epigenome-profiling

Build Comprehensive Epigenomic Profiles with ENCODE

When to Use

• User wants to build a comprehensive epigenomic profile for a tissue or cell type
• User asks about "chromatin states", "epigenome", or "histone landscape" for a biosample
• User wants to identify super-enhancers, bivalent domains, or regulatory elements
• User needs to assemble a panel of histone marks, accessibility, and TF binding data
• User wants to run ChromHMM segmentation on ENCODE data
• User asks "what epigenomic data does ENCODE have for [tissue]?"

Assemble a complete epigenomic profile for a tissue or cell type by systematically gathering histone modifications, chromatin accessibility, transcription factor binding, transcription, DNA methylation, and 3D chromatin structure data from ENCODE. Interpret the resulting profile using ChromHMM chromatin state segmentation.

Literature Foundation

Reference	Year	Journal	DOI	Citations	Contribution
Roadmap Epigenomics Consortium (Kundaje et al.)	2015	Nature	10.1038/nature14248	~5,810	111 reference epigenomes; 5-mark core model; 15/18/25-state ChromHMM
ENCODE Phase 3 (ENCODE Project Consortium)	2020	Nature	10.1038/s41586-020-2493-4	~1,656	Registry of candidate cis-regulatory elements (cCREs) across 1,310+ experiments
Ernst & Kellis	2012	Nat Methods	10.1038/nmeth.1906	~2,294	ChromHMM: multivariate HMM for chromatin state discovery and characterization
Barski et al.	2007	Cell	10.1016/j.cell.2007.05.009	~4,800	First genome-wide ChIP-Seq of 20 histone methylations in human CD4+ T cells
Mikkelsen et al.	2007	Nature	10.1038/nature06008	~4,289	Chromatin state maps in pluripotent and lineage-committed cells; H3K4me3/H3K27me3 discriminate expressed, poised, and repressed genes
Bernstein et al.	2006	Cell	10.1016/j.cell.2006.02.041	~3,500	Discovery of bivalent chromatin domains (H3K4me3+H3K27me3) in embryonic stem cells
Creyghton et al.	2010	PNAS	10.1073/pnas.1016071107	~2,800	H3K27ac distinguishes active enhancers from poised (H3K4me1-only) enhancers
Whyte et al.	2013	Cell	10.1016/j.cell.2013.03.035	~2,500	Master transcription factors and super-enhancer identification via ROSE algorithm
Buenrostro et al.	2013	Nat Methods	10.1038/nmeth.2688	~5,000	ATAC-seq: transposase-based chromatin accessibility profiling
Heintzman et al.	2007	Nat Genet	10.1038/ng1966	~2,300	H3K4me1 marks enhancers, H3K4me3 marks promoters — foundational chromatin signature for regulatory element classification
Rada-Iglesias et al.	2011	Nature	10.1038/nature09692	~1,200	Discovered "poised enhancers" (H3K4me1+H3K27me3, no H3K27ac) that activate during differentiation
ENCODE Blacklist (Amemiya et al.)	2019	Sci Rep	10.1038/s41598-019-45839-z	~1,372	Comprehensive set of problematic genomic regions to exclude from all analyses

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Step 1: Choose the Target Biosample

Clarify the target biosample with the user. Check data availability across assay types:

encode_get_facets(organ="pancreas", biosample_type="tissue")

ENCODE Cell Line Tiers

Tier	Cell Lines	Data Depth	Notes
Tier 1 (most data)	K562, GM12878, H1-hESC	Deep profiling across all assays	Preferred for methods development and benchmarking
Tier 2 (good coverage)	HeLa-S3, HepG2, HUVEC, A549, MCF-7	Most core marks and accessibility	Suitable for tissue-specific profiling
Tier 3+ (variable)	100+ additional cell lines and primary tissues	Variable coverage	Check availability per assay before committing

For primary tissues, verify what biosamples are available:

encode_search_experiments(organ="pancreas", biosample_type="tissue", limit=50)

Biosample hierarchy (from most to least standardized): tissue > primary cell > cell line > in vitro differentiated cells > organoid. Cell lines offer the deepest profiling. Primary tissues offer biological relevance but greater heterogeneity.

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Step 2: Assemble the Histone Modification Panel

Search for each histone mark in the target biosample. Organize the panel into three tiers of increasing depth.

