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BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps\u002Fprofiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fdeeptools\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"PCA","rna-seq","peaks","chip-seq","atac-seq","bam","qc","ngs analysis","NGS","toolkit","atac","RNA","conversion","analysis","correlation","visualization","2026-11-10 10:12:30",false,"---\nname: deeptools\ndescription: \"NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps\u002Fprofiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.\"\n---\n\n# deepTools: NGS Data Analysis Toolkit\n\n## Overview\n\ndeepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments.\n\n**Core capabilities:**\n- Convert BAM alignments to normalized coverage tracks (bigWig\u002FbedGraph)\n- Quality control assessment (fingerprint, correlation, coverage)\n- Sample comparison and correlation analysis\n- Heatmap and profile plot generation around genomic features\n- Enrichment analysis and peak region visualization\n\n## When to Use This Skill\n\nThis skill should be used when:\n\n- **File conversion**: \"Convert BAM to bigWig\", \"generate coverage tracks\", \"normalize ChIP-seq data\"\n- **Quality control**: \"check ChIP quality\", \"compare replicates\", \"assess sequencing depth\", \"QC analysis\"\n- **Visualization**: \"create heatmap around TSS\", \"plot ChIP signal\", \"visualize enrichment\", \"generate profile plot\"\n- **Sample comparison**: \"compare treatment vs control\", \"correlate samples\", \"PCA analysis\"\n- **Analysis workflows**: \"analyze ChIP-seq data\", \"RNA-seq coverage\", \"ATAC-seq analysis\", \"complete workflow\"\n- **Working with specific file types**: BAM files, bigWig files, BED region files in genomics context\n\n## Quick Start\n\nFor users new to deepTools, start with file validation and common workflows:\n\n### 1. Validate Input Files\n\nBefore running any analysis, validate BAM, bigWig, and BED files using the validation script:\n\n```bash\npython scripts\u002Fvalidate_files.py --bam sample1.bam sample2.bam --bed regions.bed\n```\n\nThis checks file existence, BAM indices, and format correctness.\n\n### 2. Generate Workflow Template\n\nFor standard analyses, use the workflow generator to create customized scripts:\n\n```bash\n# List available workflows\npython scripts\u002Fworkflow_generator.py --list\n\n# Generate ChIP-seq QC workflow\npython scripts\u002Fworkflow_generator.py chipseq_qc -o qc_workflow.sh \\\n    --input-bam Input.bam --chip-bams \"ChIP1.bam ChIP2.bam\" \\\n    --genome-size 2913022398\n\n# Make executable and run\nchmod +x qc_workflow.sh\n.\u002Fqc_workflow.sh\n```\n\n### 3. Most Common Operations\n\nSee `assets\u002Fquick_reference.md` for frequently used commands and parameters.\n\n## Installation\n\n```bash\nuv pip install deeptools\n```\n\n## Core Workflows\n\ndeepTools workflows typically follow this pattern: **QC → Normalization → Comparison\u002FVisualization**\n\n### ChIP-seq Quality Control Workflow\n\nWhen users request ChIP-seq QC or quality assessment:\n\n1. **Generate workflow script** using `scripts\u002Fworkflow_generator.py chipseq_qc`\n2. **Key QC steps**:\n   - Sample correlation (multiBamSummary + plotCorrelation)\n   - PCA analysis (plotPCA)\n   - Coverage assessment (plotCoverage)\n   - Fragment size validation (bamPEFragmentSize)\n   - ChIP enrichment strength (plotFingerprint)\n\n**Interpreting results:**\n- **Correlation**: Replicates should cluster together with high correlation (>0.9)\n- **Fingerprint**: Strong ChIP shows steep rise; flat diagonal indicates poor enrichment\n- **Coverage**: Assess if sequencing depth is adequate for analysis\n\nFull workflow details in `references\u002Fworkflows.md` → \"ChIP-seq Quality Control Workflow\"\n\n### ChIP-seq Complete Analysis Workflow\n\nFor full ChIP-seq analysis from BAM to visualizations:\n\n1. **Generate coverage tracks** with normalization (bamCoverage)\n2. **Create comparison tracks** (bamCompare for log2 ratio)\n3. **Compute signal matrices** around features (computeMatrix)\n4. **Generate visualizations** (plotHeatmap, plotProfile)\n5. **Enrichment analysis** at peaks (plotEnrichment)\n\nUse `scripts\u002Fworkflow_generator.py chipseq_analysis` to generate template.\n\nComplete command sequences in `references\u002Fworkflows.md` → \"ChIP-seq Analysis Workflow\"\n\n### RNA-seq Coverage Workflow\n\nFor strand-specific RNA-seq coverage tracks:\n\nUse bamCoverage with `--filterRNAstrand` to separate forward and reverse strands.