SnakeHiC

A snakemake-based pipeline for processing HiC data.

This is a work-in-progress. Documentation is currently very brief and comments welcome!

Currently, the pipeline only supports High Performance Computing (HPC) platform.

Setting up pipeline

git clone [email protected]:FarmOmics/SnakeHiC.git

cd SnakeHiC

Setting up data

Raw paired-end fastq files should be put in a directory with name of "01_raw_fq". In the "sample.txt", your sample information should be provided, including two coloumns: LibraryID and SampleID. If your each sample only has one library, two coloumns will be same. Raw paired-end fastq files should be named as LibraryID_R1.fq.gz and LibraryID_R2.fq.gz

Preparing configuration file

The configuration file "config.yaml" should be changed according to your setting.

workdir: SnakeHiC ### working directory, usually current dorectory, absolute path is better
reffa: Gallus_gallus.bGalGal1.mat.broiler.GRCg7b.dna_sm.toplevel.chr.fa   ### reference genome fasta file
chrom_size: Gallus_gallus.bGalGal1.mat.broiler.GRCg7b.dna_sm.toplevel.chr.fa.genome   ### a file containing chromosome size, including two coloumns (chromosome, its size)
temp: tmp ## temporary directory
sample_file: sample.txt  ## /path/to/sample.txt, usually in current dorectory
jucier: juicer_1.22.01.jar  ## juicer should be separaley downloaded and provide full path here
resolutions: ["500", "1000", "5000", "10000", "25000", "50000", "100000", "250000", "500000", "1000000"] ### resolutions you want to include in generated ".hic" file

Configuring source file

The "cluster.yaml" contains using resource configuration, icnlduing computational nodes, memory and threads. These configurations should be changed according to your HPC platform.

Running the pipeline

The pipeline can be run with this command in your working directory:

snakemake -j 30 -cluster-config cluster.yaml -s Snakefile --configfile config.yaml --use-conda

The -j option specifies how many tasks you want to run in parallel. The -s argument is the "Snakefile" file. You could test the pipeline with a dry-run model by adding "-n" option. This will not run the pipeline, but check all codes used. In addition, you can also include "-p" to print commands of all steps.

Results

All generated results will be put in corresponding directories.