{"slug":"accessibility-aggregation-2","title":"accessibility-aggregation","summary":"Build comprehensive chromatin accessibility maps by aggregating ATAC-seq and DNase-seq narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer \"where is chromatin accessible in my tissue?\" by combining peak calls into a union peak s","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-22T13:25:39.260085Z","repo":{"url":"https://github.com/ammawla/encode-toolkit","stars":20,"forks":5,"license":"AGPL-3.0","updatedAt":"2026-09-21T05:10:53Z"},"bodyHtml":"
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name: accessibility-aggregation\ndescription: Build comprehensive chromatin accessibility maps by aggregating ATAC-seq and DNase-seq narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer \"where is chromatin accessible in my tissue?\" by combining peak calls into a union peak set. Handles cross-lab variation, ATAC vs DNase platform differences, and ENCODE blocklist filtering.

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When to Use

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Aggregate Chromatin Accessibility Peaks Across Studies

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Build a comprehensive map of open chromatin for a tissue/cell type by merging ATAC-seq and/or DNase-seq narrowPeak files from multiple ENCODE experiments.

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Scientific Rationale

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The question: \"Where is chromatin accessible in my tissue?\"

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Like histone marks, chromatin accessibility is a detection question. An open chromatin region detected in one donor but not another is still a real accessible site — individual variation, sequencing depth, and technical factors explain absence. We want the union of all detections.

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ATAC-seq vs DNase-seq

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Both measure open chromatin but with different biases:

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PropertyATAC-seqDNase-seq
MethodTn5 transposase insertionDNase I hypersensitivity
Input required~50K cells~1M cells
ResolutionHighHigh
GC biasModerate (Tn5 preference)Low
Mitochondrial readsHigh (filter needed)None
ENCODE availabilityNewer experimentsExtensive historical catalog
ComparabilityGenerally comparable at open regions
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Literature Support

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Recommendation: If combining ATAC-seq and DNase-seq peaks, treat them as equivalent signal sources for accessibility. The union is appropriate because both detect the same biological signal (open chromatin) through different enzymatic mechanisms.

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Step 1: Find All Available Accessibility Data

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# ATAC-seq\nencode_search_experiments(\n    assay_title=\"ATAC-seq\",\n    organ=\"pancreas\",\n    biosample_type=\"tissue\",\n    limit=100\n)\n\n# DNase-seq\nencode_search_experiments(\n    assay_title=\"DNase-seq\",\n    organ=\"pancreas\",\n    biosample_type=\"tissue\",\n    limit=100\n)\n
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Present a summary to the user:

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Combining ATAC + DNase?

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Ask the user:

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For a comprehensive accessibility catalog, combining both is scientifically justified.

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Step 2: Quality-Gate Each Experiment

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encode_get_experiment(accession=\"ENCSR...\")\n
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ATAC-seq Quality Checks

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DNase-seq Quality Checks

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Track all included experiments:

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encode_track_experiment(accession=\"ENCSR...\")\n
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Step 3: Download Peak Files

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For each experiment:

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# ATAC-seq — IDR thresholded peaks\nencode_list_files(\n    experiment_accession=\"ENCSR...\",\n    file_format=\"bed\",\n    output_type=\"IDR thresholded peaks\",\n    assembly=\"GRCh38\"\n)\n\n# DNase-seq — may use different output types\nencode_list_files(\n    experiment_accession=\"ENCSR...\",\n    file_format=\"bed\",\n    output_type=\"peaks\",\n    assembly=\"GRCh38\"\n)\n
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Prefer preferred_default=True files.

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encode_download_files(\n    file_accessions=[\"ENCFF...\", ...],\n    download_dir=\"/path/to/data/accessibility\",\n    organize_by=\"flat\"\n)\n
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Validate the downloaded files before filtering. Pass --assay so the peak-width\nand Tn5 checks match the assay; gzipped inputs are read directly.

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python3 scripts/validate_peaks.py sample.narrowPeak [--assay atac|dnase|unknown] [--blacklist hg38-blacklist.v2.bed]\n
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Step 4: Per-Sample Noise Filtering

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4a. ENCODE Blocklist Filtering (Amemiya et al. 2019)

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# Download from: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz\ngunzip -k hg38-blacklist.v2.bed.gz\nbedtools intersect -a sample.narrowPeak -b hg38-blacklist.v2.bed -v > sample.filtered.narrowPeak\n
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4b. SignalValue Filtering (Perna et al. 2024)

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Same logic as histone aggregation — filter per-sample to top 75% by signalValue (column 7):

