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
.