Tier 1: Core 5-Mark Panel (ChromHMM Minimum)

This is the minimum set required for chromatin state segmentation. All 111 Roadmap Epigenomics reference epigenomes were profiled for these five marks (Kundaje et al. 2015). Ernst & Kellis (2012) demonstrated that these five marks suffice for the 15-state ChromHMM model that captures all major functional categories.

Mark	What It Marks	Genomic Location	Writers	Readers	Key Reference
H3K4me3	Active and poised promoters	Sharp peaks at TSSs	SET1A/B (COMPASS), MLL1/2	TAF3, ING proteins, CHD1	Barski et al. 2007
H3K4me1	Enhancers (primed and active)	Distal regulatory elements	MLL3 (KMT2C), MLL4 (KMT2D)	CHD1, BPTF	Heintzman et al. 2007
H3K27me3	Polycomb-mediated repression	Broad domains over silent genes	EZH2 (PRC2), EZH1	EED, CBX proteins (PRC1)	Bernstein et al. 2006
H3K36me3	Actively transcribed gene bodies	Gene bodies, 5'-to-3' gradient	SETD2 (sole trimethylase)	DNMT3B, MSH6	Mikkelsen et al. 2007
H3K9me3	Constitutive heterochromatin	Repeats, TEs, ERVs, pericentromeric	SUV39H1/2, SETDB1	HP1alpha/beta/gamma	Barski et al. 2007

Note on H3K27ac: While not in the Roadmap 5-mark core, H3K27ac is essential for distinguishing active from poised elements (Creyghton et al. 2010). It is included in the 18-state extended ChromHMM model. Always include H3K27ac if available.

Search for each:

encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K4me3",
    biosample_term_name="...",
    biosample_type="tissue"
)

Tier 2: Extended Panel

These marks provide finer-grained state resolution. The Ernst et al. (2011) 15-state model across 9 cell types used these marks together with the core 5 to define insulator, active promoter, and transcription states more precisely.

Mark	What It Marks	Genomic Location	Key Reference
H3K9ac	Active promoters and regulatory regions	TSSs, co-occurs with H3K4me3	Wang et al. 2008
H3K79me2	Transcription elongation	Gene bodies (DOT1L-mediated)	Barski et al. 2007
H2A.Z (H2AFZ)	Active regulatory elements	TSSs, enhancers, insulators	Barski et al. 2007
H4K20me1	Transcription and cell cycle	Gene bodies	Barski et al. 2007
H3K27ac	Active enhancers and promoters	Active regulatory elements (mutually exclusive with H3K27me3)	Creyghton et al. 2010

Tier 3: Advanced Panel

These acetylation marks provide additional granularity for specialized analyses. They are rarely profiled outside Tier 1 cell lines but can distinguish subtypes of active chromatin.

Mark	What It Marks	Genomic Location	Key Reference
H3K14ac	Active promoters, DNA damage response	Active TSSs, DNA double-strand break sites	Wang et al. 2008
H3K18ac	Active transcription	Active promoters and enhancers	Wang et al. 2008
H3K23ac	Active transcription	Active promoters	Wang et al. 2008
H4K5ac	Active chromatin, super-enhancers	Promoters, enhancers	Das et al. 2023
H4K8ac	Active chromatin, super-enhancers	Promoters, enhancers	Das et al. 2023
H4K16ac	Euchromatin maintenance	Globally across active euchromatin	Shogren-Knaak et al. 2006

For detailed mark biology, writers, erasers, readers, and contradictions, see the histone marks reference in the histone-aggregation skill's references/histone-marks-reference.md (1,442 lines, 74 references).

---

Step 3: Add Chromatin Accessibility

Open chromatin profiling is essential for identifying active regulatory elements. ATAC-seq is the current standard; DNase-seq is the legacy method with deeper ENCODE archives.

# Prefer ATAC-seq (Buenrostro et al. 2013) — lower input, faster protocol
encode_search_experiments(assay_title="ATAC-seq", biosample_term_name="...")

# Fall back to DNase-seq if ATAC-seq is unavailable
encode_search_experiments(assay_title="DNase-seq", biosample_term_name="...")

ATAC-seq and DNase-seq identify largely overlapping accessible regions (Pearson r > 0.8 at promoters), but ATAC-seq captures some distal elements missed by DNase-seq and vice versa (Corces et al. 2017). Do not mix the two assays in a single analysis without careful normalization.