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# Per-sample: remove bottom 25% by signalValue (true distribution quantile)\nTOTAL=$(wc -l < sample.filtered.narrowPeak)\nLINE_25=$(echo \"$TOTAL\" | awk '{printf \"%d\", $1 * 0.25}')\nTHRESHOLD=$(sort -k7,7n sample.filtered.narrowPeak | awk -v line=\"$LINE_25\" 'NR==line{print $7}')\nawk -v t=\"$THRESHOLD\" '$7 >= t' sample.filtered.narrowPeak > sample.qfiltered.narrowPeak\n
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4c. ATAC-specific: Remove Sub-nucleosomal Artifacts (optional)

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For ATAC-seq, very narrow peaks (<50bp) can be Tn5 insertion artifacts:

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awk '($3-$2) >= 50' sample.qfiltered.narrowPeak > sample.clean.narrowPeak\n
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Step 5: Union Merge

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Accessibility peaks are narrow/point-source (like H3K4me3). Use default merge (overlap only, no gap tolerance).

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CRITICAL: Tag peaks by sample before concatenation to count unique SAMPLES, not overlapping peaks:

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# Tag each sample's peaks with a unique sample ID\nawk -v sid=\"atac_s1\" 'BEGIN{OFS=\"\\t\"} {$4=sid; print}' atac_sample1.qfiltered.narrowPeak > atac_s1.tagged.bed\nawk -v sid=\"dnase_s1\" 'BEGIN{OFS=\"\\t\"} {$4=sid; print}' dnase_sample1.qfiltered.narrowPeak > dnase_s1.tagged.bed\n# ... repeat for all samples\n\n# Concatenate all tagged peaks (ATAC + DNase combined or separate)\ncat *.tagged.bed > all_accessibility.bed\n\n# Sort\nbedtools sort -i all_accessibility.bed > all_accessibility.sorted.bed\n\n# Union merge — count UNIQUE SAMPLES (not peaks)\nbedtools merge \\\n    -i all_accessibility.sorted.bed \\\n    -c 4,7,9 \\\n    -o count_distinct,max,max \\\n    > union_accessible_regions.bed\n# Columns: chr, start, end, n_unique_samples, max_signalValue, max_qValue\n
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If Tracking Assay Source

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To annotate whether peaks came from ATAC, DNase, or both:

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# Add assay tag to each peak before concatenation\nawk '{print $0\"\\tATAC\"}' atac_peaks.bed > tagged.bed\nawk '{print $0\"\\tDNase\"}' dnase_peaks.bed >> tagged.bed\n\n# After merge, use bedtools multiIntersect to track sources\nbedtools multiIntersect \\\n    -i atac_sample1.bed atac_sample2.bed dnase_sample1.bed ... \\\n    -header \\\n    -names ATAC_1 ATAC_2 DNase_1 ... \\\n    > multi_intersect.bed\n
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Step 6: Confidence Annotation

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Same logic as histone aggregation. Given N total samples:

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ConfidenceCriteriaInterpretation
High≥50% of samplesConstitutive accessible region
Supported2+ samplesLikely real, some variation
Singleton1 sample onlyKeep — may be individual-specific or condition-specific
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awk -v N=8 '{\n    if ($4 >= N*0.5) conf=\"HIGH\";\n    else if ($4 >= 2) conf=\"SUPPORTED\";\n    else conf=\"SINGLETON\";\n    print $0\"\\t\"conf\"\\t\"$4\"/\"N\n}' union_accessible_regions.bed > union_accessible_regions.annotated.bed\n
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Step 7: Log Provenance

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encode_log_derived_file(\n    file_path=\"/path/to/union_accessible_regions.annotated.bed\",\n    source_accessions=[\"ENCSR...\", \"ENCSR...\", ...],\n    description=\"Union chromatin accessibility peaks (ATAC-seq + DNase-seq) across N pancreas samples\",\n    file_type=\"aggregated_accessibility\",\n    tool_used=\"bedtools merge v2.31.0\",\n    parameters=\"blocklist filtered, signalValue >= 25th pctl per sample, ATAC min width 50bp, bedtools merge -d 0\"\n)\n
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Step 8: Summary Statistics

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Report to the user:

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Pitfalls Specific to Accessibility Data

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  1. ATAC mitochondrial reads: ENCODE pipeline removes these, but verify in QC metrics. High mitochondrial fraction indicates poor nuclear chromatin enrichment.

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  2. Tn5 sequence bias: ATAC-seq Tn5 has mild sequence preference. For union maps this is acceptable — bias affects peak intensity, not presence.

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  3. DNase hypersensitivity saturation: Deeply sequenced DNase-seq detects more sites. Shallowly sequenced samples contribute fewer peaks but are not wrong — they just miss weaker sites.

