Chromatin Accessibility

An ATAC-seq run followed from reads to accessible regions: ataqv library quality, MACS2 broad peaks, the consensus peak set across libraries, and the DESeq2 sample QC on top of it.

nf-co.re GitHub

Draft

Template version 2.1.2 latest

The atacseq template covers the merged-library, broad-peak route of a standard nf-core/atacseq 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, the ataqv library quality (TSS enrichment, the fragment ladder, where the reads landed), and the deepTools and Picard signal tables
• Peak calls: MACS2 broad calls per library, where HOMER places them relative to genes, and one genomic region across libraries
• Comparison: the consensus peak set, which libraries agree on an interval, and where the libraries sit on the DESeq2 QC of its counts

One MultiQC parquet per run, in one of two places

The release pins MultiQC 1.13, which writes no parquet. The collection binds the report the pipeline writes itself with MultiQC 1.31 or later swapped in, which is what the Nextflow trigger ingests: multiqc/broad_peak/multiqc_data/multiqc.parquet. For a run on the pinned 1.13, the reprocess step writes multiqc/multiqc_data/multiqc.parquet instead and the collection binds that one:

python -m depictio.dev_scripts.multiqc_reprocess --src <outdir> --dest <outdir>

The CLI ingests every parquet it finds under the data root as its own report, so a tree must hold exactly one of the two: never reprocess a run that already published a parquet. The two reports name their modules differently: the pipeline's numbers a tool run at several levels (picard-1, mlib_deeptools), a reprocessed one keeps the plain ids. The MultiQC tab binds the panels both carry under one id, gives the fingerprint and the insert sizes one tile per report on the same slot, and the import drops the tiles the bound report lacks.

The broad-peak, merged-library route

The pipeline default calls broad peaks, so the calls are macs2/broad_peak/*_peaks.broadPeak (BED6+3, no summit column) and a --narrow_peak run is not bound here. 2.x publishes the whole peak tree twice, per merged library and per merged replicate; only the merged-library level is read, because the recipes match on file name and both levels would land in the same table. No glob names the aligner, so bwa, bowtie2, chromap and STAR runs bind identically.

No DESeq2 differential accessibility

nf-core/atacseq 2.0 removed the differential accessibility analysis, so this template has no Differential accessibility tab. What the pipeline still runs is a DESeq2 sample QC on the consensus counts, its PCA and its sample distances, and the Consensus tab reads both in its Sample space section.

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Quick start

Point at a finished runFrom the pipeline itself (v1.10.0+)

depictio ingest /path/to/atacseq_results \
  --template nf-core/atacseq/latest

The results directory is the only thing you have to pass. The hub of the dashboard is the samplesheet the run validated, pipeline_info/samplesheet.valid.csv, which a template-local recipe turns into one row per merged library with its group, replicate, read type, role and control. For a run aligned to another build than hg38, add --var GENOME=<assembly>: it sets the genome axis of the Locus tab.

depictio config nextflow --install     # once per machine
nextflow run nf-core/atacseq -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 ataqv JSON reports, the deepTools fingerprint and profile tables, the Picard MarkDuplicates metrics, the MACS2 broad calls and their HOMER annotation, the consensus boolean and fold-enrichment matrices, the DESeq2 QC of the consensus counts and the MultiQC parquet. The preseq curve and the DESeq2 QC are optional: 2.x skips preseq by default, and --skip_deseq2_qc or a design without replicated groups skips the QC. 70 of its 122 components carry a use: catalog reference, so a tile says where its panel comes from. The run writes no params.json, so nothing is set from its parameters: GENOME is the only variable besides DATA_ROOT.

