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

Abstract 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 an...

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Bibliographic Details
Main Authors: Yupei You, Yair D. J. Prawer, Ricardo De Paoli-Iseppi, Cameron P. J. Hunt, Clare L. Parish, Heejung Shim, Michael B. Clark
Format: Article
Language:English
Published: BMC 2023-04-01
Series:Genome Biology
Online Access:https://doi.org/10.1186/s13059-023-02907-y
Description
Summary:Abstract 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 .
ISSN:1474-760X