Overview

The MSU HPCC, managed by ICER, offers a powerful environment for running bioinformatics analyses. This guide will walk you through running the nf-core/chipseq pipeline on the HPCC for reproducible and efficient ChIP-seq data analysis.

Key Benefits of nf-core/chipseq

nf-core/chipseq provides:

• Reproducible ChIP-seq Analysis: A robust, community-maintained pipeline.
• Portability: Runs smoothly on various computing infrastructures.
• Scalability: Handles both small and large ChIP-seq datasets.

Prerequisites

• Access to MSU HPCC with a valid ICER account.
• Basic knowledge of Singularity and Nextflow module usage.

Step-by-Step Tutorial

Note on Directory Variables

On the MSU HPCC:

• $HOME automatically routes to the user’s home directory (/mnt/home/username).
• $SCRATCH automatically routes to the user’s scratch directory, which is ideal for temporary files and large data processing.

Note on Working Directory

The working directory, which stores intermediate and temporary files, can be specified separately using the -w flag when running the pipeline. This helps keep your analysis outputs and temporary data organized.

1. Load Nextflow Module

Ensure Nextflow is available in your environment:

module load Nextflow

2. Create an Analysis Directory

Set up a dedicated directory for your analysis (referred to as the Analysis Directory):

mkdir $HOME/chipseq_project
cd $HOME/chipseq_project

• Modify $HOME/chipseq_project to better suit your project description, if needed.

3. Prepare Sample Sheet

Create a sample sheet (samplesheet.csv) with the following format:

sample,fastq_1,fastq_2,replicate,antibody
sample1,/path/to/sample1_R1.fastq.gz,/path/to/sample1_R2.fastq.gz,1,H3K27ac
sample2,/path/to/sample2_R1.fastq.gz,/path/to/sample2_R2.fastq.gz,1,H3K27me3

Ensure all paths to the FASTQ files are correct.

4. Configure ICER Environment

Create an icer.config file to run the pipeline with SLURM:

process {
    executor = 'slurm'
}

5. Run nf-core/chipseq

Example SLURM Job Submission Script

Below is a typical shell script for submitting an nf-core/chipseq job to SLURM:

#!/bin/bash

#SBATCH --job-name=chipseq_job
#SBATCH --time=48:00:00
#SBATCH --mem=32GB
#SBATCH --cpus-per-task=12

cd $HOME/chipseq_project
module load Nextflow/24.04.2

nextflow pull nf-core/chipseq
nextflow run nf-core/chipseq -r 2.1.0 --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

• Modify --outdir, --fasta, and --gtf to match your output and reference genome paths.

Note on Reference Genomes

Common reference genomes can be found in the research common-data space on the HPCC. Refer to the README file in that directory for more details. Additionally, you can find guidance on downloading reference genomes from Ensembl in this GitHub repository.

Execute the pipeline with the following command. This example includes a -w flag to specify a working directory in the user’s scratch space for intermediate files:

nextflow run nf-core/chipseq -profile singularity --input samplesheet.csv --genome GRCh38 -c icer.config -w $SCRATCH/chipseq_project

• The -profile singularity flag ensures that Singularity containers are used.
• Modify --genome to match your reference genome.
• Modify -w $SCRATCH/chipseq_project to better suit your project description, if needed.

6. Monitor and Manage the Run

• Use squeue or sacct to check the job status.
• Verify the output in the specified results directory.

Best Practices

• Check Logs: Regularly inspect log files generated by the pipeline for any warnings or errors.
• Resource Allocation: Adjust the icer.config to optimize resource usage based on dataset size.
• Storage Management: Ensure adequate storage space for intermediate and final results.

Getting Help

If you encounter any issues or have questions while running nf-core/chipseq on the HPCC, consider the following resources:

• nf-core Community: Visit the nf-core website for documentation, tutorials, and community support.
• ICER Support: Contact ICER consultants through the MSU ICER support page for assistance with HPCC-specific questions.
• Slack Channel: Join the nf-core Slack channel for real-time support and to engage with other users and developers.
• Nextflow Documentation: Refer to the Nextflow documentation for more details on workflow customization and troubleshooting.

Conclusion

Running nf-core/chipseq on the MSU HPCC is streamlined with Singularity and Nextflow modules. This setup supports reproducible, efficient, and large-scale ChIP-seq analyses. By following this guide, you can take full advantage of the HPCC’s computing power for your bioinformatics projects.