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pacsomatic

Automate matched tumor-normal workflow analysis from BAM inputs using nf-core/pacsomatic.

Updated May 17, 2026
Search Tags:#workflow automation#bioinformatics#tumor-normal analysis#BAM analysis#NF-core/pacsomatic
One-click install
npx skills add https://github.com/galeep/plugin-place --skill pacsomatic-galeep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pacsomatic
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-bioinformatics-genomics/skills/pacsomatic
Command: npx skills add https://github.com/galeep/plugin-place --skill pacsomatic-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, nf-core/pacsomatic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the analysis of matched tumor-normal workflows from BAM inputs, streamlining validation, artifact generation, and execution.

Core Features & Use Cases

  • Operator Toolkit: Provides a comprehensive set of tools for validating inputs, generating samplesheets, and preparing launch artifacts.
  • Local/Remote Execution: Supports local execution or submission to schedulers (LSF/Slurm/PBS/SGE).
  • Dry-Run Validation: Allows for dry-run checks before actual execution.
  • Error Triage: Assists in troubleshooting pipeline startup and scheduler submission errors.
  • Use Case: Ideal for researchers and bioinformaticians who need to analyze matched tumor-normal workflows and want to automate the process for efficiency and reliability.

Quick Start

Run the pacsomatic skill to analyze tumor-normal samples from BAM files, using the following command:

python scripts/run_pacsomatic.py --tumor-bam /path/to/tumor.bam --normal-bam /path/to/normal.bam --patient-id P001 --tumor-sample-id P001_T --normal-sample-id P001_N --outdir /path/to/output --genome GRCh38

Frequently Asked Questions about pacsomatic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate tumor-normal analysis workflows from BAM inputs?▼

You can automate tumor-normal analysis workflows from BAM inputs by using a skill that performs validation, artifact generation, and execution via nf-core/pacsomatic. It streamlines matched sample processing from initial BAM files to final results.

What is the best way to run nf-core/pacsomatic on a Slurm or LSF scheduler?▼

Running nf-core/pacsomatic on a Slurm or LSF scheduler is supported natively by this skill. It handles scheduler submission and assists in troubleshooting pipeline startup or scheduler submission errors during execution.

Do I need Python installed to run matched tumor-normal BAM analysis with this workflow?▼

Yes, you need Python installed and configured to run matched tumor-normal BAM analysis. The workflow requires both Python and nf-core/pacsomatic to be properly set up in your environment to execute successfully.

Can I validate a tumor-normal BAM pipeline configuration before actual execution?▼

Yes, you can validate a tumor-normal BAM pipeline configuration before execution by using the dry-run validation feature. This allows you to perform checks and prepare launch artifacts without submitting the full workload.

How does the pacsomatic skill handle samplesheet generation for tumor-normal pairs?▼

The pacsomatic skill handles samplesheet generation through its operator toolkit, which validates inputs and prepares launch artifacts for tumor-normal pairs. This simplifies configuration before executing the nf-core/pacsomatic pipeline.

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