BioC2024

Long-read single-cell RNA-sequencing (scRNA-seq) enables accurate determination of novel isoforms in order to assess transcript heterogeneity in health and diseases. In addition, Single-nucleotide variants (SNPs) and small insertions and deletions (INDELs) can be quantified at the single-cell level to investigate cancer heterogeneity. The analysis of long-read scRNA-seq data is currently limited by the scarcity of relevant software. To fill this gap, we have developed the open-source FLAMES software, which covers all major aspects of long-read scRNA-seq data analysis from preprocessing through to differential analyses. FLAMES is fully featured and flexible R/Bioconductor package that is integrated with standard Bioconductor containers and can support data generated using different protocols, including the emerging spatial transcriptomics protocols, and across multiple samples. In addition, the software collects and reports key quality metrics, supports the use of external packages for barcode demultiplexing and isoform discovery (e.g. flexiplex, bambu) and provides additional data visualisation functions to generate publication quality figures. Our enhanced FLAMES pipeline thus provides a complete beginning-to-end workflow for isoform-level analysis of data from long-read scRNA-seq experiments and is freely available from Bioconductor (FLAMES).