tutorial_slurm.md

Tutorial for SLURM clusters

1. Download cromwell on your $HOME directory.

   $ cd 
   $ wget https://github.com/broadinstitute/cromwell/releases/download/34/cromwell-34.jar
   $ chmod +rx cromwell-34.jar

2. Git clone this pipeline and move into its directory.

   $ cd
   $ git clone https://github.com/ENCODE-DCC/chip-seq-pipeline2
   $ cd chip-seq-pipeline2

3. Download a SUBSAMPLED (1/400) paired-end sample of ENCSR936XTK.

   $ wget https://storage.googleapis.com/encode-pipeline-test-samples/encode-chip-seq-pipeline/ENCSR936XTK/ENCSR936XTK_fastq_subsampled.tar
   $ tar xvf ENCSR936XTK_fastq_subsampled.tar

4. Download pre-built chr19/chrM-only genome database for hg38.

   $ wget https://storage.googleapis.com/encode-pipeline-genome-data/test_genome_database_hg38_chr19_chrM_chip.tar
   $ tar xvf test_genome_database_hg38_chr19_chrM_chip.tar

Our pipeline supports both Conda and Singularity.

For Conda users

5. Install Conda. Skip this if you already have equivalent Conda alternatives (Anaconda Python). Download and run the installer. Agree to the license term by typing yes. It will ask you about the installation location. On Stanford clusters (Sherlock and SCG4), we recommend to install it outside of your $HOME directory since its filesystem is slow and has very limited space. At the end of the installation, choose yes to add Miniconda's binary to $PATH in your BASH startup script.

   $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
   $ bash Miniconda3-latest-Linux-x86_64.sh

6. Install Conda dependencies.

   $ bash conda/uninstall_dependencies.sh  # to remove any existing pipeline env
   $ bash conda/install_dependencies.sh

7. Run a pipeline for the test sample. Try without partition and account settings first. If your cluster requires to specify any of them then add one to the command line.

   $ sbatch --partition [YOUR_PARTITION] --account [YOUR_ACCOUNT] examples/local/ENCSR936XTK_subsampled_chr19_only_slurm_conda.sh

For singularity users

6. CHECK YOUR SINGULARITY VERSION FIRST AND UPGRADE IT TO A VERSION >=2.5.2 OR PIPELINE WILL NOT WORK CORRECTLY.

   $ singularity --version

7. Pull a singularity container for the pipeline. This will pull pipeline's docker container first and build a singularity one on ~/.singularity.

   $ mkdir -p ~/.singularity && cd ~/.singularity && SINGULARITY_CACHEDIR=~/.singularity SINGULARITY_PULLFOLDER=~/.singularity singularity pull --name chip-seq-pipeline-v1.1.7.simg -F docker://quay.io/encode-dcc/chip-seq-pipeline:v1.1.7

8. Run a pipeline for the test sample. If your cluster requires to specify any of them then add one to the command line.

   $ sbatch --partition [YOUR_PARTITION] --account [YOUR_ACCOUNT] examples/local/ENCSR936XTK_subsampled_chr19_only_slurm_singularity.sh

For all users

8. It will take about an hour. You will be able to find all outputs on cromwell-executions/chip/[RANDOM_HASH_STRING]/. See output directory structure for details.

9. See full specification for input JSON file.

10. You can resume a failed pipeline from where it left off by using PIPELINE_METADATA(metadata.json) file. This file is created for each pipeline run. See here for details. Once you get a new input JSON file from the resumer, then edit your shell script (examples/local/ENCSR936XTK_subsampled_chr19_only_slurm_*.sh) to use it INPUT=resume.[FAILED_WORKFLOW_ID].json instead of INPUT=examples/....

For singularity users

11. IF YOU WANT TO RUN PIPELINES WITH YOUR OWN INPUT DATA/GENOME DATABASE, PLEASE ADD THEIR DIRECTORIES TO workflow_opts/slurm.json. For example, you have input FASTQs on /your/input/fastqs/ and genome database installed on /your/genome/database/ then add /your/ to singularity_bindpath. You can also define multiple directories there. It's comma-separated.

    {
        "default_runtime_attributes" : {
            "singularity_container" : "~/.singularity/chip-seq-pipeline-v1.1.7.simg",
            "singularity_bindpath" : "/your/,YOUR_OWN_DATA_DIR1,YOUR_OWN_DATA_DIR2,..."
        }
    }