CUT&RUN / CUT&Tag

Chromatin profiling from reads to peaks: SEACR and MACS2 over the same fragments, how much the two callers agree, and the consensus peak set each target's replicates reproduce.

nf-co.re GitHub

Experimental

Template version 3.2.2 3.1 latest

The cutandrun template covers the peak-calling chain of a standard run:

• Overview: the run in four key figures, live findings and four figures, each linked to the tab that explains it
• Data & QC: the MultiQC report (FastQC, Trim Galore, Bowtie 2 against the target and the spike-in, the deepTools fingerprint), then depth, spike-in scaling, duplication and enrichment per library
• Chromatin signal: the nucleosomal fragment ladder, then the SEACR regions, their pile-up and the signal they hold
• Agreement: how much SEACR and MACS2 agree, the consensus set each target's replicates reproduce, and both on one genomic region

One MultiQC report per run, chosen by MULTIQC_REPROCESSED

cutandrun 3.2.2 pins MultiQC 1.19, which writes no parquet, and Depictio reads only multiqc.parquet (MultiQC 1.31 and later). A run made with a newer MultiQC, as the Nextflow trigger runs are, publishes 04_reporting/multiqc/multiqc_data/multiqc.parquet itself, and the template binds that one by default. A run on the pinned 1.19 needs its report re-generated first:

python -m depictio.dev_scripts.multiqc_reprocess --src DATA_ROOT --dest DATA_ROOT

It re-runs the pinned MultiQC 1.35 over the run's own raw tool outputs and writes multiqc/multiqc_data/multiqc.parquet at the root, next to a REPROCESSED.json recording both versions; --dry-run prints the plan first. Ingest such a run with --var MULTIQC_REPROCESSED=true, which binds the re-generated report instead of the pipeline's. The template never binds both: a re-generated report ingested without the variable is not read, and the MultiQC tab stays empty.

Both callers, or either one

Both validation runs called peaks with both callers (--peakcaller seacr,macs2). SEACR is cutandrun's default and the path the template is built on; the MACS2 collections are optional, so a SEACR-only run still ingests and the layout drops what is missing; a MACS2-only run drops the SEACR tiles the same way. Read Caller agreement with care then: with one caller the comparison reads as complete disagreement.

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Quick start

Point at a finished runA run on the pinned MultiQC 1.19From the pipeline itself (v1.10.0+)

depictio ingest /path/to/cutandrun_results \
  --template nf-core/cutandrun/latest

The results directory is the only thing you have to pass for a run whose own MultiQC report is a parquet (MultiQC 1.31 or later): the sample hub is built from the run's own pipeline_info/samplesheet.valid.csv. For a run aligned to another build than hg38, add --var GENOME=<assembly>.

python -m depictio.dev_scripts.multiqc_reprocess \
  --src /path/to/cutandrun_results --dest /path/to/cutandrun_results
depictio ingest /path/to/cutandrun_results \
  --template nf-core/cutandrun/latest \
  --var MULTIQC_REPROCESSED=true

The release's own report has no parquet, so it is re-generated first, and the variable points the MultiQC collection at the re-generated one.

depictio config nextflow --install     # once per machine
nextflow run nf-core/cutandrun -r 3.2.2 -profile docker --outdir results

No depictio ingest, and no template named: the pipeline ingests its own output directory when it finishes and resolves this template from its own manifest. See Nextflow trigger.

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Reference

The template reads the validated samplesheet, the Bowtie 2 logs (target and spike-in), the SEACR stringent calls, the MACS2 narrow peaks, the per-target consensus peak counts, the fragment-length histograms and fragment BEDs and the three deepTools QC tables, on top of the MultiQC parquet. Every collection and recipe matches on file name, so the numbered stage directories cutandrun publishes are never spelled out. The SEACR scan skips igv/, where the pipeline copies every SEACR bed for its IGV session, so no region is read twice. A caller that finds nothing for a sample still writes its peak file, at 0 bytes: the ingest skips it with a warning, and the caller comparison keeps a row with no peaks for that sample.

cutandrun 3.2.2 still ships no params.json, so no variable is set from the run's parameters. Besides DATA_ROOT, MULTIQC_REPROCESSED says which MultiQC report to bind and GENOME (default hg38) sets the genome axis of the Locus tab.

Self-adapting layout

The dashboard adapts to whatever the run actually produced: components bound to pruned or unparsed data collections are hidden, tabs left with no real visualizations are dropped entirely, and the remaining components are re-packed so there are no empty rows.