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	✓	The atacseq --outdir: ALIGNER/merged_library/, fastqc/, trimgalore/, multiqc/, pipeline_info/, plus a MultiQC 1.31 or later parquet, either the pipeline's own under multiqc/broad_peak/multiqc_data/ or a reprocessed one under multiqc/multiqc_data/.
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

21 data collections — 17 required 4 optional · 18 direct 3 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
sample_design	direct	Table	recipe nf-core/atacseq/sample_design.py	required
samplesheet	direct	Table	file samplesheet.valid.csv$	required
multiqc_data	direct	MultiQC	file (?:.*/)?multiqc(?:/[^/]+)?/multiqc_data/multiqc.parquet$	required
ataqv_metrics	direct	Table	recipe ataqv/metrics.py	required
ataqv_fragment_length	direct	Table	recipe ataqv/fragment_length.py	required
ataqv_tss_coverage	direct	Table	recipe ataqv/tss_coverage.py	required
ataqv_chromosome_counts	direct	Table	recipe ataqv/chromosome_counts.py	required
picard_markduplicates_metrics_raw	direct	Table	file .*.mLb.mkD.sorted.MarkDuplicates.metrics.txt$	required
picard_markduplicates_metrics	derived	Table	recipe picard/markduplicates_metrics.py	required
preseq_ccurve_raw	direct	Table	file .*.lc_extrap.txt$	optional
preseq_complexity_curve	derived	Table	recipe preseq/complexity_curve.py	optional
deeptools_fingerprint_metrics	direct	Table	recipe deeptools/fingerprint_metrics.py	required
deeptools_plot_profile	direct	Table	recipe deeptools/plot_profile.py	required
macs2_peak_summary	direct	Table	recipe nf-core/atacseq/peak_summary.py	required
macs2_broad_peaks	direct	Table	recipe macs2/broad_peaks.py	required
homer_annotated_peaks	direct	Table	recipe homer/annotate_peaks.py	required
homer_tss_distance_profile	derived	Table	recipe homer/tss_distance_profile.py	required
macs2_consensus_boolean	direct	Table	recipe macs2/consensus_boolean.py	required
macs2_consensus_fc	direct	Table	recipe macs2/consensus_fc.py	required
deseq2_qc_pca	direct	Table	recipe nf-core/atacseq/deseq2_qc_pca.py	optional
deseq2_qc_sample_dists	direct	Table	recipe deseq2/qc_sample_dists.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
samplesheet · sample	→	multiqc_data	Filter the read-level MultiQC panels by the sequencing libraries picked in the left panel
sample_design · sample	→	multiqc_data	Filter the MultiQC panels by the samples picked in the left rail
sample_design · sample	→	macs2_peak_summary	Filter the peak QC summary by the samples picked in the left rail
sample_design · sample	→	ataqv_metrics	Filter the ataqv metric table by the samples picked in the left rail
sample_design · sample	→	ataqv_fragment_length	Filter the fragment-length ladder by the samples picked in the left rail
sample_design · sample	→	ataqv_tss_coverage	Filter the TSS coverage profiles by the samples picked in the left rail
sample_design · sample	→	ataqv_chromosome_counts	Filter the read-distribution matrix by the samples picked in the left rail
sample_design · sample	→	picard_markduplicates_metrics	Filter the duplication metrics by the samples picked in the left rail
sample_design · sample	→	preseq_complexity_curve	Filter the complexity curves by the samples picked in the left rail
sample_design · sample	→	deeptools_fingerprint_metrics	Filter the fingerprint metrics by the samples picked in the left rail
sample_design · sample	→	deeptools_plot_profile	Filter the metagene profiles by the samples picked in the left rail
sample_design · merged_library	→	macs2_broad_peaks	Filter the peak table by the samples picked in the left rail
sample_design · merged_library	→	homer_annotated_peaks	Filter the peak annotation by the samples picked in the left rail
sample_design · merged_library	→	homer_tss_distance_profile	Filter the TSS distance profiles by the samples picked in the left rail
macs2_broad_peaks · peak_id	→	homer_annotated_peaks	Carry a peak selected on the MACS2 panels over to its HOMER annotation
homer_annotated_peaks · peak_id	→	macs2_broad_peaks	Carry a peak selected in the annotation table back to the MACS2 panels
macs2_broad_peaks · chr	→	macs2_consensus_boolean	A region typed or brushed on the peak navigator narrows the consensus intervals
macs2_broad_peaks · chr	→	homer_annotated_peaks	A region typed or brushed on the peak navigator narrows the HOMER annotation track
macs2_consensus_boolean · peak_id	→	macs2_consensus_fc	Carry a consensus interval selected on the overlap panels over to the signal heatmap
macs2_consensus_fc · peak_id	→	macs2_consensus_boolean	Carry a consensus interval selected on the signal heatmap back to the overlap panels
sample_design · sample	→	deseq2_qc_pca	Filter the consensus PCA by the samples picked in the left rail
sample_design · sample	→	deseq2_qc_sample_dists	Filter the consensus distance matrix by the samples picked in the left rail

