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For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting.","bg-szy\u002FTOP-SKILLS","bg-szy","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F106576190?v=4","01M73A4E8KPV67SFXRY8XZGNBX","ShuZhiyong","01M73A4E96MQ294MHM74TZBVQD",6,"GPL-3.0","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fawesome-skills\u002Fmultiqc-qc-reports\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: \"multiqc-qc-reports\"\ndescription: \"Aggregates QC from 150+ bioinformatics tools into one interactive HTML report. Scans FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, GATK logs; merges per-sample stats with plots. For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting.\"\nlicense: \"GPL-3.0\"\n---\n\n# MultiQC — Multi-Sample QC Report Aggregator\n\n## Overview\n\nMultiQC automatically searches directories for QC log files from 150+ bioinformatics tools and aggregates statistics across all samples into a single interactive HTML report. It parses outputs from FastQC, samtools flagstat, STAR, HISAT2, Trim Galore, Salmon, Kallisto, featureCounts, Picard, GATK, and many more — eliminating the need to manually review per-sample QC files. Reports include interactive bar plots, scatter plots, heatmaps, and tables with configurable warnings and pass\u002Ffail thresholds.\n\n## When to Use\n\n- Reviewing QC metrics across 10+ samples at once after FastQC, alignment, or quantification\n- Final QC checkpoint before differential expression or variant analysis\n- Sharing QC summaries with collaborators or including in publications\n- Identifying batch effects, outlier samples, or failed sequencing runs\n- Combining QC from multi-step pipelines (trimming → alignment → quantification) into one view\n- Use FastQC directly instead for initial single-sample QC exploration\n- For custom QC metrics not from standard tools, use Python\u002FR directly; MultiQC parses tool outputs only\n\n## Prerequisites\n\n- **Python packages**: `multiqc`\n- **Input requirements**: Output files from bioinformatics tools (FastQC `.zip`, samtools `.flagstat`, STAR `Log.final.out`, etc.) — MultiQC finds them automatically\n- **Environment**: Python 3.8+\n\n> **Check before installing**: The tool may already be available in the current environment (e.g., inside a `pixi` \u002F `conda` env). Run `command -v multiqc` first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via `pixi run multiqc` rather than bare `multiqc`.\n\n```bash\npip install multiqc\n\n# Verify\nmultiqc --version\n# MultiQC v1.25.0\n\n# With conda (recommended for bioinformatics)\nconda install -c bioconda multiqc\n```\n\n## Pre-flight Interview\n\nSettle these with the user before writing any analysis code.\n\n```yaml\ndecisions:\n  - id: D1\n    param: searchScope\n    kind: required\n    source: data\n    ask: \"Which directories should be scanned, and are there old or partial runs in them that must be excluded?\"\n    default: \"the pipeline output root, with no exclusions\"\n\n  - id: D2\n    param: sampleNameCleaning\n    kind: required\n    source: data\n    ask: \"Do the discovered sample names collapse correctly, or do suffixes from different tools split one sample into several rows?\"\n    default: \"built-in extension stripping; verify the resulting sample list\"\n\n  - id: D3\n    param: moduleFilter\n    kind: optional\n    source: user\n    ask: \"Report on every tool found, or only some?\"\n    default: \"all modules detected\"\n\n  - id: D4\n    param: exportData\n    kind: optional_conditional\n    source: upstream\n    ask: \"Should the parsed metrics be written as flat tables for a later programmatic QC gate?