Template variables

Variables you provide when running the template: DATA_ROOT is the results directory given to depictio ingest, the others are set with --var NAME=value:

Variable	Required	Description
DATA_ROOT	✓	Root directory containing cutandrun output, the pipeline's --outdir (01_prealign/, 02_alignment/, 03_peak_calling/, 04_reporting/, pipeline_info/).
MULTIQC_REPROCESSED	—	Set (any value) when the MultiQC report was re-generated with depictio.dev_scripts.multiqc_reprocess: binds multiqc/multiqc_data/multiqc.parquet instead of the pipeline's own 04_reporting/multiqc/multiqc_data/multiqc.parquet.
GENOME	—	UCSC assembly the run was aligned to (hg38, hg19, mm10, ...). Sets the genome axis of the Locus tab; pass --var GENOME=ASSEMBLY for a run on another build.

Data collections

22 data collections — 19 required 3 optional · 13 direct 9 derived.

Origin tells you whether a collection is real pipeline data or a reshape of it: direct = a pipeline output (scanned, or a recipe that reads raw files); derived = a recipe that reshapes one or more direct collections into the layout a visualization needs (no new measurement). Reads shows what produces it: a recipe .py transform, whose name links to its source on GitHub, or a raw file scanned off disk. (A direct collection can still have a recipe — one that merely parses/cleans the raw file; derived means the recipe reshapes another collection.)

Tag	Origin	Type	Reads	Status
samples	direct	Table	recipe cutandrun/samples.py	required
samplesheet	direct	Table	file samplesheet.valid.csv$	required
multiqc_data	direct	MultiQC	file multiqc/multiqc_data/multiqc.parquet$	required
bowtie2_logs_raw	direct	Table	file .*.bowtie2.log$	required
bowtie2_spikein_factors	derived	Table	recipe bowtie2/spikein_factors.py	required
deeptools_fingerprint_metrics	direct	Table	recipe deeptools/fingerprint_metrics.py	required
deeptools_sample_pca	direct	Table	recipe deeptools/sample_pca.py	required
deeptools_correlation_matrix	direct	Table	recipe deeptools/correlation_matrix.py	required
seacr_peaks_raw	direct	Table	file (?!(?:./)?igv/)./[^/]*.seacr.peaks.\w+.bed$	required
seacr_peaks	derived	Table	recipe seacr/peaks.py	required
seacr_peak_summary	derived	Table	recipe seacr/peak_summary.py	required
macs2_peaks	direct	Table	recipe macs2/peaks.py	optional
seacr_consensus_peaks	direct	Table	recipe seacr/consensus_peaks.py	required
seacr_fragment_lengths_raw	direct	Table	file .*.frags.len.txt$	required
seacr_fragment_lengths	derived	Table	recipe seacr/fragment_lengths.py	required
seacr_fragment_classes	derived	Table	recipe seacr/fragment_classes.py	required
frip	derived	Table	recipe cutandrun/frip.py	required
caller_agreement	derived	Table	recipe cutandrun/caller_agreement.py	required
samtools_flagstat_raw	direct	Table	file (?:.*/)?markdup/[^/]+.flagstat$	required
samtools_flagstat	derived	Table	recipe samtools/flagstat.py	required
seacr_frags_raw	direct	Table	file .*.frags.cut.bed$	optional
seacr_frags_profile	derived	Table	recipe seacr/frags_profile.py	optional

Conditional routes

Rows are data collections; columns are the variables you set or params.json flags auto-detected from the run. Each filled cell is the effect of setting that variable; an empty cell means that variable leaves the collection unchanged. (21 collections are unaffected by any variable — present on every run.)

+ included− removed⇄ repointed

Data collection	MULTIQC_REPROCESSED
multiqc_data	⇄

Cross-DC links

22 links — selecting a value in the source collection filters the target. The join column is shown after the source.

