Pacsomatic Guide

This guide summarizes the official nf-core/pacsomatic usage and how this skill helps an agent validate, prepare, and launch runs across compute platforms.

What nf-core/pacsomatic runs

nf-core/pacsomatic is a Nextflow pipeline for matched tumor/normal PacBio HiFi somatic analysis.

Typical upstream command from official docs:

nextflow run nf-core/pacsomatic \
  -profile <docker/singularity/.../institute> \
  --input samplesheet.csv \
  --outdir <OUTDIR> \
  --genome GRCh38

Important notes from docs:

Input is a CSV samplesheet with columns: patient,sample,status,bam[,pbi].
status uses 1 for tumor and 0 for normal.
Pipeline parameters should be passed via CLI flags or -params-file, not -c.

Cross-agent reuse

This skill can be reused by other agents in the same workspace.

Keep the whole folder .github/skills/pacsomatic intact when reusing.
Other agents can either:
call scripts/run_pacsomatic.py to generate a backend-aware launch script, or
emit a direct platform-native script and return the matching launcher command.
If moving to another repository, copy the same folder structure and keep SKILL.md, references/, and scripts/ together.
Validate environment assumptions per cluster (Nextflow module version, profile such as singularity,sanger, queue/project names, and network access for remote BAM URLs).

Minimal samplesheet format

patient,sample,status,bam,pbi
ID1,ID1_tumor,1,/path/ID1_tumor.bam,/path/ID1_tumor.bam.pbi
ID1,ID1_normal,0,/path/ID1_normal.bam,/path/ID1_normal.bam.pbi

pbi is optional. If not available, leave it blank.

Platform-aware execution model in this skill

By default, the helper generates artifacts. With --run, it executes/submits using the selected --executor backend.

It produces:

A validated samplesheet CSV from tumor/normal BAM inputs
A standalone launch script that runs Nextflow + nf-core/pacsomatic
A ready run command (for example bash, bsub, sbatch, qsub)
Launcher output and detected job ID when --run is enabled

Example: generate script only

python .github/skills/pacsomatic/scripts/run_pacsomatic.py \
  --tumor-bam /data/P1_tumor.bam \
  --normal-bam /data/P1_normal.bam \
  --patient-id P1 \
  --tumor-sample-id P1_tumor \
  --normal-sample-id P1_normal \
  --fasta /refs/GRCh38.fa \
  --outdir /results/p1 \
  --profile singularity \
  --executor local \
  --queue normal \
  --cpus 16 \
  --memory-gb 64 \
  --walltime 48:00

Example: dry-run validation

python .github/skills/pacsomatic/scripts/run_pacsomatic.py \
  --tumor-bam /data/P1_tumor.bam \
  --normal-bam /data/P1_normal.bam \
  --patient-id P1 \
  --tumor-sample-id P1_tumor \
  --normal-sample-id P1_normal \
  --genome GRCh38 \
  --outdir /results/p1 \
  --profile singularity \
  --executor local \
  --dry-run

Example: generate and submit immediately

python .github/skills/pacsomatic/scripts/run_pacsomatic.py \
  --tumor-bam /data/P1_tumor.bam \
  --normal-bam /data/P1_normal.bam \
  --patient-id P1 \
  --tumor-sample-id P1_tumor \
  --normal-sample-id P1_normal \
  --genome GRCh38 \
  --outdir /results/p1 \
  --profile singularity \
  --executor lsf \
  --run

Example: submit on Slurm

python .github/skills/pacsomatic/scripts/run_pacsomatic.py \
  --tumor-bam /data/P1_tumor.bam \
  --normal-bam /data/P1_normal.bam \
  --patient-id P1 \
  --tumor-sample-id P1_tumor \
  --normal-sample-id P1_normal \
  --genome GRCh38 \
  --outdir /results/p1 \
  --profile singularity \
  --executor slurm \
  --queue compute \
  --cpus 16 \
  --memory-gb 64 \
  --run

Direct command-line operation (no agent wrapper)

The helper is a standalone CLI and can be run directly from shell scripts, terminal sessions, CI jobs, or workflow launch wrappers.

