Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Affiliations

¹ Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland.
² Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA, USA.
³ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
⁴ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
⁵ Bioinformatics Resource Center, Beckman Institute, California Institute of Technology, Pasadena, CA, USA.
⁶ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Department of Genome Science, University of Washington, Seattle, WA, USA.
⁸ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.
⁹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.

^# Contributed equally.

PMID: 33795888
DOI: 10.1038/s41587-021-00870-2

Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Páll Melsted et al. Nat Biotechnol. 2021 Jul.

. 2021 Jul;39(7):813-818.

doi: 10.1038/s41587-021-00870-2. Epub 2021 Apr 1.

Authors

Affiliations

¹ Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland.
² Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA, USA.
³ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
⁴ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
⁵ Bioinformatics Resource Center, Beckman Institute, California Institute of Technology, Pasadena, CA, USA.
⁶ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Department of Genome Science, University of Washington, Seattle, WA, USA.
⁸ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.
⁹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.

^# Contributed equally.

PMID: 33795888
DOI: 10.1038/s41587-021-00870-2

Abstract

We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near optimal in speed with a constant memory requirement providing scalability for arbitrarily large datasets. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.

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References

1. Tian, L. et al. scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput. Biol. 14, e1006361 (2018). - DOI - PubMed - PMC
1. Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016). - DOI - PubMed - PMC
1. Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2011). - DOI - PubMed
1. Parekh, S., Ziegenhain, C., Vieth, B., Enard, W. & Hellmann, I. zUMIs - a fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience 7, giy059 (2018). - DOI - PubMed - PMC
1. Srivastava, A., Malik, L., Smith, T., Sudbery, I. & Patro, R. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol. 20, 65 (2019). - DOI - PubMed - PMC

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Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Affiliations

Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Authors

Affiliations

Abstract

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources