This is a simple Snakemake wrapper around the Arima Genomics Capture Hi-C (CHiC) workflow. This wrapper allows the Arima workflow to be executed in parallel and with a reproducible Conda environment. Software versions used by this wrapper versus the ones mentioned in the Arima workflow differ, since the Arima versions are quite old and require manual installation. Software versions were chosen to be compatible with the ones validated by Arima.

1. Install the Miniconda software distribution at a convenient location.
2. Add the Bioconda software channel, as described.
3. Install Snakemake, e.g. in a new conda environment: conda create -n snakemake snakemake
4. Check out the Arima workflow repository: git clone https://github.com/ArimaGenomics/CHiC.git arima-chic
5. In the arima-chic directory above, uncompress the chicagoTools.tar.gz file: tar xvf chicagoTools.tar.gz, resulting in a arima-chic/chicagoTools directory.
6. Check out the snake-chic repository: git clone https://github.com/insilicoconsulting/snake-chic snake-chic

The workflow expects paired-end FASTQ files in the directory fastq, relative to the snake-chic directory containing the workflow. The files must be named in the format samplename_[R1|R2].fastq.gz, e.g. sample1_R1.fastq.gz and sample1_R2.fastq.gz. It's probably easiest to create this naming format using symbolic links, e.g. ln -s /datadir/sample1_S1_L001_R1_001.fastq.gz fastq/sample1_R1.fastq.gz.

• Adapt the parameters in config/config.yaml to the requirements. Adapt the arima_dir parameter to the location of the arima-chic workflow directory above, and the chicago_dir parameter to the location of the chicagoTools directory above. The other parameters are explained in the Arima repository README file.
• Adapt the sample metadata file config/samples.tsv to use the sample names corresponding to the fastq files, and the suitable capture BED file for the sample.
• Activate the snakemake Conda environment: conda activate snakemake
• Execute the workflow: snakemake --use-conda -p --cores 16