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
ataqv/chromosome_counts.py	Reads per reference sequence, per library, from the ataqv JSON reports.	sample, chromosome, read_count, fraction_of_reads, log10_read_count
ataqv/fragment_length.py	Fragment-length distribution of every ATAC library, in long form.	sample, fragment_length, read_count, fraction_of_all_reads, fragment_class
ataqv/metrics.py	Per-library ATAC quality metrics from the ataqv JSON reports.	sample, tss_enrichment, total_reads, hqaa, hqaa_fraction, hqaa_in_peaks, hqaa_overlapping_peaks_percent, total_autosomal_reads, total_mitochondrial_reads, mitochondrial_fraction, duplicate_fraction, duplicate_fraction_in_peaks, duplicate_fraction_not_in_peaks, peak_duplicate_ratio, total_peaks, total_peak_territory, hqaa_tf_count, hqaa_mononucleosomal_count, short_mononucleosomal_ratio, max_fraction_reads_from_single_autosome, median_mapq, properly_paired_fraction, maximum_proper_pair_fragment_size, median_fragment_length, ataqv_version
ataqv/tss_coverage.py	Coverage profile around transcription start sites, in long form.	sample, position, coverage
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/plot_profile.py	deepTools metagene profile in long form, one row per sample and bin.	sample, group, bin, bin_label, signal
deseq2/qc_sample_dists.py	The DESeq2 QC sample-distance matrix the pipeline already computed.	sample
homer/annotate_peaks.py	Tidy HOMER annotatePeaks.pl output into a per-peak annotation table.	sample, peak_id, chr, start, end, strand, peak_score, annotation_class, annotation, distance_to_tss, nearest_promoter_id, gene_name, gene_type
homer/tss_distance_profile.py	Where each sample's peaks sit relative to the nearest TSS, pre-binned.	sample, distance_to_tss, peak_count, fraction
macs2/broad_peaks.py	Concatenate MACS2 broadPeak calls into one tidy peak table.	sample, peak_id, chr, start, end, width, midpoint, score, fold_enrichment, neg_log10_pvalue, neg_log10_qvalue
macs2/consensus_boolean.py	Consensus peak x sample presence matrix from MACS2 consensus boolean tables.	peak_id, consensus_set, interval_id, chr, start, end, num_peaks, num_samples
macs2/consensus_fc.py	Fold-enrichment matrix of the strongest MACS2 consensus peaks.	peak_id, consensus_set, interval_id, chr, region, support
nf-core/atacseq/deseq2_qc_pca.py	DESeq2 QC principal components of the consensus counts, with each sample's group.	sample_id, dim_1, dim_2, dim_1_percent, dim_2_percent, pca_set, group
nf-core/atacseq/peak_summary.py	Per-sample MACS2 peak QC for atacseq 2.x: call count, FRiP score, peak spreads.	sample, num_peaks, frip_score, width_median, width_mean, width_max, fold_enrichment_median, fold_enrichment_mean, neg_log10_qvalue_median, neg_log10_pvalue_median
nf-core/atacseq/sample_design.py	One row per ATAC sample, derived from the samplesheet the run validated.	sample, merged_library, group, replicate, replicate_label, read_type, role, control, n_libraries, libraries
picard/markduplicates_metrics.py	Duplication metrics from a Picard MarkDuplicates report.	sample, library, unpaired_reads_examined, read_pairs_examined, unmapped_reads, unpaired_read_duplicates, read_pair_duplicates, read_pair_optical_duplicates, duplicate_reads, reads_after_dedup, percent_duplication, estimated_library_size
preseq/complexity_curve.py	Library complexity curve with its confidence ribbon, one row per read depth.	sample, total_reads, expected_distinct, lower_ci, upper_ci, ci_width

