ENCODE ChIP-seq Pipeline

When to Use

• User wants to run a ChIP-seq processing pipeline from FASTQ to peaks and signal tracks
• User asks about "ChIP-seq pipeline", "MACS2", "peak calling", "BWA alignment for ChIP", or "IDR"
• User needs to process histone or TF ChIP-seq data following ENCODE standards
• Example queries: "process my ChIP-seq FASTQs", "run the ENCODE ChIP-seq pipeline", "call peaks from ChIP-seq with MACS2 and IDR"

Execute the ENCODE ChIP-seq processing pipeline from raw FASTQ files through peak calling, IDR analysis, and signal track generation. This skill provides a Nextflow DSL2 implementation following ENCODE uniform analysis standards.

Overview

The pipeline processes chromatin immunoprecipitation sequencing data through quality control, adapter trimming, alignment to a reference genome, filtering and duplicate removal, blacklist filtering, peak calling with MACS2, an IDR comparison for every pair of replicates, FRiP calculation, and signal track generation.

The same workflow handles transcription factor (TF) ChIP-seq and histone modification ChIP-seq. The peak mode is chosen once per run with --peak_type narrow|broad; it applies to every sample in that run. To process narrow and broad targets together, run the workflow twice with different --peak_type and --outdir values.

Key Literature

Reference	Journal	Year	DOI	Relevance
Landt et al. "ChIP-seq guidelines and practices"	Genome Research	2012	10.1101/gr.136184.111	ENCODE ChIP-seq standards (~4,000 citations)
ENCODE Project Consortium "Expanded encyclopaedias"	Nature	2020	10.1038/s41586-020-2493-4	ENCODE Phase 3 standards
Zhang et al. "Model-based Analysis of ChIP-Seq (MACS)"	Genome Biology	2008	10.1186/gb-2008-9-9-r137	Peak caller (~7,000 citations)
Li et al. "Measuring reproducibility (IDR)"	Annals of Applied Statistics	2011	10.1214/11-AOAS466	Replicate consistency (~1,500 citations)
Amemiya et al. "ENCODE Blacklist"	Scientific Reports	2019	10.1038/s41598-019-45839-z	Artifact regions (~1,372 citations)
Ramachandran et al. "phantompeakqualtools"	—	2013	—	NSC/RSC strand correlation metrics (manual step, see below)

Pipeline Stages

FASTQ ──> FastQC / Trim Galore ──> BWA-MEM ──> Samtools Filter ──> Picard MarkDuplicates
  │                                            (-F 1804/-F 1028, -q 30)   (duplicates REMOVED)
  │                                                                       │
  │           ┌───────────────────────────────────────────────────────────┘
  │           v
  │     Blacklist Filter ──> MACS2 Peak Calling ──> IDR (narrow runs only, every pair)
  │       (applied to BAM)          │
  │                                 ├──> Signal Tracks (bdgcmp + bedGraphToBigWig)
  │                                 └──> FRiP (bedtools intersect + samtools view -c)
  v
 QC reports ──────────────────────────────────> MultiQC

Control libraries, when --control is given, travel through stages 1-3 alongside the ChIP samples with a CONTROL_ prefix and are split off before peak calling; MACS2 pools them with -c.

Stage Summary

Stage	Tool	Input	Output	Reference
1. QC & Trimming	FastQC, Trim Galore	Raw FASTQ	Trimmed FASTQ, FastQC reports	references/01-qc-trimming.md
2. Alignment	BWA-MEM, samtools	Trimmed FASTQ	Sorted BAM, flagstat	references/02-alignment.md
3. Filtering	samtools, Picard, bedtools	Sorted BAM	Deduplicated, blacklist-filtered BAM	references/03-filtering.md
4. Peak Calling & IDR	MACS2, IDR	Filtered BAM	narrowPeak/broadPeak, one <sampleA>_vs_<sampleB>.idr_peaks.txt per replicate pair	references/04-analysis.md
5. Signal, FRiP & QC report	MACS2 bdgcmp, bedGraphToBigWig, bedtools, samtools, MultiQC	MACS2 bedGraphs, final BAM, peaks, QC logs	bigWig, <sample>.frip_mqc.tsv, multiqc_report.html	references/05-qc-metrics.md

