visualization-workflowlisted

# 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):