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Dashboard tabs

One dashboard: the Overview, then seven child tabs in three groups, read as a funnel from library quality to the peaks the libraries agree on. 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, ATAC quality, Signal
Peak calls	Peaks, Annotation, Locus
Comparison	Consensus

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 (group, sample, replicate) sit in the left panel and narrow every tab through the project links; QC thresholds (TSS enrichment, FRiP, peaks called) sit collapsed under them. The Sample sheet and Reference tables (the per-library peak QC) are pinned, collapsed, to the bottom of every child tab. The template has no metadata file and no grouping variable: every grouped tile reads the samplesheet's group column. Each library has two spellings, bare and .mLb.clN, and the hub carries both, so one pick in the filter reaches every panel. The consensus matrices have the libraries as columns, so the sample filters do not narrow them.

Overview MultiQC ATAC quality Signal Peaks Annotation Locus Consensus

Chromatin accessibility, from library quality to the peaks the libraries agree on.

A short hero links the run parameters, which for atacseq 2.x 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 and groups, the sequenced libraries, the genome build and the peaks called, and Pipeline walks the five steps from trimming to the consensus merge, each linked to its tool version and its tab. The findings are live values: they follow the filters, and a route that lacks their data drops them. The fragment ladder takes the first figure slot: it is the ATAC-specific check a reader looks at first.

Filters and components

Filters · the left panel starts collapsed. Key figures has its own filter bar (group and sample), and so does Findings (group and chromosome): each narrows its own section only. The consensus card reads a table with the libraries as columns, which the Key figures bar does not reach.

Section	What it holds
Top	Hero, About this dashboard, The run, Pipeline
Key figures	4 headline cards: samples, peaks called, consensus intervals, FRiP
Findings	Live result rows, then 4 figures: the fragment ladder, the peak significance along the genome, the peaks around the nearest start site and the consensus intervals by the number of libraries calling them
How to read this dashboard	The tabs by group, each with its question

Data & QC · Did sequencing, alignment and filtering work for every library?

MultiQC panels only. Open: general statistics, FastQC sequence counts beside samtools percent mapped, then the deepTools fingerprint, FRiP scores, peaks per library and featureCounts assignments. Per-base quality, Trim Galore kept reads, Picard insert sizes and duplicates, mapped reads per contig and the ataqv mapping quality are collapsed. The general statistics, featureCounts and ataqv tiles exist on a reprocessed report only, and the import drops them on the pipeline's own.

Filters and components

Filters · Sequencing library on samplesheet, in its own Library scope group: the read-level panels are keyed on the sequencing library, a finer grain than the sample every other tab works in.

Section	What it holds
Run summary	1 MultiQC panel
Reads and alignment	2 MultiQC panels
Enrichment	4 MultiQC panels (the fingerprint has one tile per report, on one slot)
QC details (collapsed)	6 MultiQC panels (the insert sizes have one tile per report, on one slot)

Data & QC · Did transposition work, library by library?

The ataqv numbers a library is accepted or rejected on: TSS enrichment, the lowest share of reads in peaks, the mitochondrial fraction and the duplicate fraction. Then the coverage around start sites beside the signal against specificity scatter, where a lasso selects libraries. The fragment ladder has four cards of its own (fragment length, reads by nucleosome window, the sub- to mononucleosomal ratio, properly paired reads), the fragment-length curve with the nucleosome-free and mononucleosome windows shaded, and the reads per fragment class. The read distribution matrix closes the tab: read its chrM row first, since a high share there is the classic ATAC failure.

