An article in GenomeBiology presents BLAZE: a highly accurate single-cell barcode identification tool for nanopore long reads.
], the potential to study the complete array of RNA isoforms and quantify isoform expression at single-cell resolution is becoming possible. The use of “noisy” long reads, however, presents its own unique set of challenges, primarily the difficulty in identifying the cell barcodes needed to assign each transcript to its cell of origin.
We found BLAZE to be robust in its ability to accurately identify 10x cell barcodes from long reads. BLAZE can be applied to different types of single-cell samples and performs equally well on both higher accuracy Q20 data, as well as lower accuracy reads generated from ONT’s LSK110 and LSK109 protocols.
In order to accurately identify and quantify isoforms from scRNA-seq, it is important to sequence cells deeply []. BLAZE showed the greatest advantage over other methods in the higher-depth PromethION datasets and therefore performs well in the context most relevant to long-read scRNA-seq. At the same time, the performance of BLAZE is largely independent of read depth.
The accurate identification of single-cell barcodes is crucial to downstream gene and isoform quantification. Nearly all single-cell workflows cluster cells based on expression using dimensional reduction techniques such as t-SNE [
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