\n\n**Important:** NEVER use `--extendReads` for RNA-seq (would extend over splice junctions).\n\nUse normalization: CPM for fixed bins, RPKM for gene-level analysis.\n\nTemplate available: `scripts\u002Fworkflow_generator.py rnaseq_coverage`\n\nDetails in `references\u002Fworkflows.md` → \"RNA-seq Coverage Workflow\"\n\n### ATAC-seq Analysis Workflow\n\nATAC-seq requires Tn5 offset correction:\n\n1. **Shift reads** using alignmentSieve with `--ATACshift`\n2. **Generate coverage** with bamCoverage\n3. **Analyze fragment sizes** (expect nucleosome ladder pattern)\n4. **Visualize at peaks** if available\n\nTemplate: `scripts\u002Fworkflow_generator.py atacseq`\n\nFull workflow in `references\u002Fworkflows.md` → \"ATAC-seq Workflow\"\n\n## Tool Categories and Common Tasks\n\n### BAM\u002FbigWig Processing\n\n**Convert BAM to normalized coverage:**\n```bash\nbamCoverage --bam input.bam --outFileName output.bw \\\n    --normalizeUsing RPGC --effectiveGenomeSize 2913022398 \\\n    --binSize 10 --numberOfProcessors 8\n```\n\n**Compare two samples (log2 ratio):**\n```bash\nbamCompare -b1 treatment.bam -b2 control.bam -o ratio.bw \\\n    --operation log2 --scaleFactorsMethod readCount\n```\n\n**Key tools:** bamCoverage, bamCompare, multiBamSummary, multiBigwigSummary, correctGCBias, alignmentSieve\n\nComplete reference: `references\u002Ftools_reference.md` → \"BAM and bigWig File Processing Tools\"\n\n### Quality Control\n\n**Check ChIP enrichment:**\n```bash\nplotFingerprint -b input.bam chip.bam -o fingerprint.png \\\n    --extendReads 200 --ignoreDuplicates\n```\n\n**Sample correlation:**\n```bash\nmultiBamSummary bins --bamfiles *.bam -o counts.npz\nplotCorrelation -in counts.npz --corMethod pearson \\\n    --whatToShow heatmap -o correlation.png\n```\n\n**Key tools:** plotFingerprint, plotCoverage, plotCorrelation, plotPCA, bamPEFragmentSize\n\nComplete reference: `references\u002Ftools_reference.md` → \"Quality Control Tools\"\n\n### Visualization\n\n**Create heatmap around TSS:**\n```bash\n# Compute matrix\ncomputeMatrix reference-point -S signal.bw -R genes.bed \\\n    -b 3000 -a 3000 --referencePoint TSS -o matrix.gz\n\n# Generate heatmap\nplotHeatmap -m matrix.gz -o heatmap.png \\\n    --colorMap RdBu --kmeans 3\n```\n\n**Create profile plot:**\n```bash\nplotProfile -m matrix.gz -o profile.png \\\n    --plotType lines --colors blue red\n```\n\n**Key tools:** computeMatrix, plotHeatmap, plotProfile, plotEnrichment\n\nComplete reference: `references\u002Ftools_reference.md` → \"Visualization Tools\"\n\n## Normalization Methods\n\nChoosing the correct normalization is critical for valid comparisons. Consult `references\u002Fnormalization_methods.md` for comprehensive guidance.\n\n**Quick selection guide:**\n\n- **ChIP-seq coverage**: Use RPGC or CPM\n- **ChIP-seq comparison**: Use bamCompare with log2 and readCount\n- **RNA-seq bins**: Use CPM\n- **RNA-seq genes**: Use RPKM (accounts for gene length)\n- **ATAC-seq**: Use RPGC or CPM\n\n**Normalization methods:**\n- **RPGC**: 1× genome coverage (requires --effectiveGenomeSize)\n- **CPM**: Counts per million mapped reads\n- **RPKM**: Reads per kb per million (accounts for region length)\n- **BPM**: Bins per million\n- **None**: Raw counts (not recommended for comparisons)\n\nFull explanation: `references\u002Fnormalization_methods.md`\n\n## Effective Genome Sizes\n\nRPGC normalization requires effective genome size. Common values:\n\n| Organism | Assembly | Size | Usage |\n|----------|----------|------|-------|\n| Human | GRCh38\u002Fhg38 | 2,913,022,398 | `--effectiveGenomeSize 2913022398` |\n| Mouse | GRCm38\u002Fmm10 | 2,652,783,500 | `--effectiveGenomeSize 2652783500` |\n| Zebrafish | GRCz11 | 1,368,780,147 | `--effectiveGenomeSize 1368780147` |\n| *Drosophila* | dm6 | 142,573,017 | `--effectiveGenomeSize 142573017` |\n| *C. elegans* | ce10\u002Fce11 | 100,286,401 | `--effectiveGenomeSize 100286401` |\n\nComplete table with read-length-specific values: `references\u002Feffective_genome_sizes.md`\n\n## Common Parameters Across Tools\n\nMany deepTools commands share these options:\n\n**Performance:**\n- `--numberOfProcessors, -p`: Enable parallel processing (always use available cores)\n- `--region`: Process specific regions for testing (e.g., `chr1:1-1000000`)\n\n**Read Filtering:**\n- `--ignoreDuplicates`: Remove PCR duplicates (recommended for most