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Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE\u002FGSM\u002FSRR accessions, or samplesheet creation.","monoes\u002Fmonomind","monoes","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F276744696?v=4","01M71KWF37AJ2NJBP3KWBVSDGT","01M71KWF4CSF8ADH0M6HA09VD2",23,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fnextflow-development\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fnextflow-development\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"development","nextflow development","nextflow","seq","data","analyzing","gene","run","gene expression","RNA","work","analysis","when","either","sequencing","knowledge","expression","local","creation","differential expression","2026-11-10 10:56:23",false,"---\nname: nextflow-development\ndescription: \"Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS\u002FWES, or ATAC-seq data—either local FASTQs or public datasets from GEO\u002FSRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE\u002FGSM\u002FSRR accessions, or samplesheet creation.\"\ntags: []\ntools: []\nlicense: Apache-2.0\nsource: https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\nsource_path: \"bio-research\u002Fskills\u002Fnextflow-development\"\nsource_commit: 8444efcd48f7012f09797778a36a33e73d0861f4\n---\n# nf-core Pipeline Deployment\n\nRun nf-core bioinformatics pipelines on local or public sequencing data.\n\n**Target users:** Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis.\n\n## Workflow Checklist\n\n```\n- [ ] Step 0: Acquire data (if from GEO\u002FSRA)\n- [ ] Step 1: Environment check (MUST pass)\n- [ ] Step 2: Select pipeline (confirm with user)\n- [ ] Step 3: Run test profile (MUST pass)\n- [ ] Step 4: Create samplesheet\n- [ ] Step 5: Configure & run (confirm genome with user)\n- [ ] Step 6: Verify outputs\n```\n\n---\n\n## Step 0: Acquire Data (GEO\u002FSRA Only)\n\n**Skip this step if user has local FASTQ files.**\n\nFor public datasets, fetch from GEO\u002FSRA first. See [references\u002Fgeo-sra-acquisition.md](references\u002Fgeo-sra-acquisition.md) for the full workflow.\n\n**Quick start:**\n\n```bash\n# 1. Get study info\npython scripts\u002Fsra_geo_fetch.py info GSE110004\n\n# 2. Download (interactive mode)\npython scripts\u002Fsra_geo_fetch.py download GSE110004 -o .\u002Ffastq -i\n\n# 3. Generate samplesheet\npython scripts\u002Fsra_geo_fetch.py samplesheet GSE110004 --fastq-dir .\u002Ffastq -o samplesheet.csv\n```\n\n**DECISION POINT:** After fetching study info, confirm with user:\n- Which sample subset to download (if multiple data types)\n- Suggested genome and pipeline\n\nThen continue to Step 1.\n\n---\n\n## Step 1: Environment Check\n\n**Run first. Pipeline will fail without passing environment.**\n\n```bash\npython scripts\u002Fcheck_environment.py\n```\n\nAll critical checks must pass. If any fail, provide fix instructions:\n\n### Docker issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed | Install from https:\u002F\u002Fdocs.docker.com\u002Fget-docker\u002F |\n| Permission denied | `sudo usermod -aG docker $USER` then re-login |\n| Daemon not running | `sudo systemctl start docker` |\n\n### Nextflow issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed | `curl -s https:\u002F\u002Fget.nextflow.io \\| bash && mv nextflow ~\u002Fbin\u002F` |\n| Version \u003C 23.04 | `nextflow self-update` |\n\n### Java issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed \u002F \u003C 11 | `sudo apt install openjdk-11-jdk` |\n\n**Do not proceed until all checks pass.** For HPC\u002FSingularity, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n---\n\n## Step 2: Select Pipeline\n\n**DECISION POINT: Confirm with user before proceeding.