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nextflow.config
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nextflow.config
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params {
// WORKFLOW PARAMETERS TO REVIEW AT EACH RUN
// ----------------------------------------
// Location of samplesheet specifying the file label for raw demultiplexed
// FASTQs and a sample ID to assign to each file
samplesheet = "$projectDir/resources/samplesheet.csv"
// whether the sequence reads are from an Illumina, PacBio, or Oxford Nanopore instrument
illumina = false
pacbio = false
ont = true
// Virus reference sequence FASTA
pathogen_ref = "$projectDir/resources/ncbi_human_virus_refseq_20221011.masked.fasta"
// Where to place results
results = "$launchDir/results"
// Adapter trimming settings
adapter_seq = "TTTTTTTTTTAATGTACTTCGTTCAGTTACGTATTGCT"
// Whether to run in "low disk mode". If this is set to true, large or intermediate files will be
// symlinked into the results directory rather than being copied. If this workflow is being run on
// an HPC cluster or other well-resourced compute infrastructure, we recommend leaving this as false.
low_disk_mode = false
// Read batch size. This defines the number of sequences to be batched when the workflow multithreads
// a biopython operation, such as when it converts FASTQs to FASTAs.
seq_batch_size = 5000
// WORKFLOW PARAMETERS THAT SHOULD NOT BE CHANGED
// ----------------------------------------
// resources/assets folder
resources = "$projectDir/resources"
// Link to download tarball of contiminant reference FASTAS used in
// Ramuta et al. 2023
contaminants_tar = "https://dholk.primate.wisc.edu/_webdav/dho/public/manuscripts/published/Metagenomic%20sequencing%20detects%20human%20respiratory%20and%20enteric%20viruses%20in%20air%20samples%20collected%20from%20congregate%20settings/%40files/workflow/resources/contam_ref.tar.gz?contentDisposition=attachment"
// Find the maximum number of cpus to allocate for local execution that
// is not on an HPC cluster
max_local_cpus = Math.floor( Runtime.getRuntime().availableProcessors() / 4 )
// Find the date
date = new java.util.Date().format('yyyyMMdd')
}
// WHETHER TO GENERATE A REPORT OF RUN STATISTICS
report {
enabled = true
file = "${params.results}/report.html"
}
// WHETHER TO GENERATE A VISUALIZATION OF THE WORKFLOW
dag {
enabled = false
overwrite = true
file = 'workflow-visualization.png'
}
// WHETHER TO REMOVE TEMPORARY FILES AFTER A SUCCESSFUL RUN
cleanup = false
// PROFILE OPTIONS
profiles {
standard {
docker.enabled = true
singularity.enabled = false
conda.enabled = false
process.container = "nrminor/air-sample-seqs:v1.8"
}
docker {
docker.enabled = true
singularity.enabled = false
conda.enabled = false
process.container = "nrminor/air-sample-seqs:v1.8"
}
singularity {
singularity.enabled = true
docker.enabled = false
conda.enabled = false
process.container = "nrminor/air-sample-seqs:v1.8"
}
hpc_cluster {
docker.enabled = false
singularity.enabled = true
conda.enabled = false
executor {
name = 'local'
cpus = 32
memory = 64.GB
}
process.container = "nrminor/air-sample-seqs:v1.8"
}
}
// MANIFEST
manifest {
homePage = 'https://github.com/dholab/pathogen-agnostic-sequence-analysis'
mainScript = 'main.nf'
defaultBranch = 'main'
description = 'NextFlow pipeline for quality controlling and aligning air cartridge sequence reads to a variety of possible pathogens'
author = 'Nicholas R. Minor, Mitchell D. Ramuta'
}