/visualization-workflow

name: visualization-workflow
description: "Comprehensive guide for visualizing ENCODE data including deeptools heatmaps, IGV screenshots, UCSC track hubs, and publication-quality plots. Use when users need to create visualizations of ChIP-seq signal, peak landscapes, genome browser views, or any visual representation of ENCODE data. Trigger on: heatmap, visualization, genome browser, track hub, IGV, deeptools, signal plot, peak visualization, profile plot, publication figure, bigWig visualization."

Visualization Workflow for ENCODE Data

When to Use

• User wants to create genome browser visualizations, heatmaps, or signal track plots from ENCODE data
• User asks about "visualization", "genome browser", "deeptools", "heatmap", "signal track", or "IGV"
• User needs to generate publication-ready figures from ChIP-seq, ATAC-seq, or other genomic data
• User wants to compare signal profiles across conditions, tissues, or histone marks
• Example queries: "visualize H3K27ac signal at promoters", "create a heatmap of ChIP-seq signal", "set up a UCSC track hub for my data"

Help the user create informative, publication-quality visualizations of ENCODE genomic data. This skill covers four major visualization approaches: deepTools heatmaps and profiles, IGV genome browser views, UCSC track hubs for sharing, and publication-quality static plots using R and Python. Visualization is not decorative -- it is an essential analytical step that reveals patterns invisible in summary statistics and validates computational findings.

Literature Foundation

| Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Ramirez et al. (2016) | Nucleic Acids Research | deepTools2: next-generation server for deep-sequencing data analysis; heatmaps, profiles, correlation, PCA | [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) | ~3,800 | | Robinson et al. (2011) | Nature Biotechnology | Integrative Genomics Viewer (IGV): interactive exploration of large genomic datasets | [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754) | ~10,000 | | Kent et al. (2002) | Genome Research | The Human Genome Browser at UCSC: foundation for track-based genomic visualization | [10.1101/gr.229102](https://doi.org/10.1101/gr.229102) | ~8,000 | | Ramirez et al. (2014) | Nucleic Acids Research | deepTools: flexible platform for exploring deep-sequencing data; original computeMatrix/plotHeatmap framework | [10.1093/nar/gku365](https://doi.org/10.1093/nar/gku365) | ~2,500 | | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: comprehensive identification of artifact regions to exclude from visualization | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 | | Wickham (2016) | Springer | ggplot2: Elegant Graphics for Data Analysis; grammar of graphics for genomic visualization | ISBN: 978-3-319-24277-4 | ~30,000+ |

Part 1: deepTools Heatmaps and Profiles

deepTools (Ramirez et al. 2014, 2016) is the standard toolkit for visualizing ChIP-seq and ATAC-seq signal across genomic regions. The core workflow is: compute a signal matrix, then render it as a heatmap or profile plot.

1a. computeMatrix: Building the Signal Matrix

`computeMatrix` extracts signal values from bigWig files across a set of genomic regions. Two modes are available:

**reference-point mode** -- centers the signal on a single anchor point (e.g., TSS, peak summit):

# Signal centered on peak summits, +/- 3kb
computeMatrix reference-point \
    -S H3K27ac_fc.bigWig H3K4me3_fc.bigWig ATAC_fc.bigWig \
    -R peaks.bed \
    --referencePoint center \
    -b 3000 -a 3000 \
    --binSize 50 \
    --missingDataAsZero \
    --sortRegions descend \
    --sortUsing mean \
    -o matrix_refpoint.gz \
    -p 8

**scale-regions mode** -- scales all regions to uniform length (e.g., gene bodies):

# Signal across scaled gene bodies with 2kb flanks
computeMatrix scale-regions \
    -S H3K36me3_fc.bigWig RNA_signal.bigWig \
    -R genes.bed \
    --regionBodyLength 5000 \
    -b 2000 -a 2000 \
    --binSize 50 \
    --missingDataAsZero \
    -o matrix_scaled.gz \
    -p 8

**When to use which mode**:

• `reference-point`: TF ChIP-seq peaks, ATAC-seq summits, TSSs, enhancer centers -- any feature defined by a point
• `scale-regions`: gene bodies, broad histone domains (H3K27me3, H3K36me3), TADs -- features with variable length

1b. plotHeatmap: Rendering the Matrix

plotHeatmap -m matrix_refpoint.gz \
    -o heatmap.png \
    --colorMap RdYlBu_r \
    --whatToShow "heatmap and colorbar" \
    --sortRegions descend \
    --sortUsing mean \
    --heatmapHeight 15 \
    --heatmapWidth 4 \
    --zMin 0 --zMax 10 \
    --samplesLabel "H3K27ac" "H3K4me3" "ATAC" \
    --regionsLabel "Peaks" \
    --dpi 300

**Clustering**: To reveal sub-patterns within peak sets:

plotHeatmap -m matrix_refpoint.gz \
    -o heatmap_clustered.png \
    --kmeans 4 \
    --colorMap viridis \
    --zMin 0 --zMax 10 \
    --outFileSortedRegions clusters.bed \
    --dpi 300

The `--outFileSortedRegions` flag exports the cluster assignments as a BED file, enabling downstream analysis of each cluster separately.

**Recommended color maps by mark type**: | Mark Type | Recommended colorMap | Rationale | |-----------|---------------------|-----------| | Active marks (H3K27ac, H3K4me3) | Reds, YlOrRd | Warm colors for activation | | Repressive marks (H3K27me3, H3K9me3) | Blues, PuBu | Cool colors for repression | | Accessibility (ATAC, DNase) | Greens, YlGn | Distinct from histone colors | | Multi-mark comparison | viridis, inferno | Perceptually uniform, colorblind-safe |

1c. plotProfile: Average Signal Plots

Profile plots show the average signal across all regions, useful for comparing samples:

plotProfile -m matrix_refpoint.gz \
    -o profile.png \
    --perGroup \
    --plotTitle "Signal at H3K27ac peaks" \
    --yAxisLabel