Preprocessing and cna calling pipelines used for the sbg2004 manuscript
Tools:
- alfred (v0.2.1)
Python:
- pysam (v0.16.0.1)
- pandas (v1.2.4)
- argparse (v1.4.0)
Since after basespace demultiplexing the fastq files still contain multiple CUTseq samples we further demultiplex using a python script. This script needs to be build first and this can be done in the following way: Build the python/cython library using $ python setup.py build_ext --inplace, dependencies for demultiplexing are: pandas, argparse and pysam.
After building the cython library you can edit the config.yaml that is present alongside the snakefile, making sure that all the paths to the directories, executables and the reference genome are correct.
First of, make sure that gatk 4.1.4.0 is installed and present in your $PATH to be able to run the snakefile. If you have multiple/other versions of gatk installed you can change the gatk to your prefered gatk version in the snakefile. Following this, change the paths in config.yaml to the correct directories and human reference (hg19) and its dictionary file and specify the required bin size (100kb in our case). After succesfully running the copynumber pipeline you will end up with the segmented copy number files ready for plotting and further analyses.
In the data directory you can find all processed data files (fastq files are not available due to patient confidentiality) to perform and visualize all analysis used in the manuscript. There are two subdirectories, one for CNA analyses and one for TIL analyses.
For further analysis of copynumber calling output and TIL analysis scripts are available in the Analysis directory. These scripts are used to plot copy number profiles, SCNA frequencies and perform the analyses of TILs. The output of this can be used to generate all the plots in the manuscript.
If you have any questions or if you are missing files feel free to email me at: luuk.harbers@scilifelab.se