Filters and components

Filters · Fragment class and a Fragment length range on ataqv_fragment_length, plus Reference sequence on ataqv_chromosome_counts.

Section	What it holds
Library quality at a glance	4 cards
Signal at start sites	1 line chart + 1 scatter
Fragment ladder	4 cards, 1 line chart + 1 bar
Read distribution	1 advanced visualization
ATAC quality table (collapsed)	ATAC quality metrics

Single-end libraries read thinner

ataqv writes no per-reference counts and no TSS values for single-end libraries, so they are absent from the read distribution matrix and from the TSS readings.

Data & QC · How enriched and how complex is each library?

The tables deepTools and Picard write beside the curves MultiQC draws. The coverage concentration, the divergence from a uniform library, the library size Picard estimates and the metagene signal at the start site. Then the fingerprint scatter, and Picard's duplication against depth beside the deepTools metagene profile, with the start site marked and the gene body shaded. Picard makes no size estimate for single-end reads, so the library-size card is the median of the paired-end libraries.

Filters and components

Filters · a Coverage concentration range on deeptools_fingerprint_metrics, a Duplication rate range on picard_markduplicates_metrics and, on a preseq run, an Extrapolated depth range on preseq_complexity_curve.

Section	What it holds
Signal at a glance	4 cards
Coverage concentration	1 advanced visualization
Complexity and metagene	2 advanced visualizations
Complexity curve (collapsed)	1 advanced visualization, preseq runs only

The complexity curve needs preseq

preseq is off by default in 2.x, so the Complexity curve section and the depth filter drop unless the run passed --skip_preseq false. Picard's duplication holds the complexity reading on every route.

Peak calls · How many peaks did each library yield, and how strong?

MACS2 broad calls, one row per peak and library: a broad call reports a region rather than a summit. The peaks in view, their width, their fold enrichment and the median -log10(q), with the line at q 0.05. Then the significance along the genome, where a lasso narrows the cards, the widths and the table to the peaks picked, and the width distribution on a log axis.

Filters and components

Filters · Peak significance and Peak width ranges, and Chromosome, all on macs2_broad_peaks.

Section	What it holds
Peaks at a glance	4 cards
Significance along the genome	1 advanced visualization
Peak width	1 histogram
Peak table (collapsed)	MACS2 broad peaks

Peak calls · Where do the peaks fall relative to genes?

HOMER assigns every peak to a feature class and to the nearest start site. The annotated peaks by feature class, the genes reached, the distance to the nearest start site and the peak score. Then the feature classes per library as 100% bars, and the peaks around the nearest start site with the promoter window shaded. Accessible chromatin is promoter-heavy, so a working library puts a large share of its peaks there. The collapsed HOMER table has a peak record card beside it, which waits for a picked row.

Filters and components

Filters · Feature class and a Distance to TSS range on homer_annotated_peaks.

Section	What it holds
Annotation at a glance	4 cards
Feature classes	1 histogram
Distance to the nearest TSS	1 advanced visualization
Peak annotation (collapsed)	HOMER peak annotation + a peak record card

Peak calls · What do the libraries call at one genomic region?

Three collections on one genome axis. The cards count what the region in view holds and follow it as the tracks do. The navigator draws the broad calls, one lane per library, on the GENOME assembly, and opens on the first chromosome the calls carry: type a locus in its header or brush its axis, and the consensus intervals (as high as their support) and the HOMER annotation (coloured by feature class) follow. The HOMER track stands in for a gene lane, which is bundled for hg38 and mm10 only.

Filters and components

Filters · Peak significance on macs2_broad_peaks, Libraries per interval on macs2_consensus_boolean and Feature class on homer_annotated_peaks.

Section	What it holds
Region at a glance	4 cards
Peaks on one axis	3 advanced visualizations

Comparison · Which peaks do the libraries agree on?

The per-library calls merged into one set of intervals. The consensus intervals, the libraries per interval, the peaks merged and the support of the strongest intervals. Then the UpSet of the libraries calling each interval, and the sample space: the pipeline's DESeq2 PCA of the consensus counts, coloured by group, above the library distance heatmap, where replicates of a group should sit together. The fold-enrichment heatmap over the strongest intervals sits folded below them, since it grows to one row per interval.