analyses)\n- `--minMappingQuality`: Filter by alignment quality (e.g., `--minMappingQuality 10`)\n- `--minFragmentLength` \u002F `--maxFragmentLength`: Fragment length bounds\n- `--samFlagInclude` \u002F `--samFlagExclude`: SAM flag filtering\n\n**Read Processing:**\n- `--extendReads`: Extend to fragment length (ChIP-seq: YES, RNA-seq: NO)\n- `--centerReads`: Center at fragment midpoint for sharper signals\n\n## Best Practices\n\n### File Validation\n**Always validate files first** using `scripts\u002Fvalidate_files.py` to check:\n- File existence and readability\n- BAM indices present (.bai files)\n- BED format correctness\n- File sizes reasonable\n\n### Analysis Strategy\n\n1. **Start with QC**: Run correlation, coverage, and fingerprint analysis before proceeding\n2. **Test on small regions**: Use `--region chr1:1-10000000` for parameter testing\n3. **Document commands**: Save full command lines for reproducibility\n4. **Use consistent normalization**: Apply same method across samples in comparisons\n5. **Verify genome assembly**: Ensure BAM and BED files use matching genome builds\n\n### ChIP-seq Specific\n\n- **Always extend reads** for ChIP-seq: `--extendReads 200`\n- **Remove duplicates**: Use `--ignoreDuplicates` in most cases\n- **Check enrichment first**: Run plotFingerprint before detailed analysis\n- **GC correction**: Only apply if significant bias detected; never use `--ignoreDuplicates` after GC correction\n\n### RNA-seq Specific\n\n- **Never extend reads** for RNA-seq (would span splice junctions)\n- **Strand-specific**: Use `--filterRNAstrand forward\u002Freverse` for stranded libraries\n- **Normalization**: CPM for bins, RPKM for genes\n\n### ATAC-seq Specific\n\n- **Apply Tn5 correction**: Use alignmentSieve with `--ATACshift`\n- **Fragment filtering**: Set appropriate min\u002Fmax fragment lengths\n- **Check nucleosome pattern**: Fragment size plot should show ladder pattern\n\n### Performance Optimization\n\n1. **Use multiple processors**: `--numberOfProcessors 8` (or available cores)\n2. **Increase bin size** for faster processing and smaller files\n3. **Process chromosomes separately** for memory-limited systems\n4. **Pre-filter BAM files** using alignmentSieve to create reusable filtered files\n5. **Use bigWig over bedGraph**: Compressed and faster to process\n\n## Troubleshooting\n\n### Common Issues\n\n**BAM index missing:**\n```bash\nsamtools index input.bam\n```\n\n**Out of memory:**\nProcess chromosomes individually using `--region`:\n```bash\nbamCoverage --bam input.bam -o chr1.bw --region chr1\n```\n\n**Slow processing:**\nIncrease `--numberOfProcessors` and\u002For increase `--binSize`\n\n**bigWig files too large:**\nIncrease bin size: `--binSize 50` or larger\n\n### Validation Errors\n\nRun validation script to identify issues:\n```bash\npython scripts\u002Fvalidate_files.py --bam *.bam --bed regions.bed\n```\n\nCommon errors and solutions explained in script output.\n\n## Reference Documentation\n\nThis skill includes comprehensive reference documentation:\n\n### references\u002Ftools_reference.md\nComplete documentation of all deepTools commands organized by category:\n- BAM and bigWig processing tools (9 tools)\n- Quality control tools (6 tools)\n- Visualization tools (3 tools)\n- Miscellaneous tools (2 tools)\n\nEach tool includes:\n- Purpose and overview\n- Key parameters with explanations\n- Usage examples\n- Important notes and best practices\n\n**Use this reference when:** Users ask about specific tools, parameters, or detailed usage.\n\n### references\u002Fworkflows.md\nComplete workflow examples for common analyses:\n- ChIP-seq quality control workflow\n- ChIP-seq complete analysis workflow\n- RNA-seq coverage workflow\n- ATAC-seq analysis workflow\n- Multi-sample comparison workflow\n- Peak region analysis workflow\n- Troubleshooting and performance tips\n\n**Use this reference when:** Users need complete analysis pipelines or workflow examples.\n\n### references\u002Fnormalization_methods.md\nComprehensive guide to normalization methods:\n- Detailed explanation of each method (RPGC, CPM, RPKM, BPM, etc.)\n- When to use each method\n- Formulas and interpretation\n- Selection guide by experiment type\n- Common pitfalls and solutions\n- Quick reference table\n\n**Use this reference when:** Users ask about normalization, comparing samples, or which method to use.