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Step 4: Add Transcription Factor Binding

Two TFs provide critical structural and functional information for epigenomic profiling:

TF	Role	Why Essential	ENCODE Tool Call
CTCF	Insulator, TAD boundary factor	Defines chromatin domains; required for ChromHMM insulator state in expanded models	encode_search_experiments(assay_title="TF ChIP-seq", target="CTCF", biosample_term_name="...")
EP300 (p300)	Enhancer co-activator	p300 binding marks active enhancers independently of histone marks (Visel et al. 2009)	encode_search_experiments(assay_title="TF ChIP-seq", target="EP300", biosample_term_name="...")

Use encode_get_facets to discover which TFs are available for the target biosample:

encode_get_facets(assay_title="TF ChIP-seq", organ="pancreas")

---

Step 5: Add Transcription Data

Gene expression data links chromatin states to functional output.

# Poly-A plus RNA-seq (mRNA)
encode_search_experiments(assay_title="polyA plus RNA-seq", biosample_term_name="...")

# Total RNA-seq (includes non-coding RNAs, intronic transcripts)
encode_search_experiments(assay_title="total RNA-seq", biosample_term_name="...")

Both are valuable: poly-A RNA-seq captures mRNA levels for gene-level correlation with chromatin states. Total RNA-seq captures eRNAs (enhancer RNAs), lncRNAs, and other non-coding transcripts that inform regulatory element activity.

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Step 6: Add DNA Methylation

Whole-genome bisulfite sequencing (WGBS) provides single-CpG resolution methylation across the entire genome. DNA methylation is anticorrelated with H3K4me3 at CpG island promoters and anticorrelated with H3K27me3 at bivalent domains (Bernstein et al. 2006).

encode_search_experiments(assay_title="WGBS", biosample_term_name="...")

If WGBS is unavailable, check for RRBS (reduced representation bisulfite sequencing). Note that RRBS covers only CpG-dense regions and is incompatible with genome-wide methylation analyses such as partially methylated domain (PMD) or large hypo-methylated region (HMR) identification.

---

Step 7: Add 3D Chromatin Structure

3D genome organization data connects regulatory elements to their target genes through chromatin loops and topologically associating domains (TADs).

# Hi-C: genome-wide chromatin conformation
encode_search_experiments(assay_title="Hi-C", biosample_term_name="...")

# ChIA-PET: protein-centric interaction mapping
encode_search_experiments(assay_title="ChIA-PET", biosample_term_name="...")

Hi-C provides unbiased genome-wide contact maps. ChIA-PET enriches for interactions mediated by specific proteins (CTCF, RNAPII). Both are useful but not essential for a core epigenomic profile.

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Step 8: ChromHMM Chromatin State Segmentation

ChromHMM (Ernst & Kellis 2012) is the standard tool for learning chromatin states from combinatorial histone modification patterns. It uses a multivariate Hidden Markov Model to segment the genome into functionally distinct states.

The 5-Mark Core Model (15 States)

The Roadmap Epigenomics 15-state model (Kundaje et al. 2015) uses H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3:

State	Name	H3K4me3	H3K4me1	H3K36me3	H3K27me3	H3K9me3	Interpretation
1	TssA	HIGH	LOW	-	-	-	Active TSS
2	TssAFlnk	MED	MED	-	-	-	Flanking Active TSS
3	TxFlnk	LOW	MED	LOW	-	-	Transcription at gene 5' and 3'
4	Tx	-	-	HIGH	-	-	Strong Transcription
5	TxWk	-	-	MED	-	-	Weak Transcription
6	EnhG	-	MED	MED	-	-	Genic Enhancers
7	Enh	-	HIGH	-	-	-	Enhancers
8	ZNF/Rpts	-	-	LOW	-	HIGH	ZNF Genes & Repeats
9	Het	-	-	-	-	HIGH	Heterochromatin
10	TssBiv	HIGH	MED	-	HIGH	-	Bivalent/Poised TSS
11	BivFlnk	MED	MED	-	HIGH	-	Flanking Bivalent TSS/Enhancer
12	EnhBiv	-	HIGH	-	HIGH	-	Bivalent Enhancer
13	ReprPC	-	-	-	HIGH	-	Repressed Polycomb
14	ReprPCWk	-	-	-	MED	-	Weak Repressed Polycomb
15	Quies	-	-	-	-	-	Quiescent/Low signal

Source: Kundaje et al. (2015) Table derived from the 15-state core model applied to 111 reference epigenomes.