Source · column		Target	Filters
samples · sample_id	→	multiqc_data	Filter the MultiQC panels by the samples picked in the left panel
samples · sample_id	→	seacr_peaks	Filter the SEACR peak table by the samples picked in the left panel
samples · sample_id	→	seacr_peak_summary	Filter the SEACR QC summary by the samples picked in the left panel
samples · sample_id	→	seacr_fragment_lengths	Filter the fragment-length curves by the samples picked in the left panel
samples · sample_id	→	bowtie2_spikein_factors	Filter the spike-in factors by the samples picked in the left panel
samples · sample_id	→	seacr_fragment_classes	Filter the fragment-class composition by the samples picked in the left panel
samples · sample_id	→	frip	Filter the signal budget by the samples picked in the left panel
samples · sample_id	→	caller_agreement	Filter the caller comparison by the samples picked in the left panel
samples · sample_id	→	macs2_peaks	Filter the MACS2 peak table by the samples picked in the left panel
samples · target	→	seacr_consensus_peaks	Filter the consensus intervals by the targets picked in the left panel
caller_agreement · sample	→	seacr_peaks	Carry a sample selected on the caller comparison over to its SEACR peaks
caller_agreement · sample	→	seacr_peak_summary	Carry a sample selected on the caller comparison over to its SEACR QC row
bowtie2_spikein_factors · sample	→	multiqc_data	Carry the alignment and spike-in thresholds over to the MultiQC panels
seacr_peaks · sample	→	caller_agreement	Carry a sample selected on the SEACR panels back to the caller comparison
samples · sample_id	→	deeptools_fingerprint_metrics	Filter the fingerprint metrics by the samples picked on the samplesheet
samples · sample_id	→	deeptools_sample_pca	Filter the PCA scatter by the samples picked on the samplesheet
samples · sample_id	→	deeptools_correlation_matrix	Filter the correlation matrix rows by the samples picked on the samplesheet
samples · sample_id	→	samtools_flagstat	Filter the duplication counts by the samples picked on the samplesheet
samples · sample_id	→	seacr_frags_profile	Filter the fragment pile-up by the samples picked in the left panel
seacr_peaks · chr	→	seacr_frags_profile	A region brushed or typed on the SEACR navigator narrows the fragment pile-up
seacr_peaks · chr	→	macs2_peaks	A region brushed or typed on the SEACR navigator narrows the MACS2 calls
seacr_peaks · chr	→	seacr_consensus_peaks	A region brushed or typed on the SEACR navigator narrows the consensus intervals

Recipes

Each recipe reshapes raw pipeline output into a tidy table. The name links to its source; Output lists the validated OUTPUT_SCHEMA columns.

Recipe	Transforms	Output
bowtie2/spikein_factors.py	Spike-in normalisation factors, from the two Bowtie 2 log sets of a run.	sample, total_pairs, target_aligned, target_align_rate, spikein_aligned, spikein_align_rate, spikein_fraction, target_per_spikein, scale_factor
cutandrun/caller_agreement.py	Do MACS2 and SEACR agree, sample by sample?	sample, target, caller, n_peaks, total_bp, median_width, max_width, n_shared, n_unique, frac_shared, log10_n_peaks, other_caller, n_peaks_other
cutandrun/frip.py	How much of a sample's fragment coverage falls inside its own peaks.	sample, target, signal_class, coverage_bp, fraction, frip, n_peaks, peak_span_bp, fragment_bp, scale_factor
cutandrun/samples.py	One row per CUT&RUN sample, from the samplesheet the pipeline validated.	sample_id, target, replicate, replicate_label, role, is_control, control_target, n_libraries, library_ids, single_end
deeptools/correlation_matrix.py	deepTools plotCorrelation matrix as a square table, one row per sample.	sample
deeptools/fingerprint_metrics.py	Per-sample deepTools fingerprint metrics, one row per library.	sample, auc, synthetic_auc, auc_ratio, x_intercept, synthetic_x_intercept, elbow_point, synthetic_elbow_point, synthetic_js_distance
deeptools/sample_pca.py	deepTools plotPCA coordinates in long form, one row per sample.	sample, pc1, pc2, pc3, pc1_variance, pc2_variance
macs2/peaks.py	Concatenate MACS2 narrowPeak calls into one tidy peak table.	sample, peak_id, chr, start, end, width, summit, score, fold_enrichment, neg_log10_pvalue, neg_log10_qvalue
samtools/flagstat.py	One row per (sample, filter stage) from a samtools flagstat report.	sample, stage, total_reads, mapped_reads, mapped_pct, duplicate_reads, properly_paired_reads, properly_paired_pct, singleton_pct
seacr/consensus_peaks.py	Consensus peak x replicate presence matrix from CUT&RUN consensus BEDs.	peak_id, target, caller, chr, start, end, width, num_peaks, num_samples, support, total_signal, max_signal
seacr/fragment_classes.py	The fragment-length histogram binned into nucleosome classes.	sample, target, fragment_class, class_order, n_fragments, fraction, median_length, mono_to_sub, sample_median_length
seacr/fragment_lengths.py	Fragment-length distribution per sample, the signature CUT&RUN QC figure.	sample, target, fragment_length, count, fraction, cumulative_fraction
seacr/frags_profile.py	Fragment pile-up around the strongest SEACR regions, one window per region.	sample, target, peak_id, peak_rank, chr, summit, offset_bp, bin_start, bin_end, fragments, cpm
seacr/peak_summary.py	One row per sample: what SEACR called and how strong those calls are.	sample, threshold_mode, num_peaks, total_bp, width_median, width_mean, width_max, total_signal_sum, total_signal_median, max_signal_median, signal_density_median
seacr/peaks.py	Tidy every SEACR peak of every sample into one table.	sample, peak_id, threshold_mode, chr, start, end, width, summit, total_signal, max_signal, signal_density, log10_total_signal