\n\n### references\u002Feffective_genome_sizes.md\nEffective genome size values and usage:\n- Common organism values (human, mouse, fly, worm, zebrafish)\n- Read-length-specific values\n- Calculation methods\n- When and how to use in commands\n- Custom genome calculation instructions\n\n**Use this reference when:** Users need genome size for RPGC normalization or GC bias correction.\n\n## Helper Scripts\n\n### scripts\u002Fvalidate_files.py\n\nValidates BAM, bigWig, and BED files for deepTools analysis. Checks file existence, indices, and format.\n\n**Usage:**\n```bash\npython scripts\u002Fvalidate_files.py --bam sample1.bam sample2.bam \\\n    --bed peaks.bed --bigwig signal.bw\n```\n\n**When to use:** Before starting any analysis, or when troubleshooting errors.\n\n### scripts\u002Fworkflow_generator.py\n\nGenerates customizable bash script templates for common deepTools workflows.\n\n**Available workflows:**\n- `chipseq_qc`: ChIP-seq quality control\n- `chipseq_analysis`: Complete ChIP-seq analysis\n- `rnaseq_coverage`: Strand-specific RNA-seq coverage\n- `atacseq`: ATAC-seq with Tn5 correction\n\n**Usage:**\n```bash\n# List workflows\npython scripts\u002Fworkflow_generator.py --list\n\n# Generate workflow\npython scripts\u002Fworkflow_generator.py chipseq_qc -o qc.sh \\\n    --input-bam Input.bam --chip-bams \"ChIP1.bam ChIP2.bam\" \\\n    --genome-size 2913022398 --threads 8\n\n# Run generated workflow\nchmod +x qc.sh\n.\u002Fqc.sh\n```\n\n**When to use:** Users request standard workflows or need template scripts to customize.\n\n## Assets\n\n### assets\u002Fquick_reference.md\n\nQuick reference card with most common commands, effective genome sizes, and typical workflow pattern.\n\n**When to use:** Users need quick command examples without detailed documentation.\n\n## Handling User Requests\n\n### For New Users\n\n1. Start with installation verification\n2. Validate input files using `scripts\u002Fvalidate_files.py`\n3. Recommend appropriate workflow based on experiment type\n4. Generate workflow template using `scripts\u002Fworkflow_generator.py`\n5. Guide through customization and execution\n\n### For Experienced Users\n\n1. Provide specific tool commands for requested operations\n2. Reference appropriate sections in `references\u002Ftools_reference.md`\n3. Suggest optimizations and best practices\n4. Offer troubleshooting for issues\n\n### For Specific Tasks\n\n**\"Convert BAM to bigWig\":**\n- Use bamCoverage with appropriate normalization\n- Recommend RPGC or CPM based on use case\n- Provide effective genome size for organism\n- Suggest relevant parameters (extendReads, ignoreDuplicates, binSize)\n\n**\"Check ChIP quality\":**\n- Run full QC workflow or use plotFingerprint specifically\n- Explain interpretation of results\n- Suggest follow-up actions based on results\n\n**\"Create heatmap\":**\n- Guide through two-step process: computeMatrix → plotHeatmap\n- Help choose appropriate matrix mode (reference-point vs scale-regions)\n- Suggest visualization parameters and clustering options\n\n**\"Compare samples\":**\n- Recommend bamCompare for two-sample comparison\n- Suggest multiBamSummary + plotCorrelation for multiple samples\n- Guide normalization method selection\n\n### Referencing Documentation\n\nWhen users need detailed information:\n- **Tool details**: Direct to specific sections in `references\u002Ftools_reference.md`\n- **Workflows**: Use `references\u002Fworkflows.md` for complete analysis pipelines\n- **Normalization**: Consult `references\u002Fnormalization_methods.md` for method selection\n- **Genome sizes**: Reference `references\u002Feffective_genome_sizes.md`\n\nSearch references using grep patterns:\n```bash\n# Find tool documentation\ngrep -A 20 \"^### toolname\" references\u002Ftools_reference.md\n\n# Find workflow\ngrep -A 50 \"^## Workflow Name\" references\u002Fworkflows.md\n\n# Find normalization method\ngrep -A 15 \"^### Method Name\" references\u002Fnormalization_methods.md\n```\n\n## Example Interactions\n\n**User: \"I need to analyze my ChIP-seq data\"**\n\nResponse approach:\n1. Ask about files available (BAM files, peaks, genes)\n2. Validate files using validation script\n3. Generate chipseq_analysis workflow template\n4. Customize for their specific files and organism\n5. Explain each step as script runs\n\n**User: \"Which normalization should I use?\"**\n\nResponse approach:\n1. Ask about experiment type (ChIP-seq, RNA-seq, etc.)