The 18-State Extended Model

Adding H3K27ac as a 6th mark (available for a subset of Roadmap epigenomes) enables the 18-state model, which splits:

• Active TSS by H3K27ac level
• Enhancers into active (with H3K27ac) versus poised (without)
• Genic enhancers with finer precision

Running ChromHMM

ChromHMM requires binarized ChIP-seq signal (BED or BAM) for each mark. The standard pipeline:

1. Binarize signal into 200bp bins using BinarizeBed or BinarizeBam
2. Learn model with LearnModel (specify number of states; 15 is standard)
3. Assign states genome-wide with the learned emission/transition parameters
4. Compare across samples using CompareModels or Reorder

For full detail on ChromHMM state interpretation, see the histone marks reference in the histone-aggregation skill's references/histone-marks-reference.md, Part 2: Combinatorial Patterns.

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Functional Mark Combinations

Specific histone mark combinations carry distinct biological meanings. These are not arbitrary -- they reflect biochemically antagonistic or cooperative modifications at the same genomic locus.

Active Elements

Combination	Biological State	Location	Key Reference
H3K4me3 + H3K27ac	Active promoter	TSS	Creyghton et al. 2010
H3K4me1 + H3K27ac	Active enhancer	Distal regulatory elements	Creyghton et al. 2010; Rada-Iglesias et al. 2011
H3K36me3 + H3K79me2	Actively transcribed gene body	Gene bodies	Barski et al. 2007

Poised/Primed Elements

Combination	Biological State	Location	Key Reference
H3K4me3 + H3K27me3	Bivalent promoter	Developmental gene TSSs	Bernstein et al. 2006
H3K4me1 + H3K27me3	Poised enhancer	Developmental enhancers	Rada-Iglesias et al. 2011
H3K4me1 alone (no H3K27ac)	Primed enhancer	Distal elements, latent regulatory	Creyghton et al. 2010

Repressed Elements

Combination	Biological State	Location	Key Reference
H3K27me3 alone	Polycomb-repressed	Facultative heterochromatin	Boyer et al. 2006
H3K9me3 alone	Constitutive heterochromatin	Repeats, pericentromeric, TEs	Rea et al. 2000
H3K9me3 + H3K36me3	ZNF/KRAB-ZFP gene	KRAB zinc finger gene clusters	Not repressed -- unique state

Conflicting Signals

Combination	Interpretation	Action
H3K36me3 + H3K27me3	Domain boundary or mixed cell populations	Investigate at single-cell level; do not interpret as a coherent state
H3K4me3 + H3K9me3	Likely mixed signal from heterogeneous tissue	Filter for single-cell or sorted-population data

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Bivalent Chromatin Domains

Bivalent domains carry both H3K4me3 (active mark) and H3K27me3 (repressive mark) at the same promoter. They were discovered in embryonic stem cells (Bernstein et al. 2006) and are enriched at developmental transcription factor genes.

Key Properties

• Prevalence: ~2,000-3,000 bivalent promoters in human ESCs, decreasing upon differentiation (Mikkelsen et al. 2007).
• Resolution: During lineage commitment, bivalent domains resolve to H3K4me3-only (gene activated) or H3K27me3-only (gene stably repressed).
• Same-nucleosome co-occurrence: Sequential ChIP (re-ChIP) confirmed both marks on the same nucleosome or allele (Bernstein et al. 2006).
• Not restricted to ESCs: Found in adult stem cells and progenitor cells, though enriched in pluripotent states (Harikumar & Meshorer 2015).

The Poising vs. Protection Debate

The original model proposed that bivalency "poises" genes for rapid activation upon developmental cues (Bernstein et al. 2006). An alternative model argues that H3K4me3 at bivalent promoters primarily protects CpG islands from de novo DNA methylation, preventing irreversible silencing (Kumar et al. 2021, Genome Res).

Current consensus (Macrae et al. 2022, Nat Rev Mol Cell Biol, DOI:10.1038/s41580-022-00544-w): Both models are compatible. Bivalency maintains epigenetic plasticity by preventing permanent silencing, which as a consequence preserves potential for future activation. In cancer, loss of bivalency at tumor suppressor promoters correlates with aberrant DNA hypermethylation and irreversible gene silencing (Ohm et al. 2007).

