kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq

Delaney K Sullivan^{1

2}, Kyung Hoi Joseph Min³, Kristján Eldjárn Hjörleifsson⁴, Laura Luebbert¹, Guillaume Holley⁵, Lambda Moses¹, Johan Gustafsson⁶, Nicolas L Bray⁶, Harold Pimentel^{7

8

9}, A Sina Booeshaghi¹⁰, Páll Melsted^{11

12}, Lior Pachter^{13

14}

Affiliations

¹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
² UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³ Ginkgo Bioworks, Boston, MA, USA.
⁴ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
⁵ deCODE Genetics/Amgen Inc., Reykjavik, Iceland.
⁶ Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁷ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁹ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁰ Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA. sinab@berkeley.edu.
¹¹ deCODE Genetics/Amgen Inc., Reykjavik, Iceland. pmelsted@hi.is.
¹² School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland. pmelsted@hi.is.
¹³ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.
¹⁴ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.

PMID: 39390263
DOI: 10.1038/s41596-024-01057-0

Review

kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq

Delaney K Sullivan et al. Nat Protoc. 2025 Mar.

. 2025 Mar;20(3):587-607.

doi: 10.1038/s41596-024-01057-0. Epub 2024 Oct 10.

Authors

Affiliations

¹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
² UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³ Ginkgo Bioworks, Boston, MA, USA.
⁴ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
⁵ deCODE Genetics/Amgen Inc., Reykjavik, Iceland.
⁶ Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁷ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁹ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁰ Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA. sinab@berkeley.edu.
¹¹ deCODE Genetics/Amgen Inc., Reykjavik, Iceland. pmelsted@hi.is.
¹² School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland. pmelsted@hi.is.
¹³ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.
¹⁴ Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA. lpachter@caltech.edu.

PMID: 39390263
DOI: 10.1038/s41596-024-01057-0

Abstract

The term 'RNA-seq' refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, single cells or single nuclei. The kallisto, bustools and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data. Execution of this protocol requires basic familiarity with a command line environment. With this protocol, quantification of a moderately sized RNA-seq dataset can be completed within minutes.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Update of

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq.
Sullivan DK, Min KHJ, Hjörleifsson KE, Luebbert L, Holley G, Moses L, Gustafsson J, Bray NL, Pimentel H, Booeshaghi AS, Melsted P, Pachter L. Sullivan DK, et al. bioRxiv [Preprint]. 2024 Jan 23:2023.11.21.568164. doi: 10.1101/2023.11.21.568164. bioRxiv. 2024. Update in: Nat Protoc. 2025 Mar;20(3):587-607. doi: 10.1038/s41596-024-01057-0. PMID: 38045414 Free PMC article. Updated. Preprint.

References

1. Melsted, P. et al. Modular, efficient and constant-memory single-cell RNA-seq preprocessing. Nat. Biotechnol. 39, 813–818 (2021). - PubMed - DOI
1. Tian, L. et al. scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput. Biol. 14, e1006361 (2018). - PubMed - PMC - DOI
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1. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). - PubMed - DOI

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kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq

Affiliations

kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq

Authors

Affiliations

Abstract

Conflict of interest statement

Update of

References

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Associated data

Grants and funding

LinkOut - more resources

Full Text Sources