Pipeline to analyze ChIP-seq data on the HPCC using SLURM.

1. Create a directory for analysis.
2. Prepare a samplesheet table in csv format. (ex: samplesheet.csv)
3. Make a Nextflow config file.
4. Download the reference genome and file in your directory.
5. Write a bash script to run the pipeline using SLURM.
6. Run the pipeline from your ChIP-seq directory.

Login to your HPCC account using OnDemand. Navigate to your home directory by clicking 'Files' in the navigation bar. Select 'Home Directory'.

Create a directory for your analysis by clicking 'New Directory'. Name your directory (ex: chipseq). Navigate to the newly created ChIP-seq directory.

Make sure to upload your data (FASTQ files) into this directory.

In your ChIP-seq directory, click 'New File'. Name the file 'samplesheet.csv'. Click the ⋮ symbol and select edit. Create the samplesheet table FOR YOUR DATA. Below is a template:

sample,fastq_1,fastq_2,antibody,control
WT_BCATENIN_IP_REP1,BLA203A1_S27_L006_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_BCATENIN_IP_REP2,BLA203A25_S16_L001_R1_001.fastq.gz,,BCATENIN,WT_INPUT
WT_INPUT_REP1,BLA203A6_S32_L006_R1_001.fastq.gz,,,
WT_INPUT_REP2,BLA203A30_S21_L001_R1_001.fastq.gz,,,

Note: the samplesheet above is an example to show the required format. You will still need to make a samplesheet FOR YOUR DATA.

Save the samplesheet.csv file and return to your ChIP-seq directory.

In your ChIP-seq directory, click New File. Name the file 'nextflow.config'. Click the ⋮ symbol and select edit. Create the Nextflow config file:

process {
    executor = 'slurm'
}

Save the nextflow.config file and return to your ChIP-seq directory.

The following organisms have updated reference genomes and gtf/gff3 files in common-data: human, mouse, rat, zebrafish, and Arabidopsis. The paths to those files can be found here.

In your directory, click Open in Terminal to enter a development node. Download the most recent genome primary assembly and gtf for the organism of interest from Ensembl. Follow the instructions here to download the reference genome for your organism of interest. This may take more than a few minutes. For example:

wget https://ftp.ensembl.org/pub/release-108/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz

wget https://ftp.ensembl.org/pub/release-108/gtf/homo_sapiens/Homo_sapiens.GRCh38.108.gtf.gz

Note: If you do not download the genome and gtf for the organism that the RNA-seq data is derived from, this pipeline will not work correctly.

In your ChIP-seq directory, click New File. Name the file 'run_chipseq.sb;. Write the bash script, using #SBATCH directives to set resources, for example:

#!/bin/bash

#SBATCH --job-name=$jobname_chipseq
#SBATCH --time=24:00:00
#SBATCH --mem=24GB
#SBATCH --cpus-per-task=8

cd $HOME/chipseq
module load Nextflow/23.10.0

nextflow pull nf-core/chipseq
nextflow run nf-core/chipseq -r 2.0.0 --input ./samplesheet.csv  -profile singularity --outdir ./chipseq_results --fasta ./Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz --gtf ./Homo_sapiens.GRCh38.108.gtf.gz -work-dir $SCRATCH/chipseq_work -c ./nextflow.config

Save the run_chipseq.sb file and return to your ChIP-seq directory.

In your ChIP-seq directory, click Open in Terminal to enter a development node. Run jobs on the SLURM cluster:

sbatch run_chipseq.sb

Check the status of your pipeline job:

squeue -u $username

Note: replace $username with your username