\n2. Ask about comparison goal (within-sample or between-sample)\n3. Consult `references\u002Fnormalization_methods.md` selection guide\n4. Recommend appropriate method with justification\n5. Provide command example with parameters\n\n**User: \"Create a heatmap around TSS\"**\n\nResponse approach:\n1. Verify bigWig and gene BED files available\n2. Use computeMatrix with reference-point mode at TSS\n3. Generate plotHeatmap with appropriate visualization parameters\n4. Suggest clustering if dataset is large\n5. Offer profile plot as complement\n\n## Key Reminders\n\n- **File validation first**: Always validate input files before analysis\n- **Normalization matters**: Choose appropriate method for comparison type\n- **Extend reads carefully**: YES for ChIP-seq, NO for RNA-seq\n- **Use all cores**: Set `--numberOfProcessors` to available cores\n- **Test on regions**: Use `--region` for parameter testing\n- **Check QC first**: Run quality control before detailed analysis\n- **Document everything**: Save commands for reproducibility\n- **Reference documentation**: Use comprehensive references for detailed guidance\n","# deepTools: NGS Data Analysis Toolkit\n\n## Overview\n\ndeepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments.\n\n**Core capabilities:**\n- Convert BAM alignments to normalized coverage tracks (bigWig\u002FbedGraph)\n- Quality control assessment (fingerprint, correlation, coverage)\n- Sample comparison and correlation analysis\n- Heatmap and profile plot generation around genomic features\n- Enrichment analysis and peak region visualization\n\n## When to Use This Skill\n\nThis skill should be used when:\n\n- **File conversion**: \"Convert BAM to bigWig\", \"generate coverage tracks\", \"normalize ChIP-seq data\"\n- **Quality control**: \"check ChIP quality\", \"compare replicates\", \"assess sequencing depth\", \"QC analysis\"\n- **Visualization**: \"create heatmap around TSS\", \"plot ChIP signal\", \"visualize enrichment\", \"generate profile plot\"\n- **Sample comparison**: \"compare treatment vs control\", \"correlate samples\", \"PCA analysis\"\n- **Analysis workflows**: \"analyze ChIP-seq data\", \"RNA-seq coverage\", \"ATAC-seq analysis\", \"complete workflow\"\n- **Working with specific file types**: BAM files, bigWig files, BED region files in genomics context\n\n## Quick Start\n\nFor users new to deepTools, start with file validation and common workflows:\n\n### 1. Validate Input Files\n\nBefore running any analysis, validate BAM, bigWig, and BED files using the validation script:\n\n```bash\npython scripts\u002Fvalidate_files.py --bam sample1.bam sample2.bam --bed regions.bed\n```\n\nThis checks file existence, BAM indices, and format correctness.\n\n### 2. Generate Workflow Template\n\nFor standard analyses, use the workflow generator to create customized scripts:\n\n```bash\n# List available workflows\npython scripts\u002Fworkflow_generator.py --list\n\n# Generate ChIP-seq QC workflow\npython scripts\u002Fworkflow_generator.py chipseq_qc -o qc_workflow.sh \\\n    --input-bam Input.bam --chip-bams \"ChIP1.bam ChIP2.bam\" \\\n    --genome-size 2913022398\n\n# Make executable and run\nchmod +x qc_workflow.sh\n.\u002Fqc_workflow.sh\n```\n\n### 3. Most Common Operations\n\nSee `assets\u002Fquick_reference.md` for frequently used commands and parameters.\n\n## Installation\n\n```bash\nuv pip install deeptools\n```\n\n## Core Workflows\n\ndeepTools workflows typically follow this pattern: **QC → Normalization → Comparison\u002FVisualization**\n\n### ChIP-seq Quality Control Workflow\n\nWhen users request ChIP-seq QC or quality assessment:\n\n1. **Generate workflow script** using `scripts\u002Fworkflow_generator.py chipseq_qc`\n2. **Key QC steps**:\n   - Sample correlation (multiBamSummary + plotCorrelation)\n   - PCA analysis (plotPCA)\n   - Coverage assessment (plotCoverage)\n   - Fragment size validation (bamPEFragmentSize)\n   - ChIP enrichment strength (plotFingerprint)\n\n**Interpreting results:**\n- **Correlation**: Replicates should cluster together with high correlation (>0.9)\n- **Fingerprint**: Strong ChIP shows steep rise; flat diagonal indicates poor enrichment\n- **Coverage**: Assess if sequencing depth is adequate for analysis\n\nFull workflow details in `references\u002Fworkflows.md` → \"ChIP-seq Quality Control Workflow\"\n\n### ChIP-seq Complete Analysis Workflow\n\nFor full ChIP-seq analysis from BAM to visualizations:\n\n1. **Generate coverage tracks** with normalization (bamCoverage)\n2. **Create comparison tracks** (bamCompare for log2 ratio)\n3. **Compute signal matrices** around features (computeMatrix)\n4. **Generate visualizations** (plotHeatmap, plotProfile)\n5. **Enrichment analysis** at peaks (plotEnrichment)\n\nUse `scripts\u002Fworkflow_generator.py chipseq_analysis` to generate template.