Input Requirements

Required

• Treatment FASTQ (--reads): ChIP sample reads, gzipped. A Nextflow file-pair glob, e.g. 'fastq/chip_*_R{1,2}.fq.gz'. Paired-end by default; add --single_end for SE data.
• Chromosome sizes (--chrom_sizes): two-column <chrom>\t<size> file used by bedGraphToBigWig. The workflow stops immediately if it is missing. Build it with samtools faidx GRCh38.fa && cut -f1,2 GRCh38.fa.fai > GRCh38.chrom.sizes.
• BWA index directory (--bwa_index): a directory holding <genome>.fa plus its BWA index files. BWA is invoked as bwa mem ... <dir>/GRCh38.fa, so the directory must contain GRCh38.fa, GRCh38.fa.amb, GRCh38.fa.ann, GRCh38.fa.bwt, GRCh38.fa.pac and GRCh38.fa.sa. Build it once with bwa index GRCh38_index/GRCh38.fa. If the flag is omitted, the workflow looks for ./<genome>_index in the launch directory. The workflow does not build or download the index.

Optional

• Control FASTQ (--control): input/IgG reads, same glob form. Strongly recommended for meaningful enrichment, but the workflow runs without one; MACS2 then uses its local lambda background model.
• Blacklist (--blacklist): defaults to the ENCODE Blacklist v2 URL for --genome.

There is no sample sheet. Inputs are globs, and every sample in a run shares one --peak_type and one --genome.

The --reads and --control globs must not match the same files. A glob such as 'fastq/*_R{1,2}.fq.gz' also matches fastq/input_R{1,2}.fq.gz, so the control library would be processed twice: once as a control and once as a ChIP sample, producing peak calls for the input itself and possibly feeding them to IDR. Use a distinct prefix ('fastq/chip_*_R{1,2}.fq.gz') or keep controls in a separate directory.

Narrow vs Broad Peak Mode Decision

Peak Type	Targets	MACS2 flags used by the workflow
Narrow (--peak_type narrow)	H3K4me3, H3K4me1, H3K27ac, H3K9ac, all TFs, CTCF	--qvalue 0.05 --call-summits
Broad (--peak_type broad)	H3K27me3, H3K36me3, H3K9me3, H3K79me2	--qvalue 0.05 --broad --broad-cutoff 0.1

IDR runs only for --peak_type narrow. Broad runs produce no peaks/idr/ output.

Parameters

Pipeline parameters (main.nf)

Parameter	Default	Description
--reads	none (required)	Glob for the ChIP FASTQ file pairs
--chrom_sizes	none (required)	Two-column chromosome sizes file for the bigWig tracks
--bwa_index	./<genome>_index	Directory holding <genome>.fa and its BWA index files
--control	none	Glob for control/input FASTQ file pairs; optional
--genome	GRCh38	GRCh38 or mm10; sets the MACS2 genome size, the default blacklist and the default index directory name (<genome>_index)
--peak_type	narrow	narrow or broad; one value for the whole run
--blacklist	ENCODE Blacklist v2 URL for --genome	BED (or .bed.gz) of artifact regions removed from the BAM
--single_end	false	Treat --reads/--control as single-end files
--skip_idr	false	Skip the IDR step
--outdir	results	Where results are published

Infrastructure parameters (nextflow.config)

Parameter	Default	Description
--container	encode-toolkit/pipeline-chipseq:1.0.0	Image built from scripts/Dockerfile. Pass a registry image for gcp/aws, or a .sif file for slurm
--max_cpus, --max_memory, --max_time	16, 64.GB, 24.h	Upper bounds applied to every process
--slurm_queue, --slurm_account	normal, none	SLURM partition and account
--gcp_project, --gcp_workdir	none (both required for -profile gcp)	Google Cloud project and gs:// work directory
--gcp_location, --gcp_disk	us-central1, 200.GB	Google Batch region and per-task disk
--aws_queue, --aws_workdir	none (both required for -profile aws)	AWS Batch job queue and s3:// work directory
--aws_region, --aws_cli_path	us-east-1, /home/ec2-user/miniconda/bin/aws	AWS region, and the AWS CLI path inside the Batch AMI

Profiles are local, slurm, gcp and aws.