Template variables

Variables you provide when running the template: DATA_ROOT is the results directory given to depictio ingest, the others are set with --var NAME=value:

Variable	Required	Description
DATA_ROOT	✓	Root directory containing cutandrun output (01_prealign/, 02_alignment/, 03_peak_calling/, 04_reporting/, pipeline_info/) plus the reprocessed multiqc/multiqc_data/multiqc.parquet.
GENOME	—	UCSC assembly the run was aligned to (hg38, hg19, mm10, ...). Sets the genome axis of the Locus tab; pass --var GENOME= for a run on another build.

Data collections

22 data collections — 19 required 3 optional · 13 direct 9 derived.

Origin tells you whether a collection is real pipeline data or a reshape of it: direct = a pipeline output (scanned, or a recipe that reads raw files); derived = a recipe that reshapes one or more direct collections into the layout a visualization needs (no new measurement). Reads shows what produces it: a recipe .py transform, whose name links to its source on GitHub, or a raw file scanned off disk. (A direct collection can still have a recipe — one that merely parses/cleans the raw file; derived means the recipe reshapes another collection.)

Tag	Origin	Type	Reads	Status
samples	direct	Table	recipe cutandrun/samples.py	required
samplesheet	direct	Table	file samplesheet.valid.csv$	required
multiqc_data	direct	MultiQC	file (?:.*/)?multiqc(?:/[^/]+)?/multiqc_data/multiqc.parquet$	required
bowtie2_logs_raw	direct	Table	file .*.bowtie2.log$	required
bowtie2_spikein_factors	derived	Table	recipe bowtie2/spikein_factors.py	required
deeptools_fingerprint_metrics	direct	Table	recipe deeptools/fingerprint_metrics.py	required
deeptools_sample_pca	direct	Table	recipe deeptools/sample_pca.py	required
deeptools_correlation_matrix	direct	Table	recipe deeptools/correlation_matrix.py	required
seacr_peaks_raw	direct	Table	file (?!(?:./)?igv/)./[^/]*.seacr.peaks.\w+.bed$	required
seacr_peaks	derived	Table	recipe seacr/peaks.py	required
seacr_peak_summary	derived	Table	recipe seacr/peak_summary.py	required
macs2_peaks	direct	Table	recipe macs2/peaks.py	optional
seacr_consensus_peaks	direct	Table	recipe seacr/consensus_peaks.py	required
seacr_fragment_lengths_raw	direct	Table	file .*.frags.len.txt$	required
seacr_fragment_lengths	derived	Table	recipe seacr/fragment_lengths.py	required
seacr_fragment_classes	derived	Table	recipe seacr/fragment_classes.py	required
frip	derived	Table	recipe cutandrun/frip.py	required
caller_agreement	derived	Table	recipe cutandrun/caller_agreement.py	required
samtools_flagstat_raw	direct	Table	file (?:.*/)?markdup/[^/]+.flagstat$	required
samtools_flagstat	derived	Table	recipe samtools/flagstat.py	required
seacr_frags_raw	direct	Table	file .*.frags.cut.bed$	optional
seacr_frags_profile	derived	Table	recipe seacr/frags_profile.py	optional

Cross-DC links

22 links — selecting a value in the source collection filters the target. The join column is shown after the source.