**\n\n| Data Type | Pipeline | Version | Goal |\n|-----------|----------|---------|------|\n| RNA-seq | `rnaseq` | 3.22.2 | Gene expression |\n| WGS\u002FWES | `sarek` | 3.7.1 | Variant calling |\n| ATAC-seq | `atacseq` | 2.1.2 | Chromatin accessibility |\n\nAuto-detect from data:\n```bash\npython scripts\u002Fdetect_data_type.py \u002Fpath\u002Fto\u002Fdata\n```\n\nFor pipeline-specific details:\n- [references\u002Fpipelines\u002Frnaseq.md](references\u002Fpipelines\u002Frnaseq.md)\n- [references\u002Fpipelines\u002Fsarek.md](references\u002Fpipelines\u002Fsarek.md)\n- [references\u002Fpipelines\u002Fatacseq.md](references\u002Fpipelines\u002Fatacseq.md)\n\n---\n\n## Step 3: Run Test Profile\n\n**Validates environment with small data. MUST pass before real data.**\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> -r \u003Cversion> -profile test,docker --outdir test_output\n```\n\n| Pipeline | Command |\n|----------|---------|\n| rnaseq | `nextflow run nf-core\u002Frnaseq -r 3.22.2 -profile test,docker --outdir test_rnaseq` |\n| sarek | `nextflow run nf-core\u002Fsarek -r 3.7.1 -profile test,docker --outdir test_sarek` |\n| atacseq | `nextflow run nf-core\u002Fatacseq -r 2.1.2 -profile test,docker --outdir test_atacseq` |\n\nVerify:\n```bash\nls test_output\u002Fmultiqc\u002Fmultiqc_report.html\ngrep \"Pipeline completed successfully\" .nextflow.log\n```\n\nIf test fails, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n---\n\n## Step 4: Create Samplesheet\n\n### Generate automatically\n\n```bash\npython scripts\u002Fgenerate_samplesheet.py \u002Fpath\u002Fto\u002Fdata \u003Cpipeline> -o samplesheet.csv\n```\n\nThe script:\n- Discovers FASTQ\u002FBAM\u002FCRAM files\n- Pairs R1\u002FR2 reads\n- Infers sample metadata\n- Validates before writing\n\n**For sarek:** Script prompts for tumor\u002Fnormal status if not auto-detected.\n\n### Validate existing samplesheet\n\n```bash\npython scripts\u002Fgenerate_samplesheet.py --validate samplesheet.csv \u003Cpipeline>\n```\n\n### Samplesheet formats\n\n**rnaseq:**\n```csv\nsample,fastq_1,fastq_2,strandedness\nSAMPLE1,\u002Fabs\u002Fpath\u002FR1.fq.gz,\u002Fabs\u002Fpath\u002FR2.fq.gz,auto\n```\n\n**sarek:**\n```csv\npatient,sample,lane,fastq_1,fastq_2,status\npatient1,tumor,L001,\u002Fabs\u002Fpath\u002Ftumor_R1.fq.gz,\u002Fabs\u002Fpath\u002Ftumor_R2.fq.gz,1\npatient1,normal,L001,\u002Fabs\u002Fpath\u002Fnormal_R1.fq.gz,\u002Fabs\u002Fpath\u002Fnormal_R2.fq.gz,0\n```\n\n**atacseq:**\n```csv\nsample,fastq_1,fastq_2,replicate\nCONTROL,\u002Fabs\u002Fpath\u002Fctrl_R1.fq.gz,\u002Fabs\u002Fpath\u002Fctrl_R2.fq.gz,1\n```\n\n---\n\n## Step 5: Configure & Run\n\n### 5a. Check genome availability\n\n```bash\npython scripts\u002Fmanage_genomes.py check \u003Cgenome>\n# If not installed:\npython scripts\u002Fmanage_genomes.py download \u003Cgenome>\n```\n\nCommon genomes: GRCh38 (human), GRCh37 (legacy), GRCm39 (mouse), R64-1-1 (yeast), BDGP6 (fly)\n\n### 5b. Decision points\n\n**DECISION POINT: Confirm with user:**\n\n1. **Genome:** Which reference to use\n2. **Pipeline-specific options:**\n   - **rnaseq:** aligner (star_salmon recommended, hisat2 for low memory)\n   - **sarek:** tools (haplotypecaller for germline, mutect2 for somatic)\n   - **atacseq:** read_length (50, 75, 100, or 150)\n\n### 5c. Run pipeline\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> \\\n    -r \u003Cversion> \\\n    -profile docker \\\n    --input samplesheet.csv \\\n    --outdir results \\\n    --genome \u003Cgenome> \\\n    -resume\n```\n\n**Key flags:**\n- `-r`: Pin version\n- `-profile docker`: Use Docker (or `singularity` for HPC)\n- `--genome`: iGenomes key\n- `-resume`: Continue from checkpoint\n\n**Resource limits (if needed):**\n```bash\n--max_cpus 8 --max_memory '32.GB' --max_time '24.h'\n```\n\n---\n\n## Step 6: Verify Outputs\n\n### Check completion\n\n```bash\nls results\u002Fmultiqc\u002Fmultiqc_report.html\ngrep \"Pipeline completed successfully\" .nextflow.log\n```\n\n### Key outputs by