\"\n    default: \"HTML report only\"\n```\n\nMultiQC aggregates rather than analyses, so most of its parameters are\npresentation. The two that are not are D1 and D2: a stale directory in scope\nsilently adds samples that were never part of this run, and wrong name cleaning\nreports one sample as several. Both produce a clean-looking report.\n\n## Workflow\n\n### Step 1: Generate Tool-Specific QC Files\n\nMultiQC aggregates existing output — first run your QC tools.\n\n```bash\n# FastQC on all FASTQ files\nmkdir -p qc\u002Ffastqc\nfastqc data\u002F*.fastq.gz -o qc\u002Ffastqc\u002F -t 8\n\n# samtools flagstat on all BAM files\nfor bam in results\u002F*.bam; do\n    samtools flagstat $bam > qc\u002F$(basename $bam .bam).flagstat\ndone\necho \"QC files generated: $(ls qc\u002F | wc -l)\"\n```\n\n### Step 2: Run MultiQC on a Directory\n\nMultiQC recursively scans for recognized QC files.\n\n```bash\n# Basic run: scan current directory recursively\nmultiqc .\n\n# Specify output directory and report name\nmultiqc . -o reports\u002F -n project_qc_report\n\n# Scan specific subdirectories only\nmultiqc qc\u002Ffastqc\u002F results\u002Fstar\u002F logs\u002Ftrimming\u002F -o reports\u002F\n\n# Output: reports\u002Fproject_qc_report.html\necho \"Report: reports\u002Fproject_qc_report.html\"\n```\n\n### Step 3: Configure Report Behavior\n\nUse `multiqc_config.yaml` to set custom thresholds, sample naming, and module order.\n\n```yaml\n# multiqc_config.yaml — place in working directory\ntitle: \"RNA-seq QC Report — Project X\"\nsubtitle: \"Analysis date: 2026-02\"\nintro_text: \"Quality control summary for all 48 samples.\"\n\n# Sample name cleaning: remove path prefixes and suffixes\nfn_clean_exts:\n  - \".fastq.gz\"\n  - \"_R1\"\n  - \".sorted\"\n\n# Thresholds for pass\u002Fwarn\u002Ffail coloring\ngeneral_stats_addcols:\n  FastQC:\n    pct_duplication:\n      max: 40\n      warn: 30\n\n# Module run order\nmodule_order:\n  - fastqc\n  - trimgalore\n  - star\n  - featurecounts\n  - samtools\n```\n\n```bash\n# Run with config file\nmultiqc . --config multiqc_config.yaml -o reports\u002F\n```\n\n### Step 4: Use MultiQC Modules and Filters\n\nControl which tools and samples are included.\n\n```bash\n# Run only specific modules\nmultiqc . --module fastqc --module samtools\n\n# Exclude specific modules\nmultiqc . --exclude fastqc\n\n# Include only files matching a pattern\nmultiqc . --filename \"*.flagstat\" --filename \"*_fastqc.zip\"\n\n# Ignore specific directories or files\nmultiqc . --ignore \"tmp\u002F\" --ignore \"*.bam\"\n\n# Add sample name regex substitution\nmultiqc . --replace-names \"sample_\" \"\"\n```\n\n### Step 5: Export Data for Downstream Analysis\n\nExtract machine-readable statistics from the MultiQC report.\n\n```bash\n# Export data tables (CSV, JSON, YAML, TSV)\nmultiqc . -o reports\u002F --data-format json\n# Generates: reports\u002Fmultiqc_data\u002Fmultiqc_data.json\n\n# Export flat CSV tables per tool\nmultiqc . -o reports\u002F --export\nls reports\u002Fmultiqc_data\u002F\n# multiqc_fastqc.txt, multiqc_samtools_stats.txt, ...\n\n# Extract general stats as pandas DataFrame\npython3 - \u003C\u003C 'EOF'\nimport json\nimport pandas as pd\nwith open(\"reports\u002Fmultiqc_data\u002Fmultiqc_general_stats.json\") as f:\n    data = json.load(f)\ndf = pd.DataFrame(data).T\nprint(df.head())\nprint(f\"Shape: {df.shape}\")\nEOF\n```\n\n### Step 6: Automate in Pipeline Scripts\n\nIntegrate MultiQC as the final step of any QC pipeline.\n\n```bash\n#!