ArticleBioinformatics (Oxford, England)2024
Scywalker: scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq nanopore long reads data.NAR genomics and bioinformatics · 2026Article
- Current trends and challenges in deciphering single molecule resolution maps of single cell transcriptomes.Briefings in bioinformatics · 2026Review
- Review
- Bioinformatics frameworks for single-cell long-read sequencing: unlocking isoform-level resolution.Briefings in bioinformatics · 2025Review
- scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing.Bioinformatics (Oxford, England) · 2025Article
- Comparison of single-cell long-read and short-read transcriptome sequencing via cDNA molecule matching: quality evaluation of the MAS-ISO-seq approach.NAR genomics and bioinformatics · 2025Article
- Advances in single-cell long-read sequencing technologies.NAR genomics and bioinformatics · 2024Article
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Authors and funding
19 authors.
Funding
Abstract
motivationExisting nanopore single-cell data analysis tools showed severe limitations in handling current data sizes.
resultsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.