pipeline\n\n**rnaseq:**\n- `results\u002Fstar_salmon\u002Fsalmon.merged.gene_counts.tsv` - Gene counts\n- `results\u002Fstar_salmon\u002Fsalmon.merged.gene_tpm.tsv` - TPM values\n\n**sarek:**\n- `results\u002Fvariant_calling\u002F*\u002F` - VCF files\n- `results\u002Fpreprocessing\u002Frecalibrated\u002F` - BAM files\n\n**atacseq:**\n- `results\u002Fmacs2\u002FnarrowPeak\u002F` - Peak calls\n- `results\u002Fbwa\u002FmergedLibrary\u002Fbigwig\u002F` - Coverage tracks\n\n---\n\n## Quick Reference\n\nFor common exit codes and fixes, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n### Resume failed run\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> -resume\n```\n\n---\n\n## References\n\n- [references\u002Fgeo-sra-acquisition.md](references\u002Fgeo-sra-acquisition.md) - Downloading public GEO\u002FSRA data\n- [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md) - Common issues and fixes\n- [references\u002Finstallation.md](references\u002Finstallation.md) - Environment setup\n- [references\u002Fpipelines\u002Frnaseq.md](references\u002Fpipelines\u002Frnaseq.md) - RNA-seq pipeline details\n- [references\u002Fpipelines\u002Fsarek.md](references\u002Fpipelines\u002Fsarek.md) - Variant calling details\n- [references\u002Fpipelines\u002Fatacseq.md](references\u002Fpipelines\u002Fatacseq.md) - ATAC-seq details\n\n---\n\n## Disclaimer\n\nThis skill is provided as a prototype example demonstrating how to integrate nf-core bioinformatics pipelines into Claude Code for automated analysis workflows. The current implementation supports three pipelines (rnaseq, sarek, and atacseq), serving as a foundation that enables the community to expand support to the full set of nf-core pipelines.\n\nIt is intended for educational and research purposes and should not be considered production-ready without appropriate validation for your specific use case. Users are responsible for ensuring their computing environment meets pipeline requirements and for verifying analysis results.\n\nAnthropic does not guarantee the accuracy of bioinformatics outputs, and users should follow standard practices for validating computational analyses. This integration is not officially endorsed by or affiliated with the nf-core community.\n\n## Attribution\n\nWhen publishing results, cite the appropriate pipeline. Citations are available in each nf-core repository's CITATIONS.md file (e.g., https:\u002F\u002Fgithub.com\u002Fnf-core\u002Frnaseq\u002Fblob\u002F3.22.2\u002FCITATIONS.md).\n\n## Licenses\n\n- **nf-core pipelines:** MIT License (https:\u002F\u002Fnf-co.re\u002Fabout)\n- **Nextflow:** Apache License, Version 2.0 (https:\u002F\u002Fwww.nextflow.io\u002Fabout-us.html)\n- **NCBI SRA Toolkit:** Public Domain (https:\u002F\u002Fgithub.com\u002Fncbi\u002Fsra-tools\u002Fblob\u002Fmaster\u002FLICENSE)\n","# nf-core Pipeline Deployment\n\nRun nf-core bioinformatics pipelines on local or public sequencing data.\n\n**Target users:** Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis.\n\n## Workflow Checklist\n\n```\n- [ ] Step 0: Acquire data (if from GEO\u002FSRA)\n- [ ] Step 1: Environment check (MUST pass)\n- [ ] Step 2: Select pipeline (confirm with user)\n- [ ] Step 3: Run test profile (MUST pass)\n- [ ] Step 4: Create samplesheet\n- [ ] Step 5: Configure & run (confirm genome with user)\n- [ ] Step 6: Verify outputs\n```\n\n---\n\n## Step 0: Acquire Data (GEO\u002FSRA Only)\n\n**Skip this step if user has local FASTQ files.**\n\nFor public datasets, fetch from GEO\u002FSRA first. See [references\u002Fgeo-sra-acquisition.md](references\u002Fgeo-sra-acquisition.md) for the full workflow.\n\n**Quick start:**\n\n```bash\n# 1. Get study info\npython scripts\u002Fsra_geo_fetch.py info GSE110004\n\n# 2. Download (interactive mode)\npython scripts\u002Fsra_geo_fetch.py download GSE110004 -o .\u002Ffastq -i\n\n# 3. Generate samplesheet\npython scripts\u002Fsra_geo_fetch.py samplesheet GSE110004 --fastq-dir .