mickaël Mendez & Jessica Severin & Joachim Luginbühl & Imad Ab, 2019. "C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    3. Tetsutaro Hayashi & Haruka Ozaki & Yohei Sasagawa & Mana Umeda & Hiroki Danno & Itoshi Nikaido, 2018. "Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
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