Quick start

You will need:

• Nextflow
• Docker or Singularity (Apptainer)
• Reference genome and transcriptome
• Your data in .pod5 format or pre-modification-called data from Dorado or m⁶Anet

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Prepare a samplesheet CSV samplesheet.csv file with the format:

name	group	path_dorado	path_m6anet
sample1	group1	/path/to/dorado/reads.bam	/path/to/m6anet/data.indiv_proba.csv

name,group,path_dorado,path_m6anet
sample1,group1,/path/to/dorado/reads.bam,/path/to/m6anet/data.indiv_proba.csv

If you only have one of Dorado or m6Anet data, leave the other blank.

Run the pipeline:

# load nextflow, docker/singularity modules as needed
modules load nextflow apptainer

# download the pipeline
git clone https://github.com/shimlab/mako.git && cd mako

# show all configuration settings
nextflow run main.nf --help

nextflow run main.nf \
    -profile docker  \    # OR  -profile singularity  OR  -profile `my_institution`
    --dataset_name <name> \
    --samplesheet <samplesheet.csv> \
    --outdir results \
    --transcriptome <transcriptome.fasta> \
    --gtf <annotation.gtf>

Configuration settings can be found in Configuration.

If your institution has an nf-core configuration available, you can access it through -profile i.e. -profile wehi to use the WEHI Milton HPC. See Execution Profiles for more.

Running makoview

Once the pipeline has finished, you can run the visualisation tool makoview using:

export MAKO_OUTPUT_DIR="/data/gpfs/projects/punim0614/occheng/epi_differential/pipeline/runs/longbench/results"
export MODCALLER="dorado"  # either "dorado" or "m6anet"
export DIFFERENTIAL_MODEL="adaptive_binomial"
uvx makoview \
  --differential-results $MAKO_OUTPUT_DIR/differential/$MODCALLER/${DIFFERENTIAL_MODEL}_fits.tsv \
  --modification-db $MAKO_OUTPUT_DIR/modcall/$MODCALLER/all_sites.duckdb \
  --port 8000

See makoview for more information on alternative installation methods for Makoview and remote forwarding.