使用 BLAZE 从长读单细胞 RNA-seq 中识别细胞条码。
Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE.
机构信息
School of Mathematics and Statistics/Melbourne Integrative Genomics, The University of Melbourne, Parkville, VIC, 3010, Australia.
Centre for Stem Cell Systems, Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia.
出版信息
Genome Biol. 2023 Apr 6;24(1):66. doi: 10.1186/s13059-023-02907-y.
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 .
长读长单细胞 RNA 测序（scRNA-seq）可实现对单个细胞中 RNA 异构体的定量。然而，基于牛津纳米孔平台的长读长 scRNA-seq 在很大程度上依赖于匹配的短读数据来识别细胞条码。我们介绍了 BLAZE，它仅使用纳米孔长读长 scRNA-seq 数据即可准确高效地识别 10x 细胞条码。与匹配的短读相比，BLAZE 优于现有工具，为长读 scRNA-seq 中存在的细胞提供了更准确的表示。BLAZE 简化了长读 scRNA-seq，同时提高了结果的准确性，与接受细胞条码文件的下游工具兼容，并可在 https://github.com/shimlab/BLAZE 上获得。
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