\n\nComplete command sequences in `references\u002Fworkflows.md` → \"ChIP-seq Analysis Workflow\"\n\n### RNA-seq Coverage Workflow\n\nFor strand-specific RNA-seq coverage tracks:\n\nUse bamCoverage with `--filterRNAstrand` to separate forward and reverse strands.\n\n**Important:** NEVER use `--extendReads` for RNA-seq (would extend over splice junctions).\n\nUse normalization: CPM for fixed bins, RPKM for gene-level analysis.\n\nTemplate available: `scripts\u002Fworkflow_generator.py rnaseq_coverage`\n\nDetails in `references\u002Fworkflows.md` → \"RNA-seq Coverage Workflow\"\n\n### ATAC-seq Analysis Workflow\n\nATAC-seq requires Tn5 offset correction:\n\n1. **Shift reads** using alignmentSieve with `--ATACshift`\n2. **Generate coverage** with bamCoverage\n3. **Analyze fragment sizes** (expect nucleosome ladder pattern)\n4. **Visualize at peaks** if available\n\nTemplate: `scripts\u002Fworkflow_generator.py atacseq`\n\nFull workflow in `references\u002Fworkflows.md` → \"ATAC-seq Workflow\"\n\n## Tool Categories and Common Tasks\n\n### BAM\u002FbigWig Processing\n\n**Convert BAM to normalized coverage:**\n```bash\nbamCoverage --bam input.bam --outFileName output.bw \\\n    --normalizeUsing RPGC --effectiveGenomeSize 2913022398 \\\n    --binSize 10 --numberOfProcessors 8\n```\n\n**Compare two samples (log2 ratio):**\n```bash\nbamCompare -b1 treatment.bam -b2 control.bam -o ratio.bw \\\n    --operation log2 --scaleFactorsMethod readCount\n```\n\n**Key tools:** bamCoverage, bamCompare, multiBamSummary, multiBigwigSummary, correctGCBias, alignmentSieve\n\nComplete reference: `references\u002Ftools_reference.md` → \"BAM and bigWig File Processing Tools\"\n\n### Quality Control\n\n**Check ChIP enrichment:**\n```bash\nplotFingerprint -b input.bam chip.bam -o fingerprint.png \\\n    --extendReads 200 --ignoreDuplicates\n```\n\n**Sample correlation:**\n```bash\nmultiBamSummary bins --bamfiles *.bam -o counts.npz\nplotCorrelation -in counts.npz --corMethod pearson \\\n    --whatToShow heatmap -o correlation.png\n```\n\n**Key tools:** plotFingerprint, plotCoverage, plotCorrelation, plotPCA, bamPEFragmentSize\n\nComplete reference: `references\u002Ftools_reference.md` → \"Quality Control Tools\"\n\n### Visualization\n\n**Create heatmap around TSS:**\n```bash\n# Compute matrix\ncomputeMatrix reference-point -S signal.bw -R genes.bed \\\n    -b 3000 -a 3000 --referencePoint TSS -o matrix.gz\n\n# Generate heatmap\nplotHeatmap -m matrix.gz -o heatmap.png \\\n    --colorMap RdBu --kmeans 3\n```\n\n**Create profile plot:**\n```bash\nplotProfile -m matrix.gz -o profile.png \\\n    --plotType lines --colors blue red\n```\n\n**Key tools:** computeMatrix, plotHeatmap, plotProfile, plotEnrichment\n\nComplete reference: `references\u002Ftools_reference.md` → \"Visualization Tools\"\n\n## Normalization Methods\n\nChoosing the correct normalization is critical for valid comparisons. Consult `references\u002Fnormalization_methods.md` for comprehensive guidance.\n\n**Quick selection guide:**\n\n- **ChIP-seq coverage**: Use RPGC or CPM\n- **ChIP-seq comparison**: Use bamCompare with log2 and readCount\n- **RNA-seq bins**: Use CPM\n- **RNA-seq genes**: Use RPKM (accounts for gene length)\n- **ATAC-seq**: Use RPGC or CPM\n\n**Normalization methods:**\n- **RPGC**: 1× genome coverage (requires --effectiveGenomeSize)\n- **CPM**: Counts per million mapped reads\n- **RPKM**: Reads per kb per million (accounts for region length)\n- **BPM**: Bins per million\n- **None**: Raw counts (not recommended for comparisons)\n\nFull explanation: `references\u002Fnormalization_methods.md`\n\n## Effective Genome Sizes\n\nRPGC normalization requires effective genome size. Common values:\n\n| Organism | Assembly | Size | Usage |\n|----------|----------|------|-------|\n| Human | GRCh38\u002Fhg38 | 2,913,022,398 | `--effectiveGenomeSize 2913022398` |\n| Mouse | GRCm38\u002Fmm10 | 2,652,783,500 | `--effectiveGenomeSize 2652783500` |\n| Zebrafish | GRCz11 | 1,368,780,147 | `--effectiveGenomeSize 1368780147` |\n| *Drosophila* | dm6 | 142,573,017 | `--effectiveGenomeSize 142573017` |\n| *C. elegans* | ce10\u002Fce11 | 100,286,401 | `--effectiveGenomeSize 100286401` |\n\nComplete table with read-length-specific