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  4. Promoter enrichment: Both assays are enriched at promoters. When comparing accessibility across tissues, note that promoter accessibility is largely constitutive while enhancer accessibility is tissue-specific.

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  5. Cell-type heterogeneity in tissue samples: Bulk ATAC/DNase from tissue captures accessibility across ALL cell types. A peak may represent a minor cell population. This is correct for a tissue-level map but important to note.

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  6. Do NOT mix assemblies: All files must be GRCh38 or all hg19. Use encode_compare_experiments to verify.

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    7. \n

  7. Peak summits lost after merge: NarrowPeak column 10 (summit offset) is discarded by bedtools merge. If you need summits for motif analysis, extract them before merging and map back afterward.

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    8. \n

  8. CUT&RUN/CUT&Tag accessibility data: If ENCODE adds CUT&RUN-based accessibility data in the future, apply the CUT&RUN suspect list (Nordin et al. 2023, Genome Biology) in addition to the ENCODE blacklist.

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    9. \n
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Walkthrough: Building a Pan-Donor Accessibility Map for Brain Cortex

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Goal: Merge ATAC-seq peaks from 4 brain cortex experiments into a union accessibility map.\nContext: User needs comprehensive open chromatin regions for regulatory element discovery.

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Step 1: Search for brain ATAC-seq experiments

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encode_search_experiments(\n  assay_title=\"ATAC-seq\",\n  organ=\"brain\"\n)\n
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Expected output (one entry per experiment; fields abridged):

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{\n  \"results\": [\n    {\n      \"accession\": \"ENCSR001BRN\",\n      \"assay_title\": \"ATAC-seq\",\n      \"biosample_summary\": \"brain cortex tissue male adult (53 years)\",\n      \"organ\": \"brain\",\n      \"biosample_type\": \"tissue\",\n      \"assembly\": [\"GRCh38\"],\n      \"file_count\": 18\n    }\n  ],\n  \"total\": 24,\n  \"limit\": 25,\n  \"offset\": 0,\n  \"has_more\": false,\n  \"next_offset\": null\n}\n
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Step 2: Download narrowPeak files

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encode_search_files(\n  assay_title=\"ATAC-seq\",\n  organ=\"brain\",\n  file_format=\"bed\",\n  output_type=\"IDR thresholded peaks\",\n  assembly=\"GRCh38\"\n)\n
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Expected output (fields abridged):

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{\n  \"results\": [\n    {\n      \"accession\": \"ENCFF001ATQ\",\n      \"file_format\": \"bed\",\n      \"file_type\": \"bed narrowPeak\",\n      \"output_type\": \"IDR thresholded peaks\",\n      \"assembly\": \"GRCh38\",\n      \"file_size\": 1258291,\n      \"file_size_human\": \"1.2 MB\",\n      \"experiment_accession\": \"ENCSR001BRN\",\n      \"preferred_default\": true\n    }\n  ],\n  \"total\": 8,\n  \"limit\": 25,\n  \"offset\": 0,\n  \"has_more\": false,\n  \"next_offset\": null\n}\n
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Step 3: Merge into union peak set

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cat *.narrowPeak | sort -k1,1 -k2,2n | bedtools merge -i - -c 4,5 -o count,mean > union_atac_brain.bed\n
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Interpretation: Union peaks represent all genomic positions where chromatin is accessible in brain cortex. Peaks found in all 4 donors are constitutive regulatory elements.

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Code Examples

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1. Find accessibility data for aggregation

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encode_get_facets(organ=\"pancreas\", assay_title=\"ATAC-seq\")\n
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Expected output (top-level keys are ENCODE facet field names; which ones appear depends on the filters):

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{\n  \"assay_title\": [{\"term\": \"ATAC-seq\", \"count\": 7}],\n  \"biosample_ontology.term_name\": [\n    {\"term\": \"pancreas\", \"count\": 4},\n    {\"term\": \"pancreatic islet\", \"count\": 3}\n  ],\n  \"status\": [{\"term\": \"released\", \"count\": 7}]\n}\n
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Integration

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This skill produces...Feed into...Using tool/skill
Union open chromatin map (BED)Enhancer identificationregulatory-elements skill
Accessible regions for motif analysisTF motif discoverymotif-analysis skill
Tissue accessibility catalogCross-tissue comparisoncompare-biosamples skill
Open chromatin at variant sitesVariant functional annotationvariant-annotation skill
Accessible peak coordinatesVisualization signal anchorsvisualization-workflow skill
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Related Skills

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Presenting Results

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For the request: \"$ARGUMENTS\"

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