Detecting Bivalent Domains in ENCODE Data

1. Call H3K4me3 peaks (narrowPeak, IDR thresholded)
2. Call H3K27me3 peaks (broadPeak preferred for this broad mark)
3. Intersect: regions carrying both marks within 2kb of a TSS are candidate bivalent promoters
4. Validate using ChromHMM state 10 (TssBiv) from the 15-state model

Caveat: In bulk tissue data, apparent bivalency may reflect mixed cell populations where one cell type expresses H3K4me3 and another H3K27me3 at the same locus. Single-cell or sorted-population ChIP-seq is required to confirm true bivalency.

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Super-Enhancer Identification

Super-enhancers are large clusters of enhancers with disproportionately high H3K27ac and Mediator (MED1) signal. They drive expression of cell-identity genes and are preferentially sensitive to perturbation (Whyte et al. 2013).

ROSE Algorithm

The Rank Ordering of Super-Enhancers (ROSE) algorithm (Whyte et al. 2013; Loven et al. 2013, Cell, DOI:10.1016/j.cell.2013.03.036, ~2,700 cit):

1. Call H3K27ac peaks (or MED1/BRD4 peaks)
2. Stitch peaks within 12.5kb of each other (excluding +/- 2kb from TSSs to avoid promoter signal)
3. Rank stitched enhancers by total H3K27ac signal
4. Apply geometric inflection point to separate typical enhancers from super-enhancers

Using ENCODE Data for Super-Enhancer Calls

encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K27ac",
    biosample_term_name="...",
    biosample_type="..."
)

Download fold-change-over-control bigWig and IDR thresholded peaks for input to ROSE. Cancer cells acquire de novo super-enhancers at oncogenes (Hnisz et al. 2013, Cell, DOI:10.1016/j.cell.2013.09.053), making super-enhancer profiling valuable for disease research.

Caveat: The super-enhancer concept is debated. Pott & Lieb (2015, Nat Genet 47:8-12) argued that individual constituent enhancers within super-enhancers can function independently, and the term may imply a mechanistic distinction that does not exist.

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Step 9: Assess Profile Completeness

Present a coverage matrix to the user showing what data layers are available:

| Data Layer           | Assay        | Available | Accession   | Audit Status |
|----------------------|-------------|-----------|-------------|--------------|
| H3K4me3              | Histone ChIP | Yes       | ENCSR...    | PASS         |
| H3K4me1              | Histone ChIP | Yes       | ENCSR...    | PASS         |
| H3K27me3             | Histone ChIP | Yes       | ENCSR...    | WARNING      |
| H3K36me3             | Histone ChIP | Yes       | ENCSR...    | PASS         |
| H3K9me3              | Histone ChIP | No        | -           | -            |
| H3K27ac              | Histone ChIP | Yes       | ENCSR...    | PASS         |
| Accessibility        | ATAC-seq     | Yes       | ENCSR...    | PASS         |
| CTCF                 | TF ChIP-seq  | Yes       | ENCSR...    | PASS         |
| p300                 | TF ChIP-seq  | No        | -           | -            |
| mRNA expression      | RNA-seq      | Yes       | ENCSR...    | PASS         |
| Total RNA            | total RNA-seq| No        | -           | -            |
| DNA methylation      | WGBS         | No        | -           | -            |
| 3D structure         | Hi-C         | No        | -           | -            |

Minimum viable profile

• 5 core histone marks (H3K4me3, H3K4me1, H3K27me3, H3K36me3, H3K9me3): enables 15-state ChromHMM
• • H3K27ac: enables active vs. poised distinction (18-state model)
• • Accessibility (ATAC-seq or DNase-seq): validates regulatory element calls
• • RNA-seq: links chromatin states to gene expression

Track all found experiments

For each available experiment:
    encode_track_experiment(accession="ENCSR...")

Then summarize the collection:

encode_summarize_collection()

---

Step 10: Download and Organize Profile Data

Use encode_batch_download with dry_run=True first to preview:

# Histone mark signal tracks
encode_batch_download(
    assay_title="Histone ChIP-seq",
    biosample_term_name="...",
    file_format="bigWig",
    output_type="fold change over control",
    assembly="GRCh38",
    download_dir="/path/to/epigenome_profile/signal",
    preferred_default=True,
    organize_by="experiment",
    dry_run=True
)

# Histone mark peak calls
encode_batch_download(
    assay_title="Histone ChIP-seq",
    biosample_term_name="...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38",
    download_dir="/path/to/epigenome_profile/peaks",
    preferred_default=True,
    organize_by="experiment",
    dry_run=True
)

File Selection Priority

Follow this hierarchy when selecting files for analysis:

1. preferred_default=True files (ENCODE-recommended)
2. IDR thresholded peaks for narrow marks (H3K4me3, H3K4me1, H3K27ac, H3K9ac)
3. Replicated peaks for broad marks (H3K27me3, H3K9me3, H3K36me3)
4. Fold change over control bigWig for signal visualization
5. Assembly: GRCh38 for human, mm10 for mouse. Never mix assemblies in a single profile.