values: `references\u002Feffective_genome_sizes.md`\n\n## Common Parameters Across Tools\n\nMany deepTools commands share these options:\n\n**Performance:**\n- `--numberOfProcessors, -p`: Enable parallel processing (always use available cores)\n- `--region`: Process specific regions for testing (e.g., `chr1:1-1000000`)\n\n**Read Filtering:**\n- `--ignoreDuplicates`: Remove PCR duplicates (recommended for most analyses)\n- `--minMappingQuality`: Filter by alignment quality (e.g., `--minMappingQuality 10`)\n- `--minFragmentLength` \u002F `--maxFragmentLength`: Fragment length bounds\n- `--samFlagInclude` \u002F `--samFlagExclude`: SAM flag filtering\n\n**Read Processing:**\n- `--extendReads`: Extend to fragment length (ChIP-seq: YES, RNA-seq: NO)\n- `--centerReads`: Center at fragment midpoint for sharper signals\n\n## Best Practices\n\n### File Validation\n**Always validate files first** using `scripts\u002Fvalidate_files.py` to check:\n- File existence and readability\n- BAM indices present (.bai files)\n- BED format correctness\n- File sizes reasonable\n\n### Analysis Strategy\n\n1. **Start with QC**: Run correlation, coverage, and fingerprint analysis before proceeding\n2. **Test on small regions**: Use `--region chr1:1-10000000` for parameter testing\n3. **Document commands**: Save full command lines for reproducibility\n4. **Use consistent normalization**: Apply same method across samples in comparisons\n5. **Verify genome assembly**: Ensure BAM and BED files use matching genome builds\n\n### ChIP-seq Specific\n\n- **Always extend reads** for ChIP-seq: `--extendReads 200`\n- **Remove duplicates**: Use `--ignoreDuplicates` in most cases\n- **Check enrichment first**: Run plotFingerprint before detailed analysis\n- **GC correction**: Only apply if significant bias detected; never use `--ignoreDuplicates` after GC correction\n\n### RNA-seq Specific\n\n- **Never extend reads** for RNA-seq (would span splice junctions)\n- **Strand-specific**: Use `--filterRNAstrand forward\u002Freverse` for stranded libraries\n- **Normalization**: CPM for bins, RPKM for genes\n\n### ATAC-seq Specific\n\n- **Apply Tn5 correction**: Use alignmentSieve with `--ATACshift`\n- **Fragment filtering**: Set appropriate min\u002Fmax fragment lengths\n- **Check nucleosome pattern**: Fragment size plot should show ladder pattern\n\n### Performance Optimization\n\n1. **Use multiple processors**: `--numberOfProcessors 8` (or available cores)\n2. **Increase bin size** for faster processing and smaller files\n3. **Process chromosomes separately** for memory-limited systems\n4. **Pre-filter BAM files** using alignmentSieve to create reusable filtered files\n5. **Use bigWig over bedGraph**: Compressed and faster to process\n\n## Troubleshooting\n\n### Common Issues\n\n**BAM index missing:**\n```bash\nsamtools index input.bam\n```\n\n**Out of memory:**\nProcess chromosomes individually using `--region`:\n```bash\nbamCoverage --bam input.bam -o chr1.bw --region chr1\n```\n\n**Slow processing:**\nIncrease `--numberOfProcessors` and\u002For increase `--binSize`\n\n**bigWig files too large:**\nIncrease bin size: `--binSize 50` or larger\n\n### Validation Errors\n\nRun validation script to identify issues:\n```bash\npython scripts\u002Fvalidate_files.py --bam *.bam --bed regions.bed\n```\n\nCommon errors and solutions explained in script output.\n\n## Reference Documentation\n\nThis skill includes comprehensive reference documentation:\n\n### references\u002Ftools_reference.md\nComplete documentation of all deepTools commands organized by category:\n- BAM and bigWig processing tools (9 tools)\n- Quality control tools (6 tools)\n- Visualization tools (3 tools)\n- Miscellaneous tools (2 tools)\n\nEach tool includes:\n- Purpose and overview\n- Key parameters with explanations\n- Usage examples\n- Important notes and best practices\n\n**Use this reference when:** Users ask about specific tools, parameters, or detailed usage.\n\n### references\u002Fworkflows.md\nComplete workflow examples for common analyses:\n- ChIP-seq quality control workflow\n- ChIP-seq complete analysis workflow\n- RNA-seq coverage workflow\n- ATAC-seq analysis workflow\n- Multi-sample comparison workflow\n- Peak region analysis workflow\n- Troubleshooting and performance tips\n\n**Use this reference when:** Users need complete analysis pipelines or workflow examples.\n\n### references\u002Fnormalization_methods.md\nComprehensive guide to normalization methods:\n- Detailed explanation of each method (RPGC, CPM, RPKM, BPM, etc.)