\u002Fbin\u002Fbash\n# Complete RNA-seq QC pipeline → MultiQC summary\nSAMPLES=(ctrl_rep1 ctrl_rep2 treat_rep1 treat_rep2)\nOUTDIR=\"pipeline_output\"\nmkdir -p $OUTDIR\u002F{fastqc,star,featurecounts,flagstat}\n\nfor sample in \"${SAMPLES[@]}\"; do\n    # FastQC\n    fastqc data\u002F${sample}.fastq.gz -o $OUTDIR\u002Ffastqc\u002F -t 4\n    # STAR alignment\n    STAR --runThreadN 8 --genomeDir refs\u002Fstar_index \\\n         --readFilesIn data\u002F${sample}.fastq.gz \\\n         --outSAMtype BAM SortedByCoordinate \\\n         --outFileNamePrefix $OUTDIR\u002Fstar\u002F${sample}\u002F\n    # samtools flagstat\n    samtools flagstat $OUTDIR\u002Fstar\u002F${sample}\u002FAligned.sortedByCoord.out.bam \\\n        > $OUTDIR\u002Fflagstat\u002F${sample}.flagstat\ndone\n\n# Final MultiQC report\nmultiqc $OUTDIR\u002F -o $OUTDIR\u002Fqc_report\u002F -n \"full_pipeline_qc\"\necho \"Report ready: $OUTDIR\u002Fqc_report\u002Ffull_pipeline_qc.html\"\n```\n\n## Key Parameters\n\n| Parameter | Default | Range\u002FOptions | Effect |\n|-----------|---------|---------------|--------|\n| `-o, --outdir` | `.` | directory path | Output directory for report and data |\n| `-n, --filename` | `multiqc_report` | any string | Report filename (without extension) |\n| `-m, --module` | all | tool name | Run only specified module(s) |\n| `--ignore` | — | glob pattern | Ignore matching files or directories |\n| `--export` | False | flag | Export flat tab-delimited data files |\n| `--data-format` | `tsv` | `tsv`, `json`, `yaml` | Format for exported data files |\n| `--config` | auto-detected | YAML file path | Custom config file with thresholds and naming |\n| `--replace-names` | — | regex, replacement | Clean sample names in report |\n| `--fn_clean_exts` | (built-in) | list in config | File extensions to strip from sample names |\n| `--profile-runtime` | False | flag | Show per-module runtime profiling |\n\n## Common Recipes\n\n### Recipe: Add MultiQC to a Snakemake Pipeline\n\n```python\n# In Snakefile: collect all QC outputs, then run MultiQC\nrule multiqc:\n    input:\n        expand(\"qc\u002Ffastqc\u002F{sample}_fastqc.zip\", sample=SAMPLES),\n        expand(\"qc\u002Fflagstat\u002F{sample}.flagstat\", sample=SAMPLES)\n    output:\n        html=\"reports\u002Fmultiqc_report.html\",\n        data=directory(\"reports\u002Fmultiqc_data\")\n    shell:\n        \"multiqc qc\u002F -o reports\u002F -n multiqc_report\"\n```\n\n### Recipe: Parse MultiQC Output in Python\n\n```python\nimport json\nimport pandas as pd\n\n# Load general stats from JSON export\nwith open(\"reports\u002Fmultiqc_data\u002Fmultiqc_general_stats.json\") as f:\n    stats = json.load(f)\n\ndf = pd.DataFrame(stats).T\nprint(f\"Samples: {len(df)}\")\nprint(f\"Metrics: {list(df.columns[:5])}\")\n\n# Flag samples with low mapping rate\nif \"STAR_mqc-generalstats-star-uniquely_mapped_percent\" in df.columns:\n    low_mapping = df[df[\"STAR_mqc-generalstats-star-uniquely_mapped_percent\"] \u003C 70]\n    print(f\"Samples with \u003C70% mapping: {list(low_mapping.index)}\")\n```\n\n### Recipe: Compare QC Before and After Trimming\n\n```bash\n# Run FastQC on raw and trimmed reads, then combine in one report\nmkdir -p qc\u002F{raw,trimmed}\n\nfastqc data\u002F*.fastq.gz -o qc\u002Fraw\u002F -t 8\ntrim_galore data\u002F*.fastq.gz --paired -o trimmed\u002F\nfastqc trimmed\u002F*_trimmed.fastq.gz -o qc\u002Ftrimmed\u002F -t 8\n\nmultiqc qc\u002Fraw\u002F qc\u002Ftrimmed\u002F \\\n    -o reports\u002F -n raw_vs_trimmed \\\n    --dirs --dirs-depth 1  # use directory names in sample labels\n```\n\n## Expected Outputs\n\n| Output | Format | Description |\n|--------|--------|-------------|\n| `multiqc_report.html` | HTML | Interactive report with all plots and tables |\n| `multiqc_data\u002Fmultiqc_general_stats.txt` | TSV | Per-sample summary statistics (all tools) |\n| `multiqc_data\u002Fmultiqc_*.txt` | TSV | Per-tool detailed statistics tables |\n| `multiqc_data\u002Fmultiqc_data.json` | JSON | Full data (if `--data-format json`) |\n| `multiqc_data\u002Fmultiqc_sources.txt` | TSV | Mapping of source files to samples |\n\n## Troubleshooting\n\n| Problem | Cause | Solution |\n|---------|-------|----------|\n| Empty report (no modules found) | QC files not in scanned directories | Specify directories explicitly: `multiqc qc\u002F logs\u002F results\u002F` |\n| Wrong sample names in report | File extensions or paths not cleaned | Add `fn_clean_exts` to config or use `--replace-names` |\n| Module missing from report | Log file format changed in tool version | Update MultiQC: `pip install --upgrade multiqc`; check GitHub issues |\n| Duplicate sample names | Multiple files map to same sample name | Use `--sample-names` or fix `fn_clean_exts` in config |\n| Report very slow to open | Too many samples (>500) in one report | Split by project or condition; use `--flat` for simpler rendering |\n| FastQC data not parsed | FastQC ZIP not in expected location | Run MultiQC from root of project; ensure `*_fastqc.zip` files exist |\n| `ModuleNotFoundError` | Missing optional module dependencies | `pip install multiqc[all]` for all extras |\n\n## References\n\n- [MultiQC documentation](https:\u002F\u002Fmultiqc.info\u002Fdocs\u002F) — official user guide and module list\n- [GitHub: ewels\u002FMultiQC](https:\u002F\u002Fgithub.com\u002Fewels\u002FMultiQC) — source code and community modules\n- Ewels et al. (2016) \"MultiQC: summarize analysis results for multiple tools and samples in a single report\" — [Bioinformatics 32(19)](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtw354)\n- [MultiQC supported tools](https:\u002F\u002Fmultiqc.info\u002Fmodules\u002F) — full list of 150+ supported modules\n","# MultiQC — Multi-Sample QC Report Aggregator\n\n## Overview\n\nMultiQC automatically searches directories for QC log files from 150+ bioinformatics tools and aggregates statistics across all samples into a single interactive HTML report. It parses outputs from FastQC, samtools flagstat, STAR, HISAT2, Trim Galore, Salmon, Kallisto, featureCounts, Picard, GATK, and many more — eliminating the need to manually review per-sample QC files. Reports include interactive bar plots, scatter plots, heatmaps, and tables with configurable warnings and pass\u002Ffail thresholds.\n\n## When to Use\n\n- Reviewing QC metrics across 10+ samples at once after FastQC, alignment, or quantification\n- Final QC checkpoint before differential expression or variant analysis\n- Sharing QC summaries with collaborators or including in publications\n- Identifying batch effects, outlier samples, or failed sequencing runs\n- Combining QC from multi-step pipelines (trimming → alignment → quantification) into one view\n- Use FastQC directly instead for initial single-sample QC exploration\n- For custom QC metrics not from standard tools, use Python\u002FR directly; MultiQC parses tool outputs only\n\n## Prerequisites\n\n- **Python packages**: `multiqc`\n- **Input requirements**: Output files from bioinformatics tools (FastQC `.zip`, samtools `.flagstat`, STAR `Log.final.out`, etc.) — MultiQC finds them automatically\n- **Environment**: Python 3.8+\n\n> **Check before installing**: The tool may already be available in the current environment (e.g., inside a `pixi` \u002F `conda` env). Run `command -v multiqc` first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via `pixi run multiqc` rather than bare `multiqc`.\n\n```bash\npip install multiqc\n\n# Verify\nmultiqc --version\n# MultiQC v1.25.0\n\n# With conda (recommended for bioinformatics)\nconda install -c bioconda multiqc\n```\n\n## Pre-flight Interview\n\nSettle these with the user before writing any analysis code.