QC Thresholds

These thresholds follow ENCODE standards established by Landt et al. 2012 and the ENCODE DCC quality metrics documentation. The workflow itself computes only the metrics marked "workflow" below; the rest are community thresholds you evaluate from manual steps documented in references/05-qc-metrics.md.

Metric	Threshold	Computed by	Source
Total sequenced reads	>=20M (TF), >=45M (histone)	workflow (FastQC, flagstat)	Landt 2012
Mapping rate	>80%	workflow (samtools flagstat)	ENCODE
Duplication rate	<30%	workflow (Picard dup_metrics.txt)	ENCODE
IDR peaks at 0.05	>20,000 (TF)	workflow (peaks/idr/<sampleA>_vs_<sampleB>.idr_peaks.txt)	ENCODE
NRF (non-redundant fraction)	>=0.8	manual	ENCODE
PBC1 (PCR bottleneck coeff 1)	>=0.8	manual	ENCODE
PBC2 (PCR bottleneck coeff 2)	>=3	manual	ENCODE
NSC (normalized strand coeff)	>1.05	manual (phantompeakqualtools)	phantompeakqualtools
RSC (relative strand corr)	>0.8	manual (phantompeakqualtools)	phantompeakqualtools
FRiP (fraction reads in peaks)	>=1%, i.e. >=0.01 in the file	workflow (qc/<sample>.frip_mqc.tsv)	Landt 2012
Mitochondrial fraction	<5%	manual (samtools idxstats)	ENCODE

Interpreting QC: Traffic Light System

Color	Meaning	Action
Green	All metrics pass	Proceed to analysis
Yellow	1-2 metrics marginal	Review library prep, may be usable
Red	Multiple failures	Do not use; re-do experiment

Important: No single metric is sufficient. Interpret QC collectively. A sample with borderline NRF but excellent FRiP may still be usable.

Execution

Quick Start (Local Docker)

nextflow run scripts/main.nf \
  -profile local \
  --reads 'fastq/chip_*_R{1,2}.fq.gz' \
  --control 'fastq/input_*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --peak_type narrow \
  --bwa_index GRCh38_index \
  --chrom_sizes GRCh38.chrom.sizes \
  --blacklist hg38-blacklist.v2.bed.gz \
  --outdir results/

The --reads and --control globs use different prefixes so no FASTQ is picked up twice. --blacklist is optional; without it the workflow downloads the ENCODE Blacklist v2 for --genome.

SLURM HPC

The slurm profile runs through Singularity, so pass a local image file rather than the default Docker image name:

singularity build pipeline-chipseq.sif docker-daemon://encode-toolkit/pipeline-chipseq:1.0.0

nextflow run scripts/main.nf \
  -profile slurm \
  --container /path/to/pipeline-chipseq.sif \
  --slurm_queue normal \
  --reads 'fastq/chip_*_R{1,2}.fq.gz' \
  --control 'fastq/input_*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --peak_type narrow \
  --bwa_index GRCh38_index \
  --chrom_sizes GRCh38.chrom.sizes \
  --outdir results/

Cloud

# Google Cloud Batch
nextflow run scripts/main.nf -profile gcp \
    --container us-docker.pkg.dev/<project>/<repo>/pipeline-chipseq:1.0.0 \
    --gcp_project <project> \
    --gcp_workdir gs://<bucket>/work \
    --reads 'gs://<bucket>/fastq/chip_*_R{1,2}.fq.gz' \
    --control 'gs://<bucket>/fastq/input_*_R{1,2}.fq.gz' \
    --genome GRCh38 \
    --peak_type narrow \
    --bwa_index gs://<bucket>/reference/GRCh38_index \
    --chrom_sizes gs://<bucket>/reference/GRCh38.chrom.sizes \
    --outdir gs://<bucket>/results

# AWS Batch
nextflow run scripts/main.nf -profile aws \
    --container <account>.dkr.ecr.<region>.amazonaws.com/pipeline-chipseq:1.0.0 \
    --aws_queue <job-queue> \
    --aws_workdir s3://<bucket>/work \
    --reads 's3://<bucket>/fastq/chip_*_R{1,2}.fq.gz' \
    --control 's3://<bucket>/fastq/input_*_R{1,2}.fq.gz' \
    --genome GRCh38 \
    --peak_type narrow \
    --bwa_index s3://<bucket>/reference/GRCh38_index \
    --chrom_sizes s3://<bucket>/reference/GRCh38.chrom.sizes \
    --outdir s3://<bucket>/results

--outdir only sets where results are published; Google Batch and AWS Batch stage every task through the work directory, and the workflow stops with an error if it or the project/queue is missing.

