Research & Analysis🤖claude💬gpt🌐universal

bio-nextflow-manager

apps/tandem-desktop/src-tauri/resources/skill-templates/bio-nextflow-manager/SKILL.md

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

Quick Start

Install this SKILL.md

npx skills add https://github.com/frumu-ai/tandem/tree/main/apps/tandem-desktop/src-tauri/resources/skill-templates/bio-nextflow-manager
---
name: bio-nextflow-manager
description: Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
requires: [python, bash]
---

# nf-core Pipeline Deployment

Run nf-core bioinformatics pipelines on local or public sequencing data.

> **Note:** This skill utilizes the **Bio-Informatics Pack**.
> Scripts and references are located in: `src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/`

**Target users:** Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses.

## Workflow Checklist

```
- [ ] Step 0: Acquire data (if from GEO/SRA)
- [ ] Step 1: Environment check (MUST pass)
- [ ] Step 2: Select pipeline (confirm with user)
- [ ] Step 3: Run test profile (MUST pass)
- [ ] Step 4: Create samplesheet
- [ ] Step 5: Configure & run (confirm genome with user)
- [ ] Step 6: Verify outputs
```

---

## Step 0: Acquire Data (GEO/SRA Only)

**Skip this step if user has local FASTQ files.**

For public datasets, fetch from GEO/SRA first. See pack's `references/geo-sra-acquisition.md`.

**Quick start:**

```bash
# Set path to pack scripts
$PACK_SCRIPTS = "src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts"

# 1. Get study info
python $PACK_SCRIPTS/sra_geo_fetch.py info GSE110004

# 2. Download (interactive mode)
python $PACK_SCRIPTS/sra_geo_fetch.py download GSE110004 -o ./fastq -i

# 3. Generate samplesheet
python $PACK_SCRIPTS/sra_geo_fetch.py samplesheet GSE110004 --fastq-dir ./fastq -o samplesheet.csv
```

**DECISION POINT:** After fetching study info, confirm with user:

- Which sample subset to download (if multiple data types)
- Suggested genome and pipeline

Then continue to Step 1.

---

## Step 1: Environment Check

**Run first. Pipeline will fail without passing environment.**

```bash
python src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts/check_environment.py
```

All critical checks must pass. If any fail, provide fix instructions (Docker, Nextflow, Java).

---

## Step 2: Select Pipeline

**DECISION POINT: Confirm with user before proceeding.**

| Data Type | Pipeline  | Goal                    |
| --------- | --------- | ----------------------- |
| RNA-seq   | `rnaseq`  | Gene expression         |
| WGS/WES   | `sarek`   | Variant calling         |
| ATAC-seq  | `atacseq` | Chromatin accessibility |

Auto-detect from data:

```bash
python src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts/detect_data_type.py /path/to/data
```

Related Skills

More research & analysis skills you might like

pr-improver
Research & Analysis
Runs an autonomous review-and-fix improvement loop over the current branch's changes until a PR review comes back clean, scoped mechanically to the directories the branch touched. Reviews are performed by an installed PR-review skill (default: pr-review-toolkit's review-pr). Use to fix review findings on a branch before opening or updating a pull request ('clean up this branch', 'fix this PR until review passes', 'run review-and-fix on my changes'). NOT for a one-time review — run the PR-review skill directly.
🤖claude🌐universal
7.5k
643
View
code-improver
Research & Analysis
Runs an autonomous review-and-fix improvement loop over any code target — a skill, plugin, module, or directory — using a reviewer the user names: any installed skill or agent. Keeps a cross-round findings ledger, escalates when fixes stop converging, and guards scope mechanically. Use when asked to 'improve this code until review passes', 'run an improvement loop with <reviewer>', or to iterate review-and-fix with a specific reviewer. For skills prefer the skill-improver entry; for a branch prefer pr-improver.
🤖claude🌐universal
7.5k
643
View
skill-improver
Research & Analysis
Runs an autonomous review-and-fix improvement loop over a Claude Code skill until a review comes back clean, with a cross-round findings ledger, escalation when fixes stop converging, and a mechanical scope guard. Reviews are performed by the plugin-dev skill-reviewer agent. Use to fix skill quality issues, iteratively refine a skill, or resume a loop after an escalation ('fix my skill', 'improve this skill until it passes review', 'skill improvement loop'). NOT for a one-time review — use the plugin-dev skill-reviewer agent directly.
🤖claude🌐universal
7.5k
643
View
audit-augmentation
Research & Analysis
Augments Trailmark code graphs with external audit findings from SARIF static analysis results, weAudit annotation files, and version-gated Trailmark 0.4.x binary-analysis graph exports. Maps findings to graph nodes by file and line overlap, creates severity-based subgraphs, and enables cross-referencing findings with pre-analysis data (blast radius, taint, etc.). Use when projecting SARIF results onto a code graph, overlaying weAudit annotations, importing binary graph findings, cross-referencing Semgrep, CodeQL, or binary-analysis findings with call graph data, or visualizing audit findings in the context of code structure.
🌐universal
7.5k
643
View