\n\n```yaml\ndecisions:\n  - id: D1\n    param: searchScope\n    kind: required\n    source: data\n    ask: \"Which directories should be scanned, and are there old or partial runs in them that must be excluded?\"\n    default: \"the pipeline output root, with no exclusions\"\n\n  - id: D2\n    param: sampleNameCleaning\n    kind: required\n    source: data\n    ask: \"Do the discovered sample names collapse correctly, or do suffixes from different tools split one sample into several rows?\"\n    default: \"built-in extension stripping; verify the resulting sample list\"\n\n  - id: D3\n    param: moduleFilter\n    kind: optional\n    source: user\n    ask: \"Report on every tool found, or only some?\"\n    default: \"all modules detected\"\n\n  - id: D4\n    param: exportData\n    kind: optional_conditional\n    source: upstream\n    ask: \"Should the parsed metrics be written as flat tables for a later programmatic QC gate?\"\n    default: \"HTML report only\"\n```\n\nMultiQC aggregates rather than analyses, so most of its parameters are\npresentation. The two that are not are D1 and D2: a stale directory in scope\nsilently adds samples that were never part of this run, and wrong name cleaning\nreports one sample as several. Both produce a clean-looking report.\n\n## Workflow\n\n### Step 1: Generate Tool-Specific QC Files\n\nMultiQC aggregates existing output — first run your QC tools.\n\n```bash\n# FastQC on all FASTQ files\nmkdir -p qc\u002Ffastqc\nfastqc data\u002F*.fastq.gz -o qc\u002Ffastqc\u002F -t 8\n\n# samtools flagstat on all BAM files\nfor bam in results\u002F*.bam; do\n    samtools flagstat $bam > qc\u002F$(basename $bam .bam).flagstat\ndone\necho \"QC files generated: $(ls qc\u002F | wc -l)\"\n```\n\n### Step 2: Run MultiQC on a Directory\n\nMultiQC recursively scans for recognized QC files.\n\n```bash\n# Basic run: scan current directory recursively\nmultiqc .\n\n# Specify output directory and report name\nmultiqc . -o reports\u002F -n project_qc_report\n\n# Scan specific subdirectories only\nmultiqc qc\u002Ffastqc\u002F results\u002Fstar\u002F logs\u002Ftrimming\u002F -o reports\u002F\n\n# Output: reports\u002Fproject_qc_report.html\necho \"Report: reports\u002Fproject_qc_report.html\"\n```\n\n### Step 3: Configure Report Behavior\n\nUse `multiqc_config.yaml` to set custom thresholds, sample naming, and module order.\n\n```yaml\n# multiqc_config.yaml — place in working directory\ntitle: \"RNA-seq QC Report — Project X\"\nsubtitle: \"Analysis date: 2026-02\"\nintro_text: \"Quality control summary for all 48 samples.\"\n\n# Sample name cleaning: remove path prefixes and suffixes\nfn_clean_exts:\n  - \".fastq.gz\"\n  - \"_R1\"\n  - \".sorted\"\n\n# Thresholds for pass\u002Fwarn\u002Ffail coloring\ngeneral_stats_addcols:\n  FastQC:\n    pct_duplication:\n      max: 40\n      warn: 30\n\n# Module run order\nmodule_order:\n  - fastqc\n  - trimgalore\n  - star\n  - featurecounts\n  - samtools\n```\n\n```bash\n# Run with config file\nmultiqc . --config multiqc_config.yaml -o reports\u002F\n```\n\n### Step 4: Use MultiQC Modules and Filters\n\nControl which tools and samples are included.\n\n```bash\n# Run only specific modules\nmultiqc . --module fastqc --module samtools\n\n# Exclude specific modules\nmultiqc . --exclude fastqc\n\n# Include only files matching a pattern\nmultiqc . --filename \"*.flagstat\" --filename \"*_fastqc.zip\"\n\n# Ignore specific directories or files\nmultiqc . --ignore \"tmp\u002F\" --ignore \"*.bam\"\n\n# Add sample name regex substitution\nmultiqc . --replace-names \"sample_\" \"\"\n```\n\n### Step 5: Export Data for Downstream Analysis\n\nExtract machine-readable statistics from the MultiQC report.