Source · column		Target	Filters
samples · sample_id	→	multiqc_data	Filter the MultiQC panels by the samples picked in the left panel
samples · sample_id	→	seacr_peaks	Filter the SEACR peak table by the samples picked in the left panel
samples · sample_id	→	seacr_peak_summary	Filter the SEACR QC summary by the samples picked in the left panel
samples · sample_id	→	seacr_fragment_lengths	Filter the fragment-length curves by the samples picked in the left panel
samples · sample_id	→	bowtie2_spikein_factors	Filter the spike-in factors by the samples picked in the left panel
samples · sample_id	→	seacr_fragment_classes	Filter the fragment-class composition by the samples picked in the left panel
samples · sample_id	→	frip	Filter the signal budget by the samples picked in the left panel
samples · sample_id	→	caller_agreement	Filter the caller comparison by the samples picked in the left panel
samples · sample_id	→	macs2_peaks	Filter the MACS2 peak table by the samples picked in the left panel
samples · target	→	seacr_consensus_peaks	Filter the consensus intervals by the targets picked in the left panel
caller_agreement · sample	→	seacr_peaks	Carry a sample selected on the caller comparison over to its SEACR peaks
caller_agreement · sample	→	seacr_peak_summary	Carry a sample selected on the caller comparison over to its SEACR QC row
bowtie2_spikein_factors · sample	→	multiqc_data	Carry the alignment and spike-in thresholds over to the MultiQC panels
seacr_peaks · sample	→	caller_agreement	Carry a sample selected on the SEACR panels back to the caller comparison
samples · sample_id	→	deeptools_fingerprint_metrics	Filter the fingerprint metrics by the samples picked on the samplesheet
samples · sample_id	→	deeptools_sample_pca	Filter the PCA scatter by the samples picked on the samplesheet
samples · sample_id	→	deeptools_correlation_matrix	Filter the correlation matrix rows by the samples picked on the samplesheet
samples · sample_id	→	samtools_flagstat	Filter the duplication counts by the samples picked on the samplesheet
samples · sample_id	→	seacr_frags_profile	Filter the fragment pile-up by the samples picked in the left panel
seacr_peaks · chr	→	seacr_frags_profile	A region brushed or typed on the SEACR navigator narrows the fragment pile-up
seacr_peaks · chr	→	macs2_peaks	A region brushed or typed on the SEACR navigator narrows the MACS2 calls
seacr_peaks · chr	→	seacr_consensus_peaks	A region brushed or typed on the SEACR navigator narrows the consensus intervals

Recipes

Each recipe reshapes raw pipeline output into a tidy table. The name links to its source; Output lists the validated OUTPUT_SCHEMA columns.

Recipe	Transforms	Output
bowtie2/spikein_factors.py	Spike-in normalisation factors, from the two Bowtie 2 log sets of a run.	sample, total_pairs, target_aligned, target_align_rate, spikein_aligned, spikein_align_rate, spikein_fraction, target_per_spikein, scale_factor
cutandrun/caller_agreement.py	Do MACS2 and SEACR agree, sample by sample?	sample, target, caller, n_peaks, total_bp, median_width, max_width, n_shared, n_unique, frac_shared, log10_n_peaks, other_caller, n_peaks_other
cutandrun/frip.py	How much of a sample's fragment coverage falls inside its own peaks.	sample, target, signal_class, coverage_bp, fraction, frip, n_peaks, peak_span_bp, fragment_bp, scale_factor
cutandrun/samples.py	One row per CUT&RUN sample, from the samplesheet the pipeline validated.	sample_id, target, replicate, replicate_label, role, is_control, control_target, n_libraries, library_ids, single_end
deeptools/correlation_matrix.py	deepTools plotCorrelation matrix as a square table, one row per sample.	sample
deeptools/fingerprint_metrics.py	Per-sample deepTools fingerprint metrics, one row per library.	sample, auc, synthetic_auc, auc_ratio, x_intercept, synthetic_x_intercept, elbow_point, synthetic_elbow_point, synthetic_js_distance
deeptools/sample_pca.py	deepTools plotPCA coordinates in long form, one row per sample.	sample, pc1, pc2, pc3, pc1_variance, pc2_variance
macs2/peaks.py	Concatenate MACS2 narrowPeak calls into one tidy peak table.	sample, peak_id, chr, start, end, width, summit, score, fold_enrichment, neg_log10_pvalue, neg_log10_qvalue
samtools/flagstat.py	One row per (sample, filter stage) from a samtools flagstat report.	sample, stage, total_reads, mapped_reads, mapped_pct, duplicate_reads, properly_paired_reads, properly_paired_pct, singleton_pct
seacr/consensus_peaks.py	Consensus peak x replicate presence matrix from CUT&RUN consensus BEDs.	peak_id, target, caller, chr, start, end, width, num_peaks, num_samples, support, total_signal, max_signal
seacr/fragment_classes.py	The fragment-length histogram binned into nucleosome classes.	sample, target, fragment_class, class_order, n_fragments, fraction, median_length, mono_to_sub, sample_median_length
seacr/fragment_lengths.py	Fragment-length distribution per sample, the signature CUT&RUN QC figure.	sample, target, fragment_length, count, fraction, cumulative_fraction
seacr/frags_profile.py	Fragment pile-up around the strongest SEACR regions, one window per region.	sample, target, peak_id, peak_rank, chr, summit, offset_bp, bin_start, bin_end, fragments, cpm
seacr/peak_summary.py	One row per sample: what SEACR called and how strong those calls are.	sample, threshold_mode, num_peaks, total_bp, width_median, width_mean, width_max, total_signal_sum, total_signal_median, max_signal_median, signal_density_median
seacr/peaks.py	Tidy every SEACR peak of every sample into one table.	sample, peak_id, threshold_mode, chr, start, end, width, summit, total_signal, max_signal, signal_density, log10_total_signal

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Dashboard tabs

One dashboard: the Overview, then seven child tabs in three groups, read as a funnel from the libraries to the peaks a target's replicates reproduce. Each tab below carries the same icon and colour the dashboard gives it, so the page and the app read alike.

