nfcore pipelines for CUT-RUN, CUT-Tag, and TIPseq

Summary

Perform cut-run analysis using the nf-core pipeline.

ref: https://nf-co.re/cutandrun/3.2.2

Input

You will need to create a input.csv file with information about the samples in your experiment before running the pipeline. Keep the header, see the example below.

group,replicate,fastq_1,fastq_2,control
h3k27me3,1,READ1_FASTQ.gz,READ2_FASTQ.gz,igg_ctrl
h3k27me3,2,READ1_FASTQ.gz,READ2_FASTQ.gz,igg_ctrl
h3k4me3,1,READ1_FASTQ.gz,READ2_FASTQ.gz,igg_ctrl
h3k4me3,2,READ1_FASTQ.gz,READ2_FASTQ.gz,igg_ctrl
igg_ctrl,1,READ1_FASTQ.gz,READ2_FASTQ.gz,
igg_ctrl,2,READ1_FASTQ.gz,READ2_FASTQ.gz,

Column	Description
group	Group identifier for sample. This will be identical for replicate samples from the same experimental group.
replicate	Integer representing replicate number.
fastq_1	Full path to FastQ file for read 1. File has to be zipped and have the extension “.fastq.gz” or “.fq.gz”.
fastq_2	Full path to FastQ file for read 2. File has to be zipped and have the extension “.fastq.gz” or “.fq.gz”.
control	String representing the control group in the group column to which this replicate is assigned to.

Usage

hpcf_interactive # login to compute node

module load python/2.7.13

run_lsf.py -f input.csv -p cut_run_nfcore -g hg19 --addon_parameters "  --max_cpus 5 --max_memory 50GB --peakcaller macs2 --normalisation_mode Spikein -resume"

Output

See results in the $jobID folder.

code @ github.