scNT-seq

The single-cell metabolically labeled new RNA tagging sequencing (scNT-seq) was developed by [Qiu2020]. It uses Drop-seq, which is a droplet-based scRNA-seq method [Macosko2015].

  • Sequencing technology: Drop-seq

  • Induced conversion: T>C

Alignment

Here, we assume the appropriate STAR index has already been built (see Building the STAR index with ref). A single sample will consist of a pair of FASTQs, one containing the cell barcode and UMI sequences and the other containing the biological cDNA sequences. Let’s say these two FASTQs are barcode_umi.fastq.gz and cdna.fastq.gz.

dynast align -i path/to/STAR/index -o path/to/align/output -x dropseq cdna.fastq.gz barcode_umi.fastq.gz

This will run STAR alignment and output files to path/to/align/output.

Consensus

Optionally, we can call consensus sequences for each UMI using dynast consensus. This command requires the alignment BAM and the gene annotation GTF that was used to generate the STAR index.

dynast consensus -g path/to/GTF.gtf --barcode-tag CB --umi-tag UB path/to/align/output/Aligned.sortedByCoord.out.bam -o path/to/consensus/output

This will create a new BAM file named path/to/consensus/output/consensus.bam, which you can then use in the next step in place of the original alignment BAM.

Quantification

Finally, to quantify the number of labeled/unlabeled RNA, we run dynast count with the appropriate alignment BAM and the gene annotation GTF that was used to generate the STAR index to -g.

dynast count -g path/to/GTF.gtf --barcode-tag CB --umi-tag UB path/to/alignment.bam -o path/to/count/output --conversion TC

where path/to/alignment.bam should be path/to/align/output/Aligned.sortedByCoord.out.bam if you did not run dynast consensus, or path/to/consensus/output/consensus.bam if you did.

This will quantify all RNA species and write the count matrices to path/to/count/output/adata.h5ad.