Cloud Cost Estimates

Platform	Instance	Cost/Sample	Time/Sample	Notes
GCP	n1-standard-8	~$2-5	2-4 hours	Spot VMs enabled in the gcp profile
AWS	m5.2xlarge	~$2-5	2-4 hours	Spot instances recommended
Local	8 cores, 32GB	$0	3-6 hours	Docker required
SLURM	8 cores, 32GB	Varies	2-4 hours	Singularity image required

Output Directory Structure

results/
  fastqc/                       # FastQC reports for raw and trimmed reads (.html, .zip)
  trimmed/                      # Trimmed FASTQ (*_val_1.fq.gz / *_val_2.fq.gz) + trimming reports
  aligned/                      # <sample>.bam, .bam.bai, <sample>.flagstat.txt
  filtered/                     # <sample>.dup_metrics.txt, <sample>.final.bam(.bai),
                                #   <sample>.final.flagstat.txt
  peaks/
    narrow/                     # --peak_type narrow: <sample>_peaks.narrowPeak, _summits.bed,
                                #   _peaks.xls, _treat_pileup.bdg, _control_lambda.bdg
    broad/                      # --peak_type broad: _peaks.broadPeak, _peaks.gappedPeak,
                                #   _peaks.xls, the same bedGraphs
    idr/                        # <sampleA>_vs_<sampleB>.idr_peaks.txt (+ .png), one file
                                #   per replicate pair; narrow runs only
  signal/                       # <sample>.fc.bw, <sample>.pval.bw
  qc/
    <sample>.frip_mqc.tsv       # Peak set / FRiP / reads_in_peaks / total_reads
    multiqc/                    # multiqc_report.html, multiqc_data/
  pipeline_info/                # timeline.html, report.html, trace.txt

Only one of peaks/narrow/ and peaks/broad/ exists per run, matching --peak_type. When --control is given, the control libraries also appear in aligned/, filtered/ and fastqc/ under a CONTROL_<name> prefix. They are not peak-called, so no CONTROL_<name>.frip_mqc.tsv is written.

Common Pitfalls

1. Overlapping --reads and --control globs

The most common silent failure. If both globs match the same FASTQ, the control is also treated as a ChIP sample: MACS2 calls peaks on the input library, IDR pairs those peaks with every real replicate, and a FRiP row is written for it. Nothing errors. Use non-overlapping globs.

2. Missing Input Control

ChIP-seq is far more interpretable with a matched input (or IgG) control. Without one, MACS2 falls back to its local lambda background model and false positive rates rise. The workflow does not require --control, so check that you passed it.

3. Narrow vs Broad Peak Mode Mismatch

Using narrow peak calling for broad marks (H3K27me3, H3K36me3) fragments the signal into many small peaks instead of capturing the broad domains. Use --peak_type broad for these marks. Conversely, broad mode on TF ChIP-seq over-merges distinct binding sites. Because --peak_type is one value per run, group targets of the same class into one run.

4. Adapter Contamination

Short insert libraries may have significant adapter read-through. Trim Galore runs before alignment. Check the FastQC adapter content plots in fastqc/: >5% adapter after trimming suggests a problem.

5. PCR Bottleneck

Low-input ChIP-seq libraries may have high duplication rates (>30%). The workflow removes duplicates, so a bottlenecked library loses effective depth. Check filtered/<sample>.dup_metrics.txt and the MultiQC report. NRF/PBC are manual calculations (references/05-qc-metrics.md).

6. Blacklist Region Artifacts

Repetitive and high-signal artifact regions inflate peak counts and FRiP. The workflow filters the BAM against the ENCODE blacklist (Amemiya et al. 2019) before peak calling. The hg38-blacklist.v2.bed contains ~900 regions covering ~40 Mb.