Group	Tabs
Data & QC	MultiQC, Libraries
Chromatin signal	Fragments, Peaks
Agreement	Caller agreement, Consensus, Locus

Each child tab opens with a short intro and a strip of four cards, then at most three open sections; tables and details follow, collapsed. The persistent Sample filters (target, sample, replicate, role) sit in the left panel and narrow every tab through the project links. The Sample sheet is pinned, collapsed, to the bottom of every child tab. The template has no grouping variable: target, the samplesheet group, is what every grouped tile reads. The IgG controls are samples throughout; only the peak collections omit them.

Overview MultiQC Libraries Fragments Peaks Caller agreement Consensus Locus

Chromatin profiling, from fragments to the peaks replicates reproduce.

A short hero links the run parameters, which for cutandrun 3.2.2 are the software versions: the run ships no params.json. About this dashboard says how the two filter levels work, The run lists the samples, targets, IgG controls and peak callers read from the data, and Pipeline walks the six steps from trimming to the consensus merge, each linked to its tool versions and its tab. The findings are live values: they follow the filters, and a route that lacks their data drops them. The Locus tab has no figure here, since it is a browser rather than a summary.

Filters and components

Filters · the left panel starts collapsed. Key figures and Findings each have their own filter bar (target and sample): each narrows its own section only.

Section	What it holds
Top	Hero, About this dashboard, The run, Pipeline
Key figures	4 headline cards: samples, fragment length, peaks, FRiP
Findings	Live result rows, then 4 figures: the fragment-length distribution, the pile-up at the SEACR summits, MACS2 against SEACR per sample and the reproducible share per target
How to read this dashboard	The tabs by group, each with its question

Data & QC · Did reads trim, align and rise above the IgG control?

MultiQC panels only. Open: general statistics, FastQC sequence counts and the reads kept after trimming, the Bowtie 2 alignments to the target genome beside those to the spike-in genome, then the deepTools fingerprint. Percent mapped, base quality, GC content, insert sizes and reads per contig are collapsed. A re-generated report has no separate spike-in Bowtie 2 module: the import drops the spike-in tile, and the target tile, now full width, shows both genomes, the spike-in libraries carrying a .spikein suffix.

Filters and components

Filters · Target alignment rate and Spike-in scale factor ranges on bowtie2_spikein_factors, carried into the report by a reverse link.

Section	What it holds
QC overview	3 MultiQC panels
Alignment and spike-in	2 MultiQC panels (target and spike-in genome)
Enrichment over the control	1 MultiQC panel (fingerprint)
QC details (collapsed)	5 MultiQC panels

Data & QC · How deep and duplicated is each library, and is it enriched?

The tables the pipeline publishes beside the report. Read pairs, the spike-in scale factor, duplicate reads and the distance from a uniform library. Then the scale factor and the duplicate share per library, the fingerprint metrics, where targets sit apart from the IgG controls, and the PCA beside the clustered correlation matrix of the genome-wide bin counts.

Filters and components

Filters · a Spike-in scale factor range on bowtie2_spikein_factors and a Coverage concentration range on deeptools_fingerprint_metrics.

Section	What it holds
Libraries at a glance	4 cards
Spike-in and duplication	2 bars
Enrichment over the control	1 advanced visualization
Sample similarity	2 advanced visualizations
Depth detail (collapsed)	2 scatters
Library tables (collapsed)	Spike-in factors

Chromatin signal · Did the digestion work, and how nucleosomal are the fragments?

The nucleosomal ladder. The median fragment length, the fragments counted, the fragments by nucleosome class and the mononucleosome to sub-nucleosome ratio per target. Then the fragment-length distribution, one curve per sample with the sub-nucleosomal and mononucleosomal windows shaded, beside its cumulative twin, and each library split into its four nucleosome classes.

Filters and components

Filters · Nucleosome class on seacr_fragment_classes and a Fragment length range on seacr_fragment_lengths.

