NASC-seq

The new transcriptome alkylation-dependent scRNA-seq (NASC-seq) was developed by [Hendriks2019]. It uses Smart-seq, which is a plate-based scRNA-seq method that provides great read coverage, compared to droplet-based methods [Picelli2013]. Smart-seq experiments generate single or pairs of FASTQs for each cell sequenced, which dynast processes simultaneously.

  • Sequencing technology: Smart-Seq2

  • Induced conversion: T>C

Alignment

Here, we assume the appropriate STAR index has already been built (see Building the STAR index with ref). Since we have multiple sets of FASTQs, we need to prepare a FASTQ manifest CSV, instead of providing these as an argument to dynast align. The manifest CSV contains three columns where the first column is a unique cell name/ID, the second column is the path to the first FASTQ, and the third is the path to the second FASTQ. For single-end reads, the third column can be a single - character. Here is an example with two cells:

cell_1,path/to/R1.fastq.gz,path/to/R2.fastq.gz
cell_2,path/to/R1.fastq.gz,-

Then, we use this manifest as the input to dynast align.

dynast align -i path/to/STAR/index -o path/to/align/output -x smartseq manifest.csv

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

Quantification

The alignment BAM is generated at path/to/align/output/Aligned.sortedByCoord.out.bam, which we provde as input to dynast count. We also need to provide the gene annotation GTF that was used to generate the STAR index to -g.

dynast count -g path/to/GTF.gtf --barcode-tag RG path/to/align/output/Aligned.sortedByCoord.out.bam -o path/to/count/output --conversion TC

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