\n\n```bash\n# Export data tables (CSV, JSON, YAML, TSV)\nmultiqc . -o reports\u002F --data-format json\n# Generates: reports\u002Fmultiqc_data\u002Fmultiqc_data.json\n\n# Export flat CSV tables per tool\nmultiqc . -o reports\u002F --export\nls reports\u002Fmultiqc_data\u002F\n# multiqc_fastqc.txt, multiqc_samtools_stats.txt, ...\n\n# Extract general stats as pandas DataFrame\npython3 - \u003C\u003C 'EOF'\nimport json\nimport pandas as pd\nwith open(\"reports\u002Fmultiqc_data\u002Fmultiqc_general_stats.json\") as f:\n    data = json.load(f)\ndf = pd.DataFrame(data).T\nprint(df.head())\nprint(f\"Shape: {df.shape}\")\nEOF\n```\n\n### Step 6: Automate in Pipeline Scripts\n\nIntegrate MultiQC as the final step of any QC pipeline.\n\n```bash\n#!\u002Fbin\u002Fbash\n# Complete RNA-seq QC pipeline → MultiQC summary\nSAMPLES=(ctrl_rep1 ctrl_rep2 treat_rep1 treat_rep2)\nOUTDIR=\"pipeline_output\"\nmkdir -p $OUTDIR\u002F{fastqc,star,featurecounts,flagstat}\n\nfor sample in \"${SAMPLES[@]}\"; do\n    # FastQC\n    fastqc data\u002F${sample}.fastq.gz -o $OUTDIR\u002Ffastqc\u002F -t 4\n    # STAR alignment\n    STAR --runThreadN 8 --genomeDir refs\u002Fstar_index \\\n         --readFilesIn data\u002F${sample}.fastq.gz \\\n         --outSAMtype BAM SortedByCoordinate \\\n         --outFileNamePrefix $OUTDIR\u002Fstar\u002F${sample}\u002F\n    # samtools flagstat\n    samtools flagstat $OUTDIR\u002Fstar\u002F${sample}\u002FAligned.sortedByCoord.out.bam \\\n        > $OUTDIR\u002Fflagstat\u002F${sample}.flagstat\ndone\n\n# Final MultiQC report\nmultiqc $OUTDIR\u002F -o $OUTDIR\u002Fqc_report\u002F -n \"full_pipeline_qc\"\necho \"Report ready: $OUTDIR\u002Fqc_report\u002Ffull_pipeline_qc.html\"\n```\n\n## Key Parameters\n\n| Parameter | Default | Range\u002FOptions | Effect |\n|-----------|---------|---------------|--------|\n| `-o, --outdir` | `.` | directory path | Output directory for report and data |\n| `-n, --filename` | `multiqc_report` | any string | Report filename (without extension) |\n| `-m, --module` | all | tool name | Run only specified module(s) |\n| `--ignore` | — | glob pattern | Ignore matching files or directories |\n| `--export` | False | flag | Export flat tab-delimited data files |\n| `--data-format` | `tsv` | `tsv`, `json`, `yaml` | Format for exported data files |\n| `--config` | auto-detected | YAML file path | Custom config file with thresholds and naming |\n| `--replace-names` | — | regex, replacement | Clean sample names in report |\n| `--fn_clean_exts` | (built-in) | list in config | File extensions to strip from sample names |\n| `--profile-runtime` | False | flag | Show per-module runtime profiling |\n\n## Common Recipes\n\n### Recipe: Add MultiQC to a Snakemake Pipeline\n\n```python\n# In Snakefile: collect all QC outputs, then run MultiQC\nrule multiqc:\n    input:\n        expand(\"qc\u002Ffastqc\u002F{sample}_fastqc.zip\", sample=SAMPLES),\n        expand(\"qc\u002Fflagstat\u002F{sample}.flagstat\", sample=SAMPLES)\n    output:\n        html=\"reports\u002Fmultiqc_report.html\",\n        data=directory(\"reports\u002Fmultiqc_data\")\n    shell:\n        \"multiqc qc\u002F -o reports\u002F -n multiqc_report\"\n```\n\n### Recipe: Parse MultiQC Output in Python\n\n```python\nimport json\nimport pandas as pd\n\n# Load general stats from JSON export\nwith open(\"reports\u002Fmultiqc_data\u002Fmultiqc_general_stats.json\") as f:\n    stats = json.load(f)\n\ndf = pd.DataFrame(stats).T\nprint(f\"Samples: {len(df)}\")\nprint(f\"Metrics: {list(df.columns[:5])}\")\n\n# Flag samples with low mapping rate\nif \"STAR_mqc-generalstats-star-uniquely_mapped_percent\" in df.columns:\n    low_mapping = df[df[\"STAR_mqc-generalstats-star-uniquely_mapped_percent\"] \u003C 70]\n    print(f\"Samples with \u003C70% mapping: {list(low_mapping.index)}\")\n```\n\n### Recipe: Compare QC Before and After Trimming\n\n```bash\n# Run FastQC on raw and trimmed reads, then combine in one report\nmkdir -p qc\u002F{raw,trimmed}\n\nfastqc data\u002F*.fastq.gz -o qc\u002Fraw\u002F -t 8\ntrim_galore data\u002F*.fastq.gz --paired -o trimmed\u002F\nfastqc trimmed\u002F*_trimmed.fastq.gz -o qc\u002Ftrimmed\u002F -t 8\n\nmultiqc qc\u002Fraw\u002F qc\u002Ftrimmed\u002F \\\n    -o reports\u002F -n raw_vs_trimmed \\\n    --dirs --dirs-depth 1  # use directory names in sample labels\n```\n\n## Expected Outputs\n\n| Output | Format | Description |\n|--------|--------|-------------|\n| `multiqc_report.html` | HTML | Interactive report with all plots and tables |\n| `multiqc_data\u002Fmultiqc_general_stats.txt` | TSV | Per-sample summary statistics (all tools) |\n| `multiqc_data\u002Fmultiqc_*.txt` | TSV | Per-tool detailed statistics tables |\n| `multiqc_data\u002Fmultiqc_data.json` | JSON | Full data (if `--data-format json`) |\n| `multiqc_data\u002Fmultiqc_sources.txt` | TSV | Mapping of source files to samples |\n\n## Troubleshooting\n\n| Problem | Cause | Solution |\n|---------|-------|----------|\n| Empty report (no modules found) | QC files not in scanned directories | Specify directories explicitly: `multiqc qc\u002F logs\u002F results\u002F` |\n| Wrong sample names in report | File extensions or paths not cleaned | Add `fn_clean_exts` to config or use `--replace-names` |\n| Module missing from report | Log file format changed in tool version | Update MultiQC: `pip install --upgrade multiqc`; check GitHub issues |\n| Duplicate sample names | Multiple files map to same sample name | Use `--sample-names` or fix `fn_clean_exts` in config |\n| Report very slow to open | Too many samples (>500) in one report | Split by project or condition; use `--flat` for simpler rendering |\n| FastQC data not parsed | FastQC ZIP not in expected location | Run MultiQC from root of project; ensure `*_fastqc.zip` files exist |\n| `ModuleNotFoundError` | Missing optional module dependencies | `pip install multiqc[all]` for all extras |\n\n## References\n\n- [MultiQC documentation](https:\u002F\u002Fmultiqc.info\u002Fdocs\u002F) — official user guide and module list\n- [GitHub: ewels\u002FMultiQC](https:\u002F\u002Fgithub.com\u002Fewels\u002FMultiQC) — source code and community modules\n- Ewels et al. (2016) \"MultiQC: summarize analysis results for multiple tools and samples in a single report\" — [Bioinformatics 32(19)](https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtw354)\n- [MultiQC supported tools](https:\u002F\u002Fmultiqc.info\u002Fmodules\u002F) — full list of 150+ supported modules\n",{"name":5,"license":15,"description":7},"master","fd76f882a04e445e899d5cfb2732f2105ea054ee","skills\u002Fawesome-skills\u002Fmultiqc-qc-reports",0,"https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS","https:\u002F\u002Fbg-szy.github.io\u002FTOP-SKILLS\u002F","https:\u002F\u002Fgithub.com\u002Fbg-szy","https:\u002F\u002Fwww.ecust.edu.cn\u002F","a student of East China University of Science and Technology","2026-11-12 05:55:03",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add bg-szy\u002FTOP-SKILLS --skill multiqc-qc-reports",{"tool":40,"command":41},"bunx","bunx skills add bg-szy\u002FTOP-SKILLS --skill multiqc-qc-reports",{"tool":43,"command":44},"pnpm","pnpm dlx skills add bg-szy\u002FTOP-SKILLS --skill