Section	What it holds
Fragments at a glance	4 cards
Fragment length	2 line charts
Nucleosome classes	1 bar
Fragment tables (collapsed)	Nucleosome classes

Chromatin signal · What did each sample call, and how much signal do peaks hold?

SEACR regions, their width, their coverage per base and the lowest FRiP in view. Then the region width beside the mean pile-up on the summits, the summit-centred pile-up of each sample's strongest regions, and the fragment coverage inside and outside peaks. FRiP is built in base pairs of coverage, because SEACR reports no read count. The pile-up tiles need the fragment BEDs and drop when a run did not publish them.

Filters and components

Filters · Region width, Coverage per base and Contig on seacr_peaks, then Regions called and Median coverage per base on seacr_peak_summary.

Section	What it holds
Peaks at a glance	4 cards
Peak shape	1 histogram + 1 line chart
Pile-up per region	1 advanced visualization
Signal in peaks	1 bar
Peak tables (collapsed)	SEACR regions, MACS2 peaks, SEACR summary per sample, Signal budget

The pile-up needs the fragment BEDs

Both fragment collections are optional. A run or a mirror without the *.frags.cut.bed files ingests everything else, and the pile-up tiles here, the fragment track of the Locus tab and the pile-up figure on the Overview drop out.

Agreement · Do SEACR and MACS2 call the same peaks on each sample?

Peaks called by caller, the peak width by caller, the share of one caller's peaks the other reproduces and the calls only one caller made. Then MACS2 against SEACR, one selectable point per sample, beside the calls the other caller did not make, and the MACS2 significance along the genome. A sample with no MACS2 peaks keeps its row, so it reads as disagreement rather than as missing data.

Filters and components

Filters · Caller and a Share reproduced range, both on caller_agreement.

Section	What it holds
Agreement at a glance	4 cards
Caller against caller	1 scatter + 1 bar
MACS2 along the genome	1 advanced visualization
Comparison table (collapsed)	Caller comparison

Agreement · Which peaks do a target's replicates reproduce?

Consensus intervals by replicate support, the reproducible ones (two replicates or more) by target, the interval width and the coverage per interval. Then the UpSet of the replicates calling each interval, the reproducible share per target and the interval width by support. The Replicate support filter keeps every value until you narrow it, so the cards include single-replicate intervals.

Filters and components

Filters · Replicate support, and Interval width and Member peaks ranges, all on seacr_consensus_peaks.

Section	What it holds
Consensus at a glance	4 cards
Replicate agreement	1 advanced visualization (UpSet)
How reproducible	1 bar + 1 histogram
Consensus table (collapsed)	Consensus intervals

Agreement · Do the callers and the replicates agree on one region?

One region on one genome axis. The cards count what the region in view holds and follow it as the tracks do. The SEACR navigator opens on the first contig the calls sit on, so a run aligned to part of the genome opens on data: type a locus in its header or brush its axis, and the fragment pile-up, the MACS2 calls and the consensus intervals over the gene lane follow.

Filters and components

Filters · SEACR coverage per base on seacr_peaks, MACS2 -log10 q-value on macs2_peaks and Replicate support on seacr_consensus_peaks.

Section	What it holds
Region at a glance	4 cards
One region, four tracks	4 advanced visualizations

Pass the assembly for a run not on hg38

The navigator fetches only the chromosome of its region from the assembly it is given, which defaults to hg38. For a run aligned to another build, pass --var GENOME=<assembly> at ingest, or the tracks land on the wrong coordinates.

A broad mark reproduces poorly, and the dashboard says so

The replicates of a broad histone mark overlap badly, so a large part of its consensus intervals rests on a single replicate. That is a property of the data, not of the template.

Tables and point views select on their entity column: the sample sheet on sample_id; the per-sample tables (SEACR summary, fragment classes, spike-in factors, signal budget, caller comparison), the caller, depth and duplication scatters and the fingerprint tile on sample; the peak and consensus tables, the MACS2 significance track and the SEACR, MACS2 and consensus tracks of the Locus tab on peak_id. A lasso on a scatter, a brush on a track or a picked table row becomes a dashboard filter that narrows the other tiles of its collection and follows the project links to the collections they reach. The deepTools PCA emits a selection, but its collection has no outgoing link, so it narrows no other tile.

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Running the pipeline

Depictio reads the output of nf-core/cutandrun. Run the pipeline first:

nextflow run nf-core/cutandrun -r 3.2.2 \
  --input samplesheet.csv \
  --genome GRCh38 \
  --peakcaller seacr,macs2 \
  --outdir results -profile docker

Then point Depictio at the results. A run on the release's pinned MultiQC 1.19 needs its report re-generated first, and the variable that binds it:

python -m depictio.dev_scripts.multiqc_reprocess --src results/ --dest results/
depictio ingest results/ --template nf-core/cutandrun/latest \
  --var MULTIQC_REPROCESSED=true

A run made with MultiQC 1.31 or later skips both: depictio ingest results/ --template nf-core/cutandrun/latest reads the report the pipeline published.

