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danilomonge
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differentialabundance

Automate nf-core differential abundance analyses with validated samplesheets and reproducible outputs.

1|Updated Jun 19, 2026
Search Tags:#bioinformatics#proteomics#nf-core#rna-seq#deseq2#differential-abundance
One-click install
npx skills add https://github.com/danilomonge/nf-claw --skill differentialabundance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: differentialabundance
Source: https://github.com/danilomonge/nf-claw/tree/main/pipelines/differentialabundance
Command: npx skills add https://github.com/danilomonge/nf-claw --skill differentialabundance

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

nf-claw packages the nf-core differentialabundance pipeline so an AI agent can run differential abundance analyses without guessing flags, missing required inputs, or drifting from the pinned release.

Core Features & Use Cases

  • Release-pinned execution: Run the exact upstream pipeline version captured in the skill, with reproducible provenance and consistent outputs.
  • Broad omics coverage: Handle RNA-seq, Affymetrix array, MaxQuant proteomics, GEO SOFT, and generic matrix-based studies from a single entry point.
  • Validated analysis setup: Provide the samplesheet, study type, and optional analysis choices, and the skill maps them to the correct required parameters and defaults.
  • Typical use case: Compare sample groups in a matrix experiment, generate differential statistics, and produce downstream exploratory and functional analysis reports.

Quick Start

Tell the skill to run differentialabundance for your dataset with the correct samplesheet, study type, and output directory, and let it assemble the exact reproducible command for the pinned release.

Frequently Asked Questions about differentialabundance

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

FAQPage Schema
How do I run differential abundance analysis for RNA-seq and proteomics data?▼

Differential abundance analysis for RNA-seq and proteomics data is automated by validating samplesheets, selecting correct modules, and executing pinned nf-core pipelines to generate reproducible statistics and reports.

What inputs do I need for nf-core differential abundance workflows?▼

For nf-core differential abundance workflows, you need a validated samplesheet, study type specification, and matrix-based inputs like RNA-seq counts, MaxQuant proteomics files, Affymetrix arrays, or GEO SOFT files.

Can I use DESeq2 for differential abundance analysis through nf-core?▼

Yes, DESeq2 is supported for differential abundance analysis. The skill maps your study type and parameters to the correct nf-core analysis modules, preserving reproducible provenance and consistent output contracts.

Does this differential abundance approach support both RNA-seq and MaxQuant proteomics?▼

Yes, the differential abundance approach supports broad omics coverage including RNA-seq, MaxQuant proteomics, Affymetrix arrays, and GEO SOFT files from a single validated entry point without guessing flags.

How to ensure reproducible provenance when running differential abundance pipelines?▼

To ensure reproducible provenance when running differential abundance pipelines, this skill executes a release-pinned upstream nf-core version, capturing exact parameters and defaults to produce consistent outputs.

What is the best way to compare sample groups in a matrix experiment?▼

The best way to compare sample groups in a matrix experiment is providing the samplesheet and study type, allowing the skill to assemble the exact reproducible command and generate downstream functional analysis reports.

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