\n- When to use each method\n- Formulas and interpretation\n- Selection guide by experiment type\n- Common pitfalls and solutions\n- Quick reference table\n\n**Use this reference when:** Users ask about normalization, comparing samples, or which method to use.\n\n### references\u002Feffective_genome_sizes.md\nEffective genome size values and usage:\n- Common organism values (human, mouse, fly, worm, zebrafish)\n- Read-length-specific values\n- Calculation methods\n- When and how to use in commands\n- Custom genome calculation instructions\n\n**Use this reference when:** Users need genome size for RPGC normalization or GC bias correction.\n\n## Helper Scripts\n\n### scripts\u002Fvalidate_files.py\n\nValidates BAM, bigWig, and BED files for deepTools analysis. Checks file existence, indices, and format.\n\n**Usage:**\n```bash\npython scripts\u002Fvalidate_files.py --bam sample1.bam sample2.bam \\\n    --bed peaks.bed --bigwig signal.bw\n```\n\n**When to use:** Before starting any analysis, or when troubleshooting errors.\n\n### scripts\u002Fworkflow_generator.py\n\nGenerates customizable bash script templates for common deepTools workflows.\n\n**Available workflows:**\n- `chipseq_qc`: ChIP-seq quality control\n- `chipseq_analysis`: Complete ChIP-seq analysis\n- `rnaseq_coverage`: Strand-specific RNA-seq coverage\n- `atacseq`: ATAC-seq with Tn5 correction\n\n**Usage:**\n```bash\n# List workflows\npython scripts\u002Fworkflow_generator.py --list\n\n# Generate workflow\npython scripts\u002Fworkflow_generator.py chipseq_qc -o qc.sh \\\n    --input-bam Input.bam --chip-bams \"ChIP1.bam ChIP2.bam\" \\\n    --genome-size 2913022398 --threads 8\n\n# Run generated workflow\nchmod +x qc.sh\n.\u002Fqc.sh\n```\n\n**When to use:** Users request standard workflows or need template scripts to customize.\n\n## Assets\n\n### assets\u002Fquick_reference.md\n\nQuick reference card with most common commands, effective genome sizes, and typical workflow pattern.\n\n**When to use:** Users need quick command examples without detailed documentation.\n\n## Handling User Requests\n\n### For New Users\n\n1. Start with installation verification\n2. Validate input files using `scripts\u002Fvalidate_files.py`\n3. Recommend appropriate workflow based on experiment type\n4. Generate workflow template using `scripts\u002Fworkflow_generator.py`\n5. Guide through customization and execution\n\n### For Experienced Users\n\n1. Provide specific tool commands for requested operations\n2. Reference appropriate sections in `references\u002Ftools_reference.md`\n3. Suggest optimizations and best practices\n4. Offer troubleshooting for issues\n\n### For Specific Tasks\n\n**\"Convert BAM to bigWig\":**\n- Use bamCoverage with appropriate normalization\n- Recommend RPGC or CPM based on use case\n- Provide effective genome size for organism\n- Suggest relevant parameters (extendReads, ignoreDuplicates, binSize)\n\n**\"Check ChIP quality\":**\n- Run full QC workflow or use plotFingerprint specifically\n- Explain interpretation of results\n- Suggest follow-up actions based on results\n\n**\"Create heatmap\":**\n- Guide through two-step process: computeMatrix → plotHeatmap\n- Help choose appropriate matrix mode (reference-point vs scale-regions)\n- Suggest visualization parameters and clustering options\n\n**\"Compare samples\":**\n- Recommend bamCompare for two-sample comparison\n- Suggest multiBamSummary + plotCorrelation for multiple samples\n- Guide normalization method selection\n\n### Referencing Documentation\n\nWhen users need detailed information:\n- **Tool details**: Direct to specific sections in `references\u002Ftools_reference.md`\n- **Workflows**: Use `references\u002Fworkflows.md` for complete analysis pipelines\n- **Normalization**: Consult `references\u002Fnormalization_methods.md` for method selection\n- **Genome sizes**: Reference `references\u002Feffective_genome_sizes.md`\n\nSearch references using grep patterns:\n```bash\n# Find tool documentation\ngrep -A 20 \"^### toolname\" references\u002Ftools_reference.md\n\n# Find workflow\ngrep -A 50 \"^## Workflow Name\" references\u002Fworkflows.md\n\n# Find normalization method\ngrep -A 15 \"^### Method Name\" references\u002Fnormalization_methods.md\n```\n\n## Example Interactions\n\n**User: \"I need to analyze my ChIP-seq data\"**\n\nResponse approach:\n1. 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