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Update to CNV workflow #1
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Wicked thanks for sharing your code Miles, I've implemented it and will test/refine it when I get to that section of the code :) |
Reopening this due to a big update in the wf-cnv workflow. You no longer are required to use a samplesheet or just fastqs. Bams are now a valid entry point which massively speeds up the CNV calling process. Some example code: #!/bin/bash -l
# check and grab latest wf (if wanted)
nextflow pull epi2me-labs/wf-cnv
# define variables
WKDIR='/data/basecalled/AGRF'
SAMPLE='Control'
REFERENCE='/public-data/references/GRCh38/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna'
NFCONFIG='/public-data/configs/nextflow_local_overide.config'
THREADS='48'
# EPI2ME Labs CNV workflow
nextflow -c "${NFCONFIG}" run epi2me-labs/wf-cnv \
-resume \
--threads "$THREADS" \
-profile standard,local \
--bam "${WKDIR}"/"${SAMPLE}"/bam/"${SAMPLE}"_sorted_merged.hp.bam \
--sample "${SAMPLE}" \
--fasta "${REFERENCE}" \
--genome hg38 --bin_size 500 \
--map_threads 24
# Notes:
# EPI2ME Labs workflow for ONT CNV analysis: https://github.com/epi2me-labs/wf-cnv
# CNV pipeline will run on the basecalled fastq files or aligned bams,
# and then perform CNV analysis using QDNAseq. |
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I had some time recently for another job to better implement the CNV module/workflow. It's still not great, the main branch needs some more love, but the below should get you 99% of the way towards working code.
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