Python:
- argparse
- gzip
- re
- os
- multiprocessing
- functools
- collections
- itertools
- operator
- Biopython
- distance
- glob
- pandas
Other software:
- snakemake
- alfred
- umi_tools
This workflow expects standard illumina demultiplexed fastq files as input (can be either multiple fastq files for each lane or merged).
To process the a single multiplex library edit the config.yaml file in the preprocessing folder with the appropriate values.
A reference genome, the link to the alfred binary, a link the the processBarcode.py and a list of barcodes are also required. The list of barcodes should be a standard text file with each row containing one barcode.
The barcode pattern depends on the sequencing method used. For standard CUTseq (96 linkers) this is typically UUUUUUUUBBBBBBBBDDDD and for scCUTseq (384 linkers) this is typically UUUUUUUUBBBBBBBBBBBDDDD.
After successfully editing the config.yaml you can run the entire snakemake file from the command line. If you are in the preprocessing directory this can be done in the following way: snakemake -j {number of threads to use}
Python:
- snakemake
- glob
- os
R:
- data.table
- argparser
- DNAcopy
- ParDNAcopy
- pbapply
- gtools
- RColorBrewer
- scales
- ggdendro
- cowplot
- ggplot2
- tidyverse
After finishing the preprocessing workflow you should have aligned bamfiles. One for each of the barcodes provided in the barcode file.
Just like for the preprocessing workflow, here you also just have to change the config.yaml and then execute the snakemake. There are, just like in the preprocessing workflow, some file requirements. The main files you need is a blacklist (provided in this repository under /files/hg19/hg19-blacklist.v2_adjusted.bed and a couple of files regarding the variable widths of the bins. Opposed to fixed-width binning, the variable-width bins take into account the mappability of the genome (with the length of the reads that you use). Within the directory /files/hg19/ there are included some of the main files. Specifically, there are files for different binsizes, 1mb, 500kb, 250kb, 175kb, 100kb and 50kb. Also, for each of these binsizes there are files with variable widths generated for the following readlengths: 48, 76 and 150.
If you want to generate your own files I suggest you check out the following script: https://github.com/EngeLab/DNTRseq/blob/master/scripts/build_genome_files_bybin.sh
If you have all the required file and specified all the paths and parameters correctly, you can run snakemake like before snakemake -j {number of threads to use}. Note: you can specify multiple binsizes, i.e. binsize: [100000, 50000], and the analysis will run for all the specified binsizes.