Pipeline Scripts

File	Description
scripts/main.nf	Nextflow DSL2 pipeline
scripts/nextflow.config	Execution profiles (local/slurm/gcp/aws)
scripts/Dockerfile	Docker image with all pipeline tools

The image is pinned to linux/amd64; on an arm64 host it runs under emulation.

Tool versions in the image: BWA 0.7.18, samtools 1.19, bedtools 2.31.0, Picard 3.1.1 (Java 17), Trim Galore 0.6.10 with cutadapt 4.6, FastQC 0.12.1, MACS2 2.2.9.1, IDR 2.0.4.2, MultiQC 1.21, deepTools 3.5.5, UCSC bedGraphToBigWig. The conda environment bioinformatics-installer/environments/chipseq-env.yml pins the same versions of the tools it lists and adds phantompeakqualtools=1.2.2, which the image does not carry.

ENCODE Data Integration

After running this pipeline on your own data, compare results with ENCODE:

# Find matching ENCODE experiments
encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K27ac",
    organ="pancreas",
    biosample_type="tissue"
)

# Download ENCODE peaks for comparison
encode_batch_download(
    download_dir="/data/encode_reference/",
    output_type="IDR thresholded peaks",
    target="H3K27ac",
    organ="pancreas",
    assembly="GRCh38"
)

Pitfalls & Edge Cases

• Input control is optional but strongly recommended: MACS2 -c is only added when --control is given. Without it, enrichment is measured against the local lambda model, which is noisier.
• Broad vs narrow peak mode: H3K27me3, H3K36me3 and H3K9me3 need --peak_type broad. Narrow mode on broad marks fragments them into thousands of small peaks.
• Duplicates are removed before peak calling: Picard runs with REMOVE_DUPLICATES=true, and MACS2 then runs with --keep-dup all because the BAM is already deduplicated. There is no "mark only" mode.
• Blacklist filtering happens before peak calling: bedtools intersect -v is applied to the BAM between deduplication and MACS2, so peaks are already blacklist-clean.
• Cross-correlation QC can mislead: NSC/RSC values depend on fragment length distribution. Deeply sequenced libraries can have high NSC but poor enrichment. Check FRiP, which the workflow computes, alongside NSC/RSC, which it does not.
• IDR compares every pair of replicates: all samples matched by --reads are treated as replicates of one experiment, and idr runs once per pair (two samples give one comparison, three give three). No replicate is dropped, and with a single sample IDR is skipped silently. Run one target and condition per invocation: samples of different marks or conditions matched by the same glob are paired as well, and those comparisons are meaningless. There is still no pooled peak call, no pseudoreplicates, and no rescue or self-consistency ratio.

Walkthrough: Processing ENCODE H3K27ac ChIP-seq from FASTQ to Peaks

Goal: Process raw H3K27ac ChIP-seq FASTQ files through this pipeline to generate peak calls, IDR comparisons and signal tracks. Context: BWA-MEM alignment, duplicate removal, blacklist filtering, MACS2 peak calling, FRiP, and one IDR comparison per pair of replicates.

Step 1: Find the experiment and download FASTQs

encode_get_experiment(accession="ENCSR000AKA")

Expected output (fields abridged; the full response also carries files and the four audit_*_count values):

{
  "accession": "ENCSR000AKA",
  "assay_title": "Histone ChIP-seq",
  "target": "H3K27ac",
  "biosample_summary": "GM12878",
  "bio_replicate_count": 2,
  "tech_replicate_count": 2,
  "assembly": ["GRCh38"],
  "status": "released"
}

Step 2: List FASTQ files

encode_list_files(experiment_accession="ENCSR000AKA", file_format="fastq")

Expected output (a JSON array of files; fields abridged):

[
  {"accession": "ENCFF001FQ1", "file_format": "fastq", "output_type": "reads", "biological_replicates": [1], "file_size_human": "2.3 GB"},
  {"accession": "ENCFF002FQ2", "file_format": "fastq", "output_type": "reads", "biological_replicates": [1], "file_size_human": "2.4 GB"},
  {"accession": "ENCFF003FQ3", "file_format": "fastq", "output_type": "reads", "biological_replicates": [2], "file_size_human": "2.1 GB"},
  {"accession": "ENCFF004FQ4", "file_format": "fastq", "output_type": "reads", "biological_replicates": [2], "file_size_human": "2.2 GB"}
]

Interpretation: 2 biological replicates, two FASTQ files each. The listing does not say which file of a pair is read 1 and which is read 2 -- no encode_* tool reports that. Open each file's page on encodeproject.org, where paired_end is 1 or 2 and paired_with names the other accession. Both replicates are needed for the IDR step.

