I regularly work on combinatorial barcoding data.
If you’re open to a pseudoalignment approach, kallisto (via kb-python) can process Parse data really easily. Say you’re processing human single-cell samples with v2 of Parse’s kit, and your read files are named R1.fastq.gz and R2.fastq.gz. You can do:
pip install kb_python
wget https://raw.githubusercontent.com/Yenaled/barcodes/refs/heads/main/splitseqv2_barcodes.txt
wget https://raw.githubusercontent.com/Yenaled/barcodes/refs/heads/main/splitseqv2_replace.txt
kb ref -d human -i index.idx -g t2g.txt
kb count -x SPLIT-SEQ -w splitseqv2_barcodes.txt -r splitseqv2_replace.txt -i index.idx -g t2g.txt -o output_dir R1.fastq.gz R2.fastq.gz
The matrices will be outputted into output_dir/counts_unfiltered_modified/ in sparse MatrixMarket (.mtx) format that you can then load into python via scipy.io.mmread().
(Note: splitseqv2_barcodes.txt and splitseqv2_replace.txt contain the Parse kit's barcodes and the correspondence between oligo-dT and randO primers, respectively). If you need any adjustments to the command (like if you want to use a different/custom species, quantify nucleus RNA, using another version of Parse's kit, or have any questions about the command or output files or run into problems running the code, let me know).
If you would like to use a STAR aligner-based approach, I can also tell you how to go about doing it (unfortunately my code for that is in a bit-disorganized snakemake workflow that I can't share).