Always filter with the ENCODE blacklist (Amemiya et al. 2019) before any downstream analysis. Download from: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz

---

Pitfalls and Caveats

1. Batch Effects Across Labs and Dates

Histone ChIP-seq experiments from different ENCODE labs can show substantial batch effects in signal intensity, peak width, and background levels. When assembling a profile from multiple labs, normalize signal tracks independently and compare peak calls rather than raw signal.

2. Antibody Lot Variation

Different antibody lots for the same histone mark target can have different specificities and affinities. ENCODE documents antibody lot information in experiment metadata. Prefer experiments using validated antibody lots. Be especially cautious with H3K9me3 and H3K27me3 antibodies, which can cross-react.

3. Cell Type Heterogeneity in Tissue Samples

Bulk tissue profiling averages signal across all cell types present. A chromatin mark detected in bulk tissue may be present in only a minor cell population. Apparent bivalent domains (H3K4me3+H3K27me3) in tissue may reflect distinct cell populations rather than true same-cell bivalency. Consider sorted-population or single-cell data (scATAC-seq, CUT&Tag) when available.

4. ChromHMM State Interpretation

ChromHMM states are probabilistic assignments, not deterministic annotations. The same genomic region may have different posterior probabilities for multiple states. A "Quiescent" call often means "low signal for all marks" -- this could be true quiescence or simply poor data quality in that region. Always cross-reference ChromHMM calls with individual mark tracks.

5. Broad Marks Require broadPeak Calls

H3K27me3, H3K9me3, and H3K36me3 form broad domains spanning tens to hundreds of kilobases. Use broadPeak (not narrowPeak) calls for these marks. NarrowPeak calls fragment broad domains into many small peaks and lose domain-level information critical for Polycomb and heterochromatin annotation.

6. H3K27ac vs. H3K27me3 Mutual Exclusivity

H3K27ac and H3K27me3 modify the same lysine residue and are biochemically mutually exclusive on the same histone tail. If both appear enriched at the same locus in bulk data, this reflects mixed cell populations. Do not interpret co-occurrence as a coherent chromatin state.

7. CUT&RUN and CUT&Tag Data

ENCODE increasingly includes CUT&RUN and CUT&Tag data alongside traditional ChIP-seq. These assays have lower background but different peak characteristics (sharper, lower read depth). Do not directly merge CUT&RUN peaks with ChIP-seq peaks without accounting for assay-specific biases. CUT&RUN data may also carry suspect regions not captured by the standard ENCODE blacklist (Nordin et al. 2023).

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Walkthrough: Building a Complete Epigenomic Profile for Pancreatic Islets

Goal: Assemble a comprehensive multi-mark epigenomic profile for a single tissue, combining histone modifications, chromatin accessibility, DNA methylation, and 3D genome data from ENCODE. Context: A complete epigenomic profile requires multiple complementary assays. This walkthrough shows how to systematically collect and integrate them.

Step 1: Survey available assay types for the tissue

encode_get_facets(facet_field="assay_title", organ="pancreas", organism="Homo sapiens")

Expected output:

{
  "facets": {
    "assay_title": {"Histone ChIP-seq": 25, "ATAC-seq": 6, "RNA-seq": 12, "WGBS": 4, "Hi-C": 2, "TF ChIP-seq": 8}
  }
}

Step 2: Collect the core marks

For a complete profile, gather:

encode_search_experiments(assay_title="Histone ChIP-seq", organ="pancreas", target="H3K27ac", organism="Homo sapiens")
encode_search_experiments(assay_title="Histone ChIP-seq", organ="pancreas", target="H3K4me3", organism="Homo sapiens")
encode_search_experiments(assay_title="Histone ChIP-seq", organ="pancreas", target="H3K27me3", organism="Homo sapiens")
encode_search_experiments(assay_title="ATAC-seq", organ="pancreas", organism="Homo sapiens")

Step 3: Track all profiling experiments

encode_track_experiment(accession="ENCSR100PAN", notes="Pancreas H3K27ac for epigenome profiling")

