CRA HPC Configuration
nfcore pipeline sarek and rnaseq have been tested on the CRA HPC.
Before running the pipeline
- You will need an account to use the CRA HPC cluster in order to run the pipeline.
- Make sure that Singularity and Nextflow are installed.
- Downlode pipeline singularity images to a HPC system using nf-core tools
$ conda install nf-core
$ nf-core download
- You will need to specify a Singularity cache directory in your ~./bashrc. This will store your container images in this cache directory without repeatedly downloading them every time you run a pipeline. Since space on home directory is limited, using lustre file system is recommended.
export NXF_SINGULARITY_CACHEDIR = "/lustre/fs0/storage/yourCRAAccount/cache_dir"
- Download iGenome reference to be used as a local copy.
$ aws s3 --no-sign-request --region eu-west-1 sync s3://ngi-igenomes/igenomes/Homo_sapiens/GATK/GRCh38/ /lustre/fs0/storage/yourCRAAccount/references/Homo_sapiens/GATK/GRCh38/
Running the pipeline using the adcra config profile
- Run the pipeline within a screen or tmux session.
- Specify the config profile with
-profile adcra. - Using lustre file systems to store results (
--outdir) and intermediate files (-work-dir) is recommended.
nextflow run /path/to/nf-core/<pipeline-name> -profile adcra \
--genome GRCh38 \
--igenomes_base /path/to/genome_references/ \
... # the rest of pipeline flags
Config file
/*
* --------------------------------------------------------------
* nf-core pipelines config file for AD project using CRA HPC
* --------------------------------------------------------------
*/
params {
config_profile_name = 'adcra'
config_profile_description = 'CRA HPC profile provided by nf-core/configs'
config_profile_contact = 'Kalayanee Chairat (@kalayaneech)'
config_profile_url = 'https://bioinformatics.kmutt.ac.th/'
}
params {
max_cpus = 16
max_memory = 128.GB
max_time = 120.h
}
// Specify the job scheduler
executor {
name = 'slurm'
queueSize = 20
submitRateLimit = '6/1min'
}
singularity {
enabled = true
autoMounts = true
}
process {
resourceLimits = [
memory: 128.GB,
cpus: 16,
time: 120.h
]
scratch = true
queue = 'unlimit'
queueStatInterval = '10 min'
maxRetries = 3
errorStrategy = { task.attempt <= 3 ? 'retry' : 'finish' }
cache = 'lenient'
exitStatusReadTimeoutMillis = '2700000'
}
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