LR-splitpipe

LR-splitpipe is a pipeline designed for demultiplexing, debarcoding, and preparing LR-Split-seq data.

Demultiplexing reads

To demultiplex reads for their Split-seq barcodes, use demultiplex.py.

Usage: python LR-splitpipe/demultiplex.py [options]

optional arguments:
  -h, --help      show this help message and exit
  -f FASTQ        FASTQ file output from Lima with LR-Split-seq reads.
  -steps STEPS    Comma separated list of steps to perform. Default is all.
                  Options include: score_linkers, align_linkers, correct_bcs,
                  trim, i_filt, write_fastq
  -o OPREFIX      Output file path/prefix
  -t THREADS      Number of threads to run on (multithreading is recommended)
  -i_file I_FILE  Barcodes from Illumina to filter PacBio barcodes on.
                  Default: None

Demultiplexing steps:

Each step requires the prior step have been run and output from that step has been generated and is still there!

1. Score linkers

First, LR-splitpipe uses alignment to find reads that have linkers in them, which are the static regions that connect each of the combinatorial barcodes. Reads that have fewer than 4 errors in each linker will then be used to find barcodes.

score_linkers option for the -steps argument.

2. Align linkers

Using the reads that had valid linkers found in them, determine the location in the read of each linker.

align_linkers option for the -steps argument.

Note: This step typically takes the longest! Parallelization will help with this!

3. Find and correct barcodes

With the locations of the linkers from step 2, extract the barcodes from each read. Correct barcodes to those that are within edit distance of 3 of the list of possible Split-seq barcodes. This step uses code and assets (barcodes and barcodes within edit distance 3) from the original Parse Biosciences Split-seq demultiplexing code, which is designed for short reads.

correct_bcs option for the -steps argument.

4. Trim barcodes

After recording the barcode and UMI for each read, trim the construct off from the sequence as this part will mess up mapping.

trim_bcs option for the -steps argument.

5. Optional filtering

If a corresponding short-read Split-seq experiment has been performed, barcodes that pass the UMI/cell cutoff in that experiment can be used to limit the corresponding barcodes in the long-read experiment. The barcodes should be given in a text file where each line corresponds to one 24nt long barcode.

i_filt option for the -steps argument. i_file to specify the short-read barcodes file.

6. Write to fastq

Take the barcode/UMI for each read and append it to the read name of each read. Output the trimmed reads labeled by their barcodes to a fastq file.

write_fastq option for the -steps argument.

Adding cell barcode as BAM tag

After running the demultiplexer, reads should be mapped and converted to a SAM file. In this file format, the barcode, which is in the read header in fastq format, can be moved to a cell barcode (CB:Z:NNNNN...) tag as part of the SAM file. This is useful to be able to run TALON on the single-cell data down the line. To do this, run add_bam_tag.py.

Note: It is recommended to use the --merge_primers option. This option was only included to compare the random hexamer and oligo-dT primed reads for the pilot experiment.

Usage: python LR-splitpipe/add_bam_tag.py [options]

  -h, --help       show this help message and exit
  -s SAMFILE       SAM file output from Minimap2/TranscriptClean with splitseq
                   barcode+UMI information in the read name
  --merge_primers  Merge reads that come from the same cell from different
                   priming strategies
  -o OPREFIX       Output file path/prefix