Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Journal article

Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes. We introduce BLAZE, which accurately and efficiently identifies 10x cell barcodes using only nanopore long-read scRNA-seq data. BLAZE outperforms the existing tools and provides an accurate representation of the cells present in long-read scRNA-seq when compared to matched short reads. BLAZE simplifies long-read scRNA-seq while improving the results, is compatible with downstream tools accepting a cell barcode file, and is available at https://github.com/shimlab/BLAZE

Authors: You, Y; Prawer, YDJ; De Paoli-Iseppi, R; Hunt, CPJ; Parish, CL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Genome Biology
• Volume: 24
• Issue: 1
• Pages: 66-66
• Article number: 66
• Publication date: 2023-04-06
• DOI: 10.1186/s13059-023-02907-y
• EISSN: 1474-760X
• ISSN: 1474-7596
• Language: English
• Keywords: Identification (biology); Single-cell analysis; Operating system; RNA-Seq; Barcode; Cell; RNA; Gene expression; Gene; Transcriptome; DNA sequencing; Computational biology; Genetics; Computer science; Biology; Nanopore sequencing