Accessibility Aggregation

When to Use

• User wants to combine ATAC-seq or DNase-seq peaks across multiple experiments for a tissue
• User asks "where is chromatin accessible in my tissue?" or "build an open chromatin map"
• User needs to merge accessibility data from different labs, donors, or platforms (ATAC vs DNase)
• User wants a comprehensive set of open chromatin regions for regulatory element discovery
• Example queries: "aggregate ATAC-seq peaks for pancreas", "combine DNase-seq across donors", "find all accessible regions in liver"

Aggregate Chromatin Accessibility Peaks Across Studies

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.

Scientific Rationale

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

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.

ATAC-seq vs DNase-seq

Both measure open chromatin but with different biases:

Literature Support

• Corces et al. 2017 (Nature Methods, 733 citations): Established that ATAC-seq and DNase-seq identify largely overlapping accessible regions, with ATAC capturing ~75% of DNase sites. Both are valid for union maps.
• ENCODE Blacklist (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Comprehensive set of problematic genomic regions to filter. Essential for all functional genomics analyses. DOI
• F-Seq2 (Zhao & Boyle 2020, NAR Genomics): Improved peak caller for DNase-seq and ATAC-seq with proper test statistics for IDR compatibility.
• ENCODE Phase 3 (Gorkin et al. 2020, Nature, 301 citations): Integrated accessibility data with histone marks across tissues for chromatin state annotation.

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.

Step 1: Find All Available Accessibility Data

# ATAC-seq
encode_search_experiments(
    assay_title="ATAC-seq",
    organ="pancreas",
    biosample_type="tissue",
    limit=100
)

# DNase-seq
encode_search_experiments(
    assay_title="DNase-seq",
    organ="pancreas",
    biosample_type="tissue",
    limit=100
)

Present a summary to the user:

• Total ATAC-seq experiments
• Total DNase-seq experiments
• Labs represented
• Whether to use one or both assay types

Combining ATAC + DNase?

Ask the user:

• Same assay only (purest comparison, no cross-platform effects)
• Both assays combined (maximum coverage, slight platform variation)

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

Step 2: Quality-Gate Each Experiment

encode_get_experiment(accession="ENCSR...")

ATAC-seq Quality Checks

• Audit status: no ERROR flags
• Has IDR thresholded peaks
• Low mitochondrial read fraction (ENCODE pipeline removes these)
• Good TSS enrichment score
• Nucleosome-free fragment enrichment visible

DNase-seq Quality Checks

• Audit status: no ERROR flags
• Has Hotspot2 peaks or IDR thresholded peaks
• Adequate sequencing depth (20M+ mapped reads)
• Signal-to-noise ratio

Track all included experiments:

encode_track_experiment(accession="ENCSR...")

Step 3: Download Peak Files

For each experiment:

# ATAC-seq — IDR thresholded peaks
encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38"
)

# DNase-seq — may use different output types
encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="peaks",
    assembly="GRCh38"
)

Prefer preferred_default=True files.

encode_download_files(
    file_accessions=["ENCFF...", ...],
    download_dir="/path/to/data/accessibility",
    organize_by="flat"
)

Step 4: Per-Sample Noise Filtering

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

# Download from: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz
bedtools intersect -a sample.narrowPeak -b hg38-blacklist.v2.bed -v > sample.filtered.narrowPeak

4b. SignalValue Filtering (Perna et al. 2024)

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

# Per-sample: remove bottom 25% by signalValue (true distribution quantile)
TOTAL=$(wc -l < sample.filtered.narrowPeak)
LINE_25=$(echo "$TOTAL" | awk '{printf "%d", $1 * 0.25}')
THRESHOLD=$(sort -k7,7n sample.filtered.narrowPeak | awk -v line="$LINE_25" 'NR==line{print $7}')
awk -v t="$THRESHOLD" '$7 >= t' sample.filtered.narrowPeak > sample.qfiltered.narrowPeak

4c. ATAC-specific: Remove Sub-nucleosomal Artifacts (optional)

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

awk '($3-$2) >= 50' sample.qfiltered.narrowPeak > sample.clean.narrowPeak

Step 5: Union Merge

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

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

# Tag each sample's peaks with a unique sample ID
awk -v sid="atac_s1" 'BEGIN{OFS="\t"} {$4=sid; print}' atac_sample1.qfiltered.narrowPeak > atac_s1.tagged.bed
awk -v sid="dnase_s1" 'BEGIN{OFS="\t"} {$4=sid; print}' dnase_sample1.qfiltered.narrowPeak > dnase_s1.tagged.bed
# ... repeat for all samples