Filters and components

Filters · Libraries per interval and Peaks merged ranges on macs2_consensus_boolean, plus Support on macs2_consensus_fc.

Section	What it holds
Consensus at a glance	4 cards
Replicate agreement	1 advanced visualization (UpSet)
Sample space	2 advanced visualizations
Signal at the strongest intervals (collapsed)	1 advanced visualization
Consensus tables (collapsed)	Consensus intervals, Consensus fold enrichment, Principal components, Library distance matrix

The sample space needs replicated groups

The DESeq2 QC is skipped with --skip_deseq2_qc and for a design without replicated groups. The Sample space section and its two tables then drop, and the rest of the tab stays as it is.

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Running the pipeline

Depictio reads the output of nf-core/atacseq, it does not run the pipeline. Run the pipeline first, then ingest, regenerating the MultiQC report only if the run kept the release's MultiQC 1.13:

nextflow run nf-core/atacseq -r 2.1.2 -profile docker \
  --input samplesheet.csv --genome GRCh37

python -m depictio.dev_scripts.multiqc_reprocess --src results/ --dest results/

depictio ingest results/ --template nf-core/atacseq/latest --var GENOME=hg19

GRCh37 is the iGenomes name of the UCSC hg19 assembly, which is what GENOME takes.

See nf-co.re/atacseq/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 every recipe matches on file name, so a run aligned with another aligner binds identically. Keep the merged_replicate/ tree out for that same reason.

<DATA_ROOT>/
├── pipeline_info/
│   ├── samplesheet.valid.csv                     # the hub: one row per merged library
│   └── software_versions.yml
├── multiqc/broad_peak/multiqc_data/
│   └── multiqc.parquet                           # or multiqc/multiqc_data/ after a reprocess
├── fastqc/zips/, trimgalore/                     # raw and trimmed reads
└── bwa/merged_library/
    ├── samtools_stats/                           # *.mLb.mkD.* and *.mLb.clN.*
    ├── picard_metrics/                           # MarkDuplicates, CollectMultipleMetrics
    ├── deeptools/{plotfingerprint,plotprofile}/
    ├── preseq/*.lc_extrap.txt                    # optional: --skip_preseq false only
    ├── ataqv/broad_peak/*.ataqv.json             # the ATAC quality reports
    └── macs2/broad_peak/
        ├── *.mLb.clN_peaks.broadPeak             # BED6+3, no summit column
        ├── *.mLb.clN_peaks.annotatePeaks.txt     # HOMER annotation
        ├── qc/                                   # peak QC summary and FRiP per library
        └── consensus/
            ├── consensus_peaks.mLb.clN.boolean.txt
            └── deseq2/*.{pca.vals,sample.dists}.txt  # optional: the DESeq2 QC

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Validation runs

No AWS megatest is usable for 2.1.2: the 2.1.1 and 2.1.2 prefixes in the bucket each hold a single 12 GB object, so nothing can be mirrored from them. The template was validated on EMBL HPC runs of the release instead (keys atacseq2-*): the test profile under each of the four aligners (bwa, bowtie2, chromap and STAR), test_controls for a run with input controls, and the megatest profile test_full on hg19, ingested with --var GENOME=hg19. megatest.yaml describes that run file by file, so a usable S3 run can be pinned later without rewriting it. The screenshots above come from a test_full run of an earlier, four-tab layout of the dashboard; the current layout has not been captured yet.

Do not pass --project when ingesting: the dashboard is attached to the project by name, so renaming it breaks a later depictio dashboard import. Re-ingesting accumulates dashboards, so delete the project before repeating a run.

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Additional resources

• nf-co.re/atacseq: official pipeline documentation
• nf-co.re/atacseq/2.1.2/results: AWS test results
• 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 a Draft. Open

Maintainers Keep it working as nf-core/atacseq 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.