multiqc-qc-reports","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fawesome-skills\u002Fmultiqc-qc-reports\u002FSKILL.md",true,[],[49,57,64,73,81,89],{"skillNo":50,"name":51,"nameZh":6,"description":52,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":53,"licenseFileUrl":6,"sourceHtmlUrl":54,"categories":55,"keywords":56,"publishedAt":19},"01M748C5DS5BQBVBMG22FCGG34","branding","When the user wants to define, audit, or apply brand strategy—purpose, values, positioning, storytelling, voice, narrative (not only visuals). Also use when the user mentions \"brand strategy\", \"brand story\", \"brand storytelling\" or \"brand voice\". For typography, colors, design tokens, and frontend visuals, use brand-visual-generator.","MIT","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fsuperskills\u002Fbranding\u002FSKILL.md",[],[],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":53,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":63,"publishedAt":19},"01M748C1GXJ9WRKP18X45DKP6G","brand-visual-generator","When the user wants to define, audit, or apply visual identity (typography, colors, spacing, design tokens, frontend aesthetics). Also use when the user mentions \"brand style guide\", \"visual identity\", \"design system\" or \"typography\". For brand story, positioning, and voice, use branding.","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fsuperskills\u002Fbrand-visual-generator\u002FSKILL.md",[],[],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":68,"licenseFileUrl":69,"sourceHtmlUrl":70,"categories":71,"keywords":72,"publishedAt":19},"01M747T565TFEP3RAVWM0Y899Y","mcp-builder","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node\u002FTypeScript (MCP SDK).","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fmcp-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fmcp-builder\u002FSKILL.md",[],[],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":68,"licenseFileUrl":77,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M747RAJGMBZ2QN4563STB5SH","frontend-design","Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Ffrontend-design\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":68,"licenseFileUrl":85,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":19},"01M747NHAY5BE9GXEB31H255R1","claude-api","Reference for the Claude API \u002F Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude\u002FAnthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing\u002Fmodel choice\u002Flimits\u002Fcaching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent\u002FMCP\u002Ftool-definition\u002Fmulti-agent\u002FRAG\u002FLLM-judge\u002Fcomputer-use; generate\u002Fsummarize\u002Fextract\u002Fclassify\u002Frewrite\u002Fconverse over NL; debugging refusals\u002Fcutoffs\u002Fstreaming\u002Ftool-calls\u002Ftokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI\u002FGPT\u002FGemini\u002FLlama\u002FMistral\u002FCohere\u002FOllama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fclaude-api\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fclaude-api\u002FSKILL.md",[],[],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":53,"licenseFileUrl":93,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":19},"01M73WM4TPHEJ57D7G2AZ0ABVT","inbox-guardian-for-gmail","Review a personal Gmail inbox with local, owner-approved spam rules. Use for audit-first quarantine, Trash, repeated-evidence sender learning, and header review. Do not use for automatic unsubscribe requests or unreviewed mailbox actions.","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fmarketplace\u002Finbox-guardian-for-gmail\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbg-szy\u002FTOP-SKILLS\u002Fblob\u002Fmaster\u002Fskills\u002Fmarketplace\u002Finbox-guardian-for-gmail\u002FSKILL.md",[],[]]