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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Current nanopore single-cell sequencing data analysis tools struggle with large data volumes.
• Scalability is a major challenge for analyzing extensive single-cell datasets.

Purpose of the Study:

• Introduce scywalker, a scalable package for comprehensive analysis of long-read single-cell or single-nuclei cDNA sequencing data.
• Develop and implement novel scalable methods for cell barcode demultiplexing and single-cell isoform calling and quantification.

Main Methods:

• Scywalker integrates data quality control, cell type identification, and interactive reporting.
• The package streamlines the analysis from FASTQ files to pseudobulk isoform counts with a single command.
• Novel scalable algorithms for demultiplexing and isoform quantification are core components.

Main Results:

• Scywalker demonstrates excellent correlation with short-read analyses for cell barcoding and gene quantification.
• Analysis of human brain, Arabidopsis leaves, and mixed cell line datasets validated scywalker's performance.
• The package facilitates the identification of cell-type-specific expression of novel isoforms.

Conclusions:

• Scywalker provides a scalable and comprehensive solution for long-read single-cell RNA sequencing data analysis.
• The tool enables efficient processing and accurate quantification, including novel isoform discovery.
• It addresses limitations of existing tools in handling large-scale single-cell genomic data.