\u002Ffastq -o samplesheet.csv\n```\n\n**DECISION POINT:** After fetching study info, confirm with user:\n- Which sample subset to download (if multiple data types)\n- Suggested genome and pipeline\n\nThen continue to Step 1.\n\n---\n\n## Step 1: Environment Check\n\n**Run first. Pipeline will fail without passing environment.**\n\n```bash\npython scripts\u002Fcheck_environment.py\n```\n\nAll critical checks must pass. If any fail, provide fix instructions:\n\n### Docker issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed | Install from https:\u002F\u002Fdocs.docker.com\u002Fget-docker\u002F |\n| Permission denied | `sudo usermod -aG docker $USER` then re-login |\n| Daemon not running | `sudo systemctl start docker` |\n\n### Nextflow issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed | `curl -s https:\u002F\u002Fget.nextflow.io \\| bash && mv nextflow ~\u002Fbin\u002F` |\n| Version \u003C 23.04 | `nextflow self-update` |\n\n### Java issues\n\n| Problem | Fix |\n|---------|-----|\n| Not installed \u002F \u003C 11 | `sudo apt install openjdk-11-jdk` |\n\n**Do not proceed until all checks pass.** For HPC\u002FSingularity, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n---\n\n## Step 2: Select Pipeline\n\n**DECISION POINT: Confirm with user before proceeding.**\n\n| Data Type | Pipeline | Version | Goal |\n|-----------|----------|---------|------|\n| RNA-seq | `rnaseq` | 3.22.2 | Gene expression |\n| WGS\u002FWES | `sarek` | 3.7.1 | Variant calling |\n| ATAC-seq | `atacseq` | 2.1.2 | Chromatin accessibility |\n\nAuto-detect from data:\n```bash\npython scripts\u002Fdetect_data_type.py \u002Fpath\u002Fto\u002Fdata\n```\n\nFor pipeline-specific details:\n- [references\u002Fpipelines\u002Frnaseq.md](references\u002Fpipelines\u002Frnaseq.md)\n- [references\u002Fpipelines\u002Fsarek.md](references\u002Fpipelines\u002Fsarek.md)\n- [references\u002Fpipelines\u002Fatacseq.md](references\u002Fpipelines\u002Fatacseq.md)\n\n---\n\n## Step 3: Run Test Profile\n\n**Validates environment with small data. MUST pass before real data.**\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> -r \u003Cversion> -profile test,docker --outdir test_output\n```\n\n| Pipeline | Command |\n|----------|---------|\n| rnaseq | `nextflow run nf-core\u002Frnaseq -r 3.22.2 -profile test,docker --outdir test_rnaseq` |\n| sarek | `nextflow run nf-core\u002Fsarek -r 3.7.1 -profile test,docker --outdir test_sarek` |\n| atacseq | `nextflow run nf-core\u002Fatacseq -r 2.1.2 -profile test,docker --outdir test_atacseq` |\n\nVerify:\n```bash\nls test_output\u002Fmultiqc\u002Fmultiqc_report.html\ngrep \"Pipeline completed successfully\" .nextflow.log\n```\n\nIf test fails, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n---\n\n## Step 4: Create Samplesheet\n\n### Generate automatically\n\n```bash\npython scripts\u002Fgenerate_samplesheet.py \u002Fpath\u002Fto\u002Fdata \u003Cpipeline> -o samplesheet.csv\n```\n\nThe script:\n- Discovers FASTQ\u002FBAM\u002FCRAM files\n- Pairs R1\u002FR2 reads\n- Infers sample metadata\n- Validates before writing\n\n**For sarek:** Script prompts for tumor\u002Fnormal status if not auto-detected.\n\n### Validate existing samplesheet\n\n```bash\npython scripts\u002Fgenerate_samplesheet.py --validate samplesheet.csv \u003Cpipeline>\n```\n\n### Samplesheet formats\n\n**rnaseq:**\n```csv\nsample,fastq_1,fastq_2,strandedness\nSAMPLE1,\u002Fabs\u002Fpath\u002FR1.fq.gz,\u002Fabs\u002Fpath\u002FR2.fq.gz,auto\n```\n\n**sarek:**\n```csv\npatient,sample,lane,fastq_1,fastq_2,status\npatient1,tumor,L001,\u002Fabs\u002Fpath\u002Ftumor_R1.fq.gz,\u002Fabs\u002Fpath\u002Ftumor_R2.fq.gz,1\npatient1,normal,L001,\u002Fabs\u002Fpath\u002Fnormal_R1.fq.gz,\u002Fabs\u002Fpath\u002Fnormal_R2.fq.gz,0\n```\n\n**atacseq:**\n```csv\nsample,fastq_1,fastq_2,replicate\nCONTROL,\u002Fabs\u002Fpath\u002Fctrl_R1.fq.gz,\u002Fabs\u002Fpath\u002Fctrl_R2.fq.gz,1\n```\n\n---\n\n## Step 