Step 3: Download FASTQs

encode_download_files(file_accessions=["ENCFF001FQ1", "ENCFF002FQ2", "ENCFF003FQ3", "ENCFF004FQ4"], download_dir="/data/chipseq/fastq")

Step 4: Name the files so a read-pair glob can find them

ENCODE FASTQs are named by accession (ENCFF123ABC.fastq.gz) with no _R1/_R2 in the name, so the --reads glob (*_R{1,2}.fq.gz) cannot pair them. Take the mate assignment from each file's page on encodeproject.org (paired_end is 1 or 2, paired_with names the other accession), then link them into the shape the glob expects:

cd /data/chipseq/fastq
ln -s ENCFF001FQ1.fastq.gz chip_rep1_R1.fq.gz
ln -s ENCFF002FQ2.fastq.gz chip_rep1_R2.fq.gz
ln -s ENCFF003FQ3.fastq.gz chip_rep2_R1.fq.gz
ln -s ENCFF004FQ4.fastq.gz chip_rep2_R2.fq.gz

Do the same for the control library FASTQs using an input_ prefix.

Step 5: Run the ChIP-seq pipeline

nextflow run scripts/main.nf \
  -profile local \
  --reads '/data/chipseq/fastq/chip_*_R{1,2}.fq.gz' \
  --control '/data/chipseq/fastq/input_*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --peak_type narrow \
  --bwa_index /data/reference/GRCh38_index \
  --chrom_sizes /data/reference/GRCh38.chrom.sizes \
  --blacklist /data/reference/hg38-blacklist.v2.bed.gz \
  --outdir /data/chipseq/results

Step 6: Validate output quality

From the workflow:

Output	What to check
qc/multiqc/multiqc_report.html	Mapping rate (>80%), adapter content, per-base quality
filtered/<sample>.dup_metrics.txt	Duplication rate (<30%)
qc/<sample>.frip_mqc.tsv	FRiP (>=0.01 for ChIP-seq); also a MultiQC section
peaks/narrow/<sample>_peaks.narrowPeak	Peak count per replicate
peaks/idr/chip_rep1_vs_chip_rep2.idr_peaks.txt	IDR peaks at 0.05 (>20,000 for TFs)

With the two replicates above there is one IDR file; a third replicate would add chip_rep1_vs_chip_rep3 and chip_rep2_vs_chip_rep3.

Manual follow-ups (not run by this workflow): NSC/RSC, NRF/PBC1/PBC2 and the deeptools fingerprint. Commands are in references/05-qc-metrics.md.

Step 7: Log provenance

encode_log_derived_file(
  file_path="/data/chipseq/results/peaks/idr/chip_rep1_vs_chip_rep2.idr_peaks.txt",
  source_accessions=["ENCFF001FQ1", "ENCFF002FQ2", "ENCFF003FQ3", "ENCFF004FQ4"],
  description="IDR peaks from the ENCODE ChIP-seq pipeline skill, H3K27ac GM12878",
  file_type="idr_peaks",
  tool_used="pipeline-chipseq 1.0.0 (BWA 0.7.18, MACS2 2.2.9.1, IDR 2.0.4.2)",
  parameters="--genome GRCh38 --peak_type narrow"
)

Integration with downstream skills

• IDR peaks feed into -> peak-annotation for gene assignment
• Signal tracks (bigWig) feed into -> visualization-workflow for genome browser display
• Peak coordinates feed into -> histone-aggregation for cross-experiment union merge
• QC metrics evaluated by -> quality-assessment against ENCODE standards
• Pipeline provenance logged by -> data-provenance

Code Examples

1. Find ChIP-seq data to process

encode_search_experiments(
  assay_title="Histone ChIP-seq",
  organ="liver",
  target="H3K4me3"
)