# Concatenate all tagged peaks (ATAC + DNase combined or separate)
cat *.tagged.bed > all_accessibility.bed

# Sort
bedtools sort -i all_accessibility.bed > all_accessibility.sorted.bed

# Union merge — count UNIQUE SAMPLES (not peaks)
bedtools merge \
    -i all_accessibility.sorted.bed \
    -c 4,7,9 \
    -o count_distinct,max,max \
    > union_accessible_regions.bed
# Columns: chr, start, end, n_unique_samples, max_signalValue, max_qValue

If Tracking Assay Source

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

# Add assay tag to each peak before concatenation
awk '{print $0"\tATAC"}' atac_peaks.bed > tagged.bed
awk '{print $0"\tDNase"}' dnase_peaks.bed >> tagged.bed

# After merge, use bedtools multiIntersect to track sources
bedtools multiIntersect \
    -i atac_sample1.bed atac_sample2.bed dnase_sample1.bed ... \
    -header \
    -names ATAC_1 ATAC_2 DNase_1 ... \
    > multi_intersect.bed

Step 6: Confidence Annotation

Same logic as histone aggregation. Given N total samples:

awk -v N=8 '{
    if ($4 >= N*0.5) conf="HIGH";
    else if ($4 >= 2) conf="SUPPORTED";
    else conf="SINGLETON";
    print $0"\t"conf"\t"$4"/"N
}' union_accessible_regions.bed > union_accessible_regions.annotated.bed

Step 7: Log Provenance

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

Step 8: Summary Statistics

Report to the user:

• Total input experiments: N (ATAC: X, DNase: Y)
• Experiments passing QC: M
• Total peaks before merge: X
• Union peaks after merge: Y
• High-confidence regions: Z (≥50% support)
• Supported regions: W (2+ support)
• Singleton regions: V (1 sample only)
• Genome coverage: bp covered / total genome
• Overlap between ATAC-only and DNase-only peaks (if both assays used)

Pitfalls Specific to Accessibility Data

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

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

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.

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.

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.

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

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.

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.

Walkthrough: Building a Pan-Donor Accessibility Map for Brain Cortex

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

Step 1: Search for brain ATAC-seq experiments

encode_search_experiments(
  assay_title="ATAC-seq",
  organ="brain"
)

Expected output:

{
  "total": 24,
  "experiments": [
    {"accession": "ENCSR001BRN", "biosample_summary": "brain cortex tissue male adult (53 years)"},
    {"accession": "ENCSR002BRN", "biosample_summary": "brain cortex tissue female adult (49 years)"}
  ]
}

Step 2: Download narrowPeak files

encode_search_files(
  assay_title="ATAC-seq",
  organ="brain",
  file_format="bed",
  output_type="IDR thresholded peaks",
  assembly="GRCh38"
)

Expected output:

{
  "total": 8,
  "files": [{"accession": "ENCFF001ATQ", "output_type": "IDR thresholded peaks", "assembly": "GRCh38"}]
}

Step 3: Merge into union peak set

cat *.narrowPeak | sort -k1,1 -k2,2n | bedtools merge -i - -c 4,5 -o count,mean > union_atac_brain.bed

Interpretation: Union peaks represent all genomic positions where chromatin is accessible in brain cortex. Peaks found in all 4 donors are constitutive regulatory elements.

Code Examples

1. Find accessibility data for aggregation

encode_get_facets(organ="pancreas", assay_title="ATAC-seq")

Expected output:

{
  "facets": {"biosample_term_name": {"pancreas": 4, "pancreatic islet": 3}, "status": {"released": 6}}
}

Integration

Related Skills

• histone-aggregation: Same union approach for histone ChIP-seq narrowPeak data
• methylation-aggregation: Different approach (averaging) for continuous methylation signal; HMRs + accessibility peaks mark active regulatory elements
• hic-aggregation: Union approach for BEDPE chromatin loops; loops often anchor at accessible regions
• regulatory-elements: Use union accessibility maps to define active regulatory elements with histone mark combinations
• motif-analysis: Find enriched TF motifs in accessible regions using HOMER and MEME
• pipeline-atacseq: Process raw ATAC-seq data through the full ENCODE-aligned pipeline
• batch-analysis: Batch processing workflows for systematic accessibility aggregation
• publication-trust: Verify literature claims backing analytical decisions

Presenting Results

• Present merged accessibility regions as: chr | start | end | assay_type | sample_count. Show ATAC vs DNase contribution. Suggest: "Would you like to run motif analysis on these accessible regions?"

For the request: "$ARGUMENTS"