5: Configure & Run\n\n### 5a. Check genome availability\n\n```bash\npython scripts\u002Fmanage_genomes.py check \u003Cgenome>\n# If not installed:\npython scripts\u002Fmanage_genomes.py download \u003Cgenome>\n```\n\nCommon genomes: GRCh38 (human), GRCh37 (legacy), GRCm39 (mouse), R64-1-1 (yeast), BDGP6 (fly)\n\n### 5b. Decision points\n\n**DECISION POINT: Confirm with user:**\n\n1. **Genome:** Which reference to use\n2. **Pipeline-specific options:**\n   - **rnaseq:** aligner (star_salmon recommended, hisat2 for low memory)\n   - **sarek:** tools (haplotypecaller for germline, mutect2 for somatic)\n   - **atacseq:** read_length (50, 75, 100, or 150)\n\n### 5c. Run pipeline\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> \\\n    -r \u003Cversion> \\\n    -profile docker \\\n    --input samplesheet.csv \\\n    --outdir results \\\n    --genome \u003Cgenome> \\\n    -resume\n```\n\n**Key flags:**\n- `-r`: Pin version\n- `-profile docker`: Use Docker (or `singularity` for HPC)\n- `--genome`: iGenomes key\n- `-resume`: Continue from checkpoint\n\n**Resource limits (if needed):**\n```bash\n--max_cpus 8 --max_memory '32.GB' --max_time '24.h'\n```\n\n---\n\n## Step 6: Verify Outputs\n\n### Check completion\n\n```bash\nls results\u002Fmultiqc\u002Fmultiqc_report.html\ngrep \"Pipeline completed successfully\" .nextflow.log\n```\n\n### Key outputs by pipeline\n\n**rnaseq:**\n- `results\u002Fstar_salmon\u002Fsalmon.merged.gene_counts.tsv` - Gene counts\n- `results\u002Fstar_salmon\u002Fsalmon.merged.gene_tpm.tsv` - TPM values\n\n**sarek:**\n- `results\u002Fvariant_calling\u002F*\u002F` - VCF files\n- `results\u002Fpreprocessing\u002Frecalibrated\u002F` - BAM files\n\n**atacseq:**\n- `results\u002Fmacs2\u002FnarrowPeak\u002F` - Peak calls\n- `results\u002Fbwa\u002FmergedLibrary\u002Fbigwig\u002F` - Coverage tracks\n\n---\n\n## Quick Reference\n\nFor common exit codes and fixes, see [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md).\n\n### Resume failed run\n\n```bash\nnextflow run nf-core\u002F\u003Cpipeline> -resume\n```\n\n---\n\n## References\n\n- [references\u002Fgeo-sra-acquisition.md](references\u002Fgeo-sra-acquisition.md) - Downloading public GEO\u002FSRA data\n- [references\u002Ftroubleshooting.md](references\u002Ftroubleshooting.md) - Common issues and fixes\n- [references\u002Finstallation.md](references\u002Finstallation.md) - Environment setup\n- [references\u002Fpipelines\u002Frnaseq.md](references\u002Fpipelines\u002Frnaseq.md) - RNA-seq pipeline details\n- [references\u002Fpipelines\u002Fsarek.md](references\u002Fpipelines\u002Fsarek.md) - Variant calling details\n- [references\u002Fpipelines\u002Fatacseq.md](references\u002Fpipelines\u002Fatacseq.md) - ATAC-seq details\n\n---\n\n## Disclaimer\n\nThis skill is provided as a prototype example demonstrating how to integrate nf-core bioinformatics pipelines into Claude Code for automated analysis workflows. The current implementation supports three pipelines (rnaseq, sarek, and atacseq), serving as a foundation that enables the community to expand support to the full set of nf-core pipelines.\n\nIt is intended for educational and research purposes and should not be considered production-ready without appropriate validation for your specific use case. Users are responsible for ensuring their computing environment meets pipeline requirements and for verifying analysis results.\n\nAnthropic does not guarantee the accuracy of bioinformatics outputs, and users should follow standard practices for validating computational analyses. This integration is not officially endorsed by or affiliated with the nf-core community.\n\n## Attribution\n\nWhen publishing results, cite the appropriate pipeline. 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