Supported HPC schedulers via --executor:

lsf (uses bsub)
slurm (uses sbatch)
pbs (uses qsub)
sge (uses qsub)

Local direct execution is also supported with:

--executor local (runs generated script with bash)

Direct LSF script (no Python wrapper)

If users request a ready-to-submit LSF script directly, provide a .lsf.sh file that can be submitted as-is.

Example file: submit_pacsomatic_hg008.lsf.sh

#!/usr/bin/env bash
#BSUB -J Somatic_singularity
#BSUB -P Somatic_singularity
#BSUB -q heavy_io
#BSUB -n 16
#BSUB -M 64000
#BSUB -W 48:00
#BSUB -o out%J.out
#BSUB -e err%J.err

set -euo pipefail

RUN_DIR="${RUN_DIR:-$PWD/pacsomatic_hg008_run}"
OUTDIR="${OUTDIR:-$RUN_DIR/results}"
WORKDIR="${WORKDIR:-$RUN_DIR/work}"
SAMPLESHEET="$RUN_DIR/samplesheet.csv"

mkdir -p "$RUN_DIR" "$OUTDIR" "$WORKDIR"

cat > "$SAMPLESHEET" << 'CSV'
patient,sample,status,bam,pbi
Patient_HG008,DS_MT_T,1,https://raw.githubusercontent.com/nf-core/test-datasets/pacsomatic/testdata/HG008_Downsample_MT_tumor.bam,
Patient_HG008,DS_MT_N,0,https://raw.githubusercontent.com/nf-core/test-datasets/pacsomatic/testdata/HG008_Downsample_MT_normal.bam,
CSV

module load nextflow/21.10.5
export NXF_WORK="$WORKDIR"

nextflow run nf-core/pacsomatic \
  -profile singularity,sanger \
  --input "$SAMPLESHEET" \
  --outdir "$OUTDIR" \
  --genome GRCh38 \
  -with-report "$OUTDIR/HiFi_Somatic_Nextflow_Run_Report.html" \
  -with-dag "$OUTDIR/HiFi_Somatic_Flowchart.png" \
  -resume

Submit with:

bsub < submit_pacsomatic_hg008.lsf.sh

LSF examples aligned with your cluster style

The script supports your style of submission, including -P, queue switching, module load nextflow/21.10.5, -resume, and report/DAG outputs.

Built-in defaults now match your common combo:

project: Somatic_singularity
queue: heavy_io
module-load: module load nextflow/21.10.5

Default LSF output naming now follows your style:

stdout: out%J.out
stderr: err%J.err

You can override with --stdout-file and --stderr-file, and optionally set --logdir to place them under a specific directory.

python .github/skills/pacsomatic/scripts/run_pacsomatic.py \
  --tumor-bam /data/P1_tumor.bam \
  --normal-bam /data/P1_normal.bam \
  --patient-id P1 \
  --tumor-sample-id P1_tumor \
  --normal-sample-id P1_normal \
  --genome GRCh38 \
  --outdir /results/p1 \
  --project Somatic_test \
  --queue heavy_io \
  --memory-gb 20 \
  --job-name Somatic_test \
  --module-load "module load nextflow/21.10.5" \
  --with-report HiFi_Somatic_Nextflow_Run_Report.html \
  --with-dag HiFi_Somatic_Flowchart.png

For your Sanger configs usage, set combined profiles such as:

--profile singularity,sanger

Reference: https://nf-co.re/configs/sanger/

Best practices

Ensure BAMs are coordinate-valid and index files are available when possible.
Use explicit pipeline version with --pipeline-version for reproducibility.
Prefer pinning --pipeline-version when using fixed test datasets to avoid schema drift across pipeline revisions.
Use --params-file for large parameter sets and keep script options minimal.
Prefer containerized profile (singularity or docker) on HPC.
Set NXF_OPTS memory ceiling if Nextflow launcher memory spikes.
nf-core/pacsomatic may require a newer Nextflow than legacy module versions; if the cluster allows, prefer a modern Nextflow release compatible with the pipeline.