菜单
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.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Strategic scientific problem selection, project ideation, and troubleshooting based on the Fischbach & Walsh framework.
Deep learning for single-cell analysis using scvi-tools and scverse ecosystem. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping, (7) RNA velocity with veloVI, or (8) QC analysis of single-cell RNA-seq data. Triggers include scVI, scANVI, totalVI, QC, quality control, batch correction, integration, multi-modal.
A conversational framework for systematic scientific problem selection, project ideation, troubleshooting, and strategic decision making.
You MUST use this before any creative work (features, products, content, strategy, systems, or behavior changes). Start by classifying what we’re brainstorming, then run thorough one-question-at-a-time discovery, propose 2–3 approaches, and converge on a validated plan/spec.
| 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"] |
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.
- [ ] 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
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:
# 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:
Then continue to Step 1.
Run first. Pipeline will fail without passing environment.
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).
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:
python src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts/detect_data_type.py /path/to/data