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Scywalker : scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing

Peter De Rijk, Tijs Watzeels, Fahri Küçükali, Jasper Van Dongen, Júlia Faura, Patrick Willems (UGent) , Lara De Deyn, Lena Duchateau, Carolin Seyfferth (UGent) , Thomas Eekhout (UGent) , et al.
(2024) BIOINFORMATICS. 40(9).
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Abstract
Motivation: Existing nanopore single-cell data analysis tools showed severe limitations in handling current data sizes. Results: We introduce scywalker, an innovative and scalable package developed to comprehensively analyze long-read sequencing data of full-length single-cell or single-nuclei cDNA. We developed novel scalable methods for cell barcode demultiplexing and single-cell isoform calling and quantification and incorporated these in an easily deployable package. Scywalker streamlines the entire analysis process, from sequenced fragments in FASTQ format to demultiplexed pseudobulk isoform counts, into a single command suitable for execution on either server or cluster. Scywalker includes data quality control, cell type identification, and an interactive report. Assessment of datasets from the human brain, Arabidopsis leaves, and previously benchmarked data from mixed cell lines demonstrate excellent correlation with short-read analyses at both the cell-barcoding and gene quantification levels. At the isoform level, we show that scywalker facilitates the direct identification of cell-type-specific expression of novel isoforms. Availability and implementation: Scywalker is available on github.com/derijkp/scywalker under the GNU General Public License (GPL) and at https://zenodo.org/records/13359438/files/scywalker-0.108.0-Linux-x86_64.tar.gz.
Keywords
EXPRESSION, REVEALS

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MLA
De Rijk, Peter, et al. “Scywalker : Scalable End-to-End Data Analysis Workflow for Long-Read Single-Cell Transcriptome Sequencing.” BIOINFORMATICS, edited by Anthony Mathelier, vol. 40, no. 9, 2024, doi:10.1093/bioinformatics/btae549.
APA
De Rijk, P., Watzeels, T., Küçükali, F., Van Dongen, J., Faura, J., Willems, P., … De Coster, W. (2024). Scywalker : scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing. BIOINFORMATICS, 40(9). https://doi.org/10.1093/bioinformatics/btae549
Chicago author-date
De Rijk, Peter, Tijs Watzeels, Fahri Küçükali, Jasper Van Dongen, Júlia Faura, Patrick Willems, Lara De Deyn, et al. 2024. “Scywalker : Scalable End-to-End Data Analysis Workflow for Long-Read Single-Cell Transcriptome Sequencing.” Edited by Anthony Mathelier. BIOINFORMATICS 40 (9). https://doi.org/10.1093/bioinformatics/btae549.
Chicago author-date (all authors)
De Rijk, Peter, Tijs Watzeels, Fahri Küçükali, Jasper Van Dongen, Júlia Faura, Patrick Willems, Lara De Deyn, Lena Duchateau, Carolin Seyfferth, Thomas Eekhout, Tim De Pooter, Geert Joris, Stephane Rombauts, Bert De Rybel, Rosa Rademakers, Frank Van Breusegem, Mojca Strazisar, Kristel Sleegers, and Wouter De Coster. 2024. “Scywalker : Scalable End-to-End Data Analysis Workflow for Long-Read Single-Cell Transcriptome Sequencing.” Ed by. Anthony Mathelier. BIOINFORMATICS 40 (9). doi:10.1093/bioinformatics/btae549.
Vancouver
1.
De Rijk P, Watzeels T, Küçükali F, Van Dongen J, Faura J, Willems P, et al. Scywalker : scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing. Mathelier A, editor. BIOINFORMATICS. 2024;40(9).
IEEE
[1]
P. De Rijk et al., “Scywalker : scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing,” BIOINFORMATICS, vol. 40, no. 9, 2024.
@article{01J96VAH691PTZQ0HM7K3DPBSH,
  abstract     = {{Motivation: Existing nanopore single-cell data analysis tools showed severe limitations in handling current data sizes.

Results: We introduce scywalker, an innovative and scalable package developed to comprehensively analyze long-read sequencing data of full-length single-cell or single-nuclei cDNA. We developed novel scalable methods for cell barcode demultiplexing and single-cell isoform calling and quantification and incorporated these in an easily deployable package. Scywalker streamlines the entire analysis process, from sequenced fragments in FASTQ format to demultiplexed pseudobulk isoform counts, into a single command suitable for execution on either server or cluster. Scywalker includes data quality control, cell type identification, and an interactive report. Assessment of datasets from the human brain, Arabidopsis leaves, and previously benchmarked data from mixed cell lines demonstrate excellent correlation with short-read analyses at both the cell-barcoding and gene quantification levels. At the isoform level, we show that scywalker facilitates the direct identification of cell-type-specific expression of novel isoforms.

Availability and implementation: Scywalker is available on github.com/derijkp/scywalker under the GNU General Public License (GPL) and at https://zenodo.org/records/13359438/files/scywalker-0.108.0-Linux-x86_64.tar.gz.}},
  articleno    = {{btae549}},
  author       = {{De Rijk, Peter and Watzeels, Tijs and Küçükali, Fahri and Van Dongen, Jasper and Faura, Júlia and Willems, Patrick and De Deyn, Lara and Duchateau, Lena and Seyfferth, Carolin and Eekhout, Thomas and De Pooter, Tim and Joris, Geert and Rombauts, Stephane and De Rybel, Bert and Rademakers, Rosa and Van Breusegem, Frank and Strazisar, Mojca and Sleegers, Kristel and De Coster, Wouter}},
  editor       = {{Mathelier, Anthony}},
  issn         = {{1367-4803}},
  journal      = {{BIOINFORMATICS}},
  keywords     = {{EXPRESSION,REVEALS}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{10}},
  title        = {{Scywalker : scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing}},
  url          = {{http://doi.org/10.1093/bioinformatics/btae549}},
  volume       = {{40}},
  year         = {{2024}},
}
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