Expected output:

{"status": "tracked", "accession": "ENCSR100PAN", "notes": "Pancreas H3K27ac for epigenome profiling"}

Step 4: Generate collection summary

encode_summarize_collection()

Expected output:

{
  "total_tracked": 6,
  "by_assay": {"Histone ChIP-seq": 3, "ATAC-seq": 1, "WGBS": 1, "Hi-C": 1},
  "by_target": {"H3K27ac": 1, "H3K4me3": 1, "H3K27me3": 1}
}

Integration with downstream skills

• Individual marks feed into histone-aggregation for cross-sample merge
• Chromatin accessibility feeds into accessibility-aggregation
• Combined profile feeds into regulatory-elements for chromatin state segmentation
• Complete profile enables integrative-analysis for multi-mark analysis

Code Examples

1. Survey available marks for a tissue

encode_get_facets(facet_field="target.label", organ="pancreas", assay_title="Histone ChIP-seq", organism="Homo sapiens")

Expected output:

{
  "facets": {"target.label": {"H3K27ac": 5, "H3K4me3": 4, "H3K27me3": 3, "H3K4me1": 3, "H3K36me3": 2}}
}

2. Get experiment details for profiling

encode_get_experiment(accession="ENCSR100PAN")

Expected output:

{
  "accession": "ENCSR100PAN",
  "assay_title": "Histone ChIP-seq",
  "target": "H3K27ac",
  "biosample_summary": "pancreas",
  "replicates": 2
}

3. Summarize profiling collection

encode_summarize_collection()

Expected output:

{
  "total_tracked": 6,
  "by_assay": {"Histone ChIP-seq": 3, "ATAC-seq": 1, "WGBS": 1, "Hi-C": 1}
}

Integration

This skill produces...	Feed into...	Purpose
Multi-mark peak sets	histone-aggregation	Aggregate individual marks across experiments
Complete chromatin profiles	regulatory-elements	ChromHMM/chromatin state segmentation
Epigenomic landscape	integrative-analysis	Multi-mark integrative analysis
Tissue-specific marks	compare-biosamples	Compare profiles between tissues
Profiling experiment collection	visualization-workflow	Multi-track genome browser sessions
Active enhancer maps	peak-annotation	Assign enhancers to target genes
Methylation + histone data	methylation-aggregation	Correlate methylation with histone marks

Related Skills

Skill	When to Use
histone-aggregation	Merge peaks for a single histone mark across multiple experiments/donors into a union peak set
regulatory-elements	Identify and classify cis-regulatory elements (promoters, enhancers, insulators) using ENCODE cCRE catalog
quality-assessment	Evaluate ChIP-seq quality metrics (FRiP, NSC, RSC, NRF) and ENCODE audit flags before including experiments in the profile
compare-biosamples	Compare epigenomic profiles between two tissues or cell types to identify differential chromatin states
accessibility-aggregation	Merge ATAC-seq and DNase-seq peaks across experiments for comprehensive open chromatin maps
methylation-aggregation	Aggregate WGBS data across donors for per-CpG methylation maps and HMR/PMD identification
single-cell-encode	Single-cell epigenomic data resolves cell-type heterogeneity in bulk profiles
multi-omics-integration	Combine multiple data layers into a comprehensive regulatory landscape
disease-research	Epigenomic profiles are the foundation for disease regulatory models
variant-annotation	Variant annotation relies on the epigenomic profile for functional context
hic-aggregation	Hi-C data complements the 3D genome structure dimension of the profile
data-provenance	Document all profile assembly parameters, tool versions, and mark selections
pipeline-guide	Guidance for ChromHMM setup and other profile assembly pipelines
ucsc-browser	Retrieve ENCODE tracks and cCRE data from UCSC for profile visualization
ensembl-annotation	Ensembl Regulatory Build provides independent regulatory annotations to compare with ENCODE profiles
visualization-workflow	Visualize epigenomic profiles with genome browser tracks, heatmaps, and signal plots
pipeline-chipseq	Process raw ChIP-seq data through the full ENCODE-aligned pipeline
publication-trust	Verify literature claims backing analytical decisions

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Presenting Results

• Present epigenomic profiles as a mark x tissue matrix showing signal presence/absence. Summarize chromatin states using the 15-state ChromHMM model. Suggest: "Would you like to compare with another tissue?"

For the request: "$ARGUMENTS"