Expected output (one entry per experiment; fields abridged):

{
  "results": [
    {
      "accession": "ENCSR456LIV",
      "assay_title": "Histone ChIP-seq",
      "target": "H3K4me3",
      "biosample_summary": "liver tissue male adult (54 years)",
      "status": "released",
      "assembly": ["GRCh38"]
    }
  ],
  "total": 15,
  "limit": 25,
  "offset": 0,
  "has_more": false,
  "next_offset": null
}

2. Download FASTQ files for pipeline input

encode_download_files takes file accessions, so list the files first:

encode_list_files(experiment_accession="ENCSR456LIV", file_format="fastq")

encode_download_files(
  file_accessions=["ENCFF001REP1", "ENCFF002REP1"],
  download_dir="/data/chipseq/liver_h3k4me3"
)

Expected output:

{
  "downloaded": [
    {"accession": "ENCFF001REP1", "file_path": "/data/chipseq/liver_h3k4me3/ENCFF001REP1.fastq.gz", "file_size_human": "3.0 GB", "success": true, "md5_verified": true},
    {"accession": "ENCFF002REP1", "file_path": "/data/chipseq/liver_h3k4me3/ENCFF002REP1.fastq.gz", "file_size_human": "3.1 GB", "success": true, "md5_verified": true}
  ],
  "errors": [],
  "summary": {"total_requested": 2, "successful": 2, "failed": 0, "total_size_human": "6.1 GB"}
}

Integration

This skill produces...	Feed into...	Purpose
IDR peaks (peaks/idr/<sampleA>_vs_<sampleB>.idr_peaks.txt)	peak-annotation	Assign peaks to nearest genes
MACS2 peaks (narrowPeak/broadPeak)	histone-aggregation	Cross-experiment union merge for histone marks
Signal tracks (bigWig)	visualization-workflow	Genome browser visualization
Peak coordinates (BED)	motif-analysis	De novo motif discovery in peak regions
Filtered peaks	regulatory-elements	Classify as enhancers, promoters, insulators
QC metrics	quality-assessment	Validate against ENCODE ChIP-seq standards
pipeline_info/ reports	data-provenance	Record tool versions and parameters
Peak files	variant-annotation	Identify variants in ChIP-seq peaks

Related Skills

• pipeline-guide (parent): General pipeline selection and resource assessment
• histone-aggregation: Merge peaks across samples/replicates after peak calling
• quality-assessment: Deep-dive QC analysis beyond basic metrics
• regulatory-elements: Annotate peaks with regulatory element classifications
• peak-annotation: Annotate peaks with gene associations
• compare-biosamples: Compare ChIP-seq profiles across cell types
• publication-trust: Verify literature claims backing analytical decisions

Presenting Results

When reporting ChIP-seq pipeline results:

• Pipeline status: Report completion status for each stage (QC, alignment, filtering, peak calling, IDR, signal generation) with pass/fail indicators
• Key QC metrics from the run: mapping rate and read counts (samtools flagstat, MultiQC), duplication rate (Picard dup_metrics.txt), FRiP (qc/<sample>.frip_mqc.tsv, a fraction: >=0.01 meets the >=1% standard), peak counts per replicate, and the IDR peak count for each replicate pair. State plainly that NSC/RSC, NRF/PBC and the fingerprint plot were not computed unless the user ran the manual steps
• Peak counts: Report the per-replicate MACS2 peak count and, for every replicate pair, the IDR peak count at the 0.05 threshold. Note the --peak_type used. There are no optimal/conservative/pseudoreplicated peak sets in this workflow
• Signal tracks: Provide paths to the fold-enrichment (signal/<sample>.fc.bw) and p-value (signal/<sample>.pval.bw) tracks
• Traffic light summary: Use green/yellow/red for overall sample quality, and say which metrics were unavailable
• Output paths: List the key output directories (peaks/idr/, peaks/<narrow|broad>/, signal/, qc/ for the FRiP tables, qc/multiqc/, pipeline_info/)
• Next steps: Suggest quality-assessment for deeper QC evaluation, or visualization-workflow for genome browser session generation

For the request: "$ARGUMENTS"