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gh-k-dense-ai-claude-scient…/skills/deeptools/assets/quick_reference.md
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# deepTools Quick Reference
## Most Common Commands
### BAM to bigWig (normalized)
```bash
bamCoverage --bam input.bam --outFileName output.bw \
--normalizeUsing RPGC --effectiveGenomeSize 2913022398 \
--binSize 10 --numberOfProcessors 8
```
### Compare two BAM files
```bash
bamCompare -b1 treatment.bam -b2 control.bam -o ratio.bw \
--operation log2 --scaleFactorsMethod readCount
```
### Correlation heatmap
```bash
multiBamSummary bins --bamfiles *.bam -o counts.npz
plotCorrelation -in counts.npz --corMethod pearson \
--whatToShow heatmap -o correlation.png
```
### Heatmap around TSS
```bash
computeMatrix reference-point -S signal.bw -R genes.bed \
-b 3000 -a 3000 --referencePoint TSS -o matrix.gz
plotHeatmap -m matrix.gz -o heatmap.png
```
### ChIP enrichment check
```bash
plotFingerprint -b input.bam chip.bam -o fingerprint.png \
--extendReads 200 --ignoreDuplicates
```
## Effective Genome Sizes
| Organism | Assembly | Size |
|----------|----------|------|
| Human | hg38 | 2913022398 |
| Mouse | mm10 | 2652783500 |
| Fly | dm6 | 142573017 |
## Common Normalization Methods
- **RPGC**: 1× genome coverage (requires --effectiveGenomeSize)
- **CPM**: Counts per million (for fixed bins)
- **RPKM**: Reads per kb per million (for genes)
## Typical Workflow
1. **QC**: plotFingerprint, plotCorrelation
2. **Coverage**: bamCoverage with normalization
3. **Comparison**: bamCompare for treatment vs control
4. **Visualization**: computeMatrix → plotHeatmap/plotProfile