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.

SUMMARY: Modified nucleotides play a crucial role in gene expression regulation. Here, we describe methplotlib, a tool developed for the visualization of modified nucleotides detected from Oxford Nanopore Technologies sequencing platforms, together with additional scripts for statistical analysis of allele-specific modification within-subjects and differential modification frequency across subjects. AVAILABILITY AND IMPLEMENTATION: The methplotlib command-line tool is written in Python3, is compatible with Linux, Mac OS and the MS Windows 10 Subsystem for Linux and released under the MIT license. The source code can be found at https://github.com/wdecoster/methplotlib and can be installed from PyPI and bioconda. Our repository includes test data, and the tool is continuously tested at travis-ci.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.