See nf-co.re/cutandrun/usage for full pipeline documentation.

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Required data structure

Point depictio ingest at the directory holding the pipeline output. Depictio scans recursively and matches on file name, so the stage numbering below is the reference run's layout, not a requirement.

<DATA_ROOT>/
├── pipeline_info/
│   ├── samplesheet.valid.csv                          # the sample hub is derived from this
│   └── software_versions.yml                          # provenance, with local_versions.yml
├── 01_prealign/
│   ├── pretrim_fastqc/*_fastqc.zip
│   └── trimgalore/{fastqc/*_fastqc.zip,*_trimming_report.txt}
├── 02_alignment/bowtie2/
│   ├── target/log/*.bowtie2.log
│   ├── spikein/log/*.spikein.bowtie2.log
│   └── target/markdup/*.{stats,flagstat,idxstats}
├── 03_peak_calling/
│   ├── 04_called_peaks/
│   │   ├── seacr/*.seacr.peaks.stringent.bed          # required, the default caller
│   │   └── macs2/*.macs2_peaks.narrowPeak             # optional, plus *.macs2_peaks.xls
│   ├── 05_consensus_peaks/*.consensus.peak_counts.bed # *.seacr.consensus.* in 3.2.2
│   └── 06_fragments_from_bams/
│       ├── *.frags.len.txt
│       └── *.frags.cut.bed                            # optional, for the pile-up tiles
├── 04_reporting/
│   ├── deeptools_qc/
│   │   ├── *.plotFingerprint.qcmetrics.txt
│   │   └── all_target_bams.plot{PCA.tab,Correlation.mat.tab}
│   ├── igv/                                           # copies of the SEACR beds, skipped
│   └── multiqc/multiqc_data/multiqc.parquet           # the run's own, MultiQC 1.31 or later
└── multiqc/multiqc_data/
    └── multiqc.parquet                                # or this one, from multiqc_reprocess

A tree may hold both MultiQC parquets: the default binds the run's own under 04_reporting/, --var MULTIQC_REPROCESSED=true the re-generated one at the root, and the template never loads the two together.

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Validation runs

No AWS megatest is usable for 3.2.2: every cutandrun 3.2.x prefix in the bucket holds directory markers only. The template was validated on two EMBL HPC runs of the release instead: cutandrun322-full, the test_full profile (the megatest design, two histone marks in two replicates each against IgG controls), and cutandrun322-small, the test_full_small profile (the same design on a read subset against one chromosome, with linear duplicate removal and mitochondrial filtering on). Both runs wrote MultiQC 1.19, so both were re-generated with multiqc_reprocess and checked with --var MULTIQC_REPROCESSED=true; the pipeline's own MultiQC 1.35 report was checked on the small run only. The small run was ingested on a local stack, its zero-byte peak file included; the full run was checked by dry runs and by building every collection without a server.

megatest.yaml lists the tables-only subset a 3.2.2 megatest would need, so a usable S3 run can be pinned later without rewriting it. The 3.1 megatest the previous template version was built on is still fetched by download_test_data.sh next to the 3.1 template:

bash depictio/projects/nf-core/cutandrun/3.1/download_test_data.sh /tmp/cutandrun_test
python -m depictio.dev_scripts.multiqc_reprocess \
  --src /tmp/cutandrun_test --dest /tmp/cutandrun_test
depictio ingest /tmp/cutandrun_test --template nf-core/cutandrun/3.1

The screenshots above show the 3.1 template on an earlier layout of the dashboard; the 3.2.2 layout has not been captured yet.

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Additional resources

• nf-co.re/cutandrun: official pipeline documentation
• nf-co.re/cutandrun/3.2.2/docs/output: the output files the template reads
• nf-co.re/cutandrun/3.1/results: AWS test results of the 3.1 release (3.2.x has none)
• Template System Reference: YAML format, variables, conditionals
• Recipes: how to read, test, and write recipes

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Authorship

Developers Wrote the template, its recipes and its dashboards. weber8thomas

Reviewers Nobody has run it on their own data and signed it off yet, which is what keeps it Experimental. Open

Maintainers Keep it working as nf-core/cutandrun releases. weber8thomas

Reviewing a template on your own data, or taking over a role here, is a contribution in itself: the contributing guide says what each one involves.