Bisulfite Methylation

Bismark alignment and conversion QC, coverage, M-bias per context, the binned methylome with its cohort structure, and a window-level comparison between two groups, next to the pipeline's MultiQC report.

nf-co.re GitHub

Draft

Template version 2.3.0 latest

The methylseq template follows the Bismark route of an nf-core/methylseq run from the reads to the windows whose methylation differs between groups:

• Overview: the run in four key figures, live findings and four figures, each linked to the tab that explains it
• Data & QC: FastQC, Cutadapt, Bismark and Qualimap panels from the regenerated MultiQC report, the libraries that fail the alignment, duplication or conversion floors, and how deep and how evenly the alignments cover the reference
• Methylome: whether the extraction needs a read-position trim, whether each methylome is bimodal, whether the libraries group by the design, and the windows that differ between two groups

The persistent Sample filters (the design factor, then the sample id) sit in the left panel and narrow every tab through the project links, and the collapsed Sample sheet is pinned to the bottom of every child tab.

The MultiQC report must be regenerated

methylseq 2.3.0 ships MultiQC 1.13, which writes no parquet, and Depictio reads only multiqc.parquet (MultiQC 1.31 and later). Re-run MultiQC over the run's own tool outputs before ingesting, or the MultiQC tab stays empty:

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

Bismark route only

methylseq also publishes bismark_hisat/ and bwameth/ routes. This template covers the default bismark/ route; its file patterns match any bismark_<aligner> suffix, so the sample ids parse the same way on the hisat2 route.

The design is a table beside the run

The methylseq samplesheet carries no design column, and the template never parses one out of sample names. Pass a design table with METADATA_FILE: without it there is no group to filter or colour by, and the window comparison is pruned, leaving the Group comparison tab with only the per-library lanes of its collapsed locus section.

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Quick start

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

depictio ingest /path/to/methylseq_results \
  --template nf-core/methylseq/latest \
  --var METADATA_FILE=/path/to/design.tsv \
  --var GENOME=hg38

The sample hub reads input/samplesheet_full.csv under the data root. The variables:

Variable	Default	What it does
METADATA_FILE	none	Design table (TSV): sample id in a sample column or the first column, one column per factor
METADATA_ID_COL	first column	The design table's sample-id column
GROUP_COL	first factor	The factor the dashboards colour and filter by; the comparison tests it when it has two levels, else the first two-level factor
GROUP_COL_DISPLAY	title-cased GROUP_COL	Label used in titles
GENOME	hg38	UCSC assembly of the locus tracks (contig axis and region search)

depictio config nextflow --install     # once per machine
nextflow run nf-core/methylseq -r 2.3.0 -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. Without a METADATA_FILE the window comparison is pruned, and the MultiQC tab stays empty until the report is regenerated as above. See Nextflow trigger.

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Reference

The template reads the samplesheet and the design table, the regenerated MultiQC report, Bismark's alignment, deduplication, splitting and M-bias reports and its per-CpG bedGraphs, and Qualimap BamQC. 68 of its 74 tiles carry a use: catalog reference (bismark/*, qualimap/*, multiqc/*), and the two comparison tracks of the locus view name their viz_kind explicitly.

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 methylseq Bismark-route output (bismark/, fastqc/, trimgalore/, qualimap/, pipeline_info/, input/) plus the reprocessed multiqc/multiqc_data/multiqc.parquet.
METADATA_FILE	—	Optional design table (TSV): sample id in a column named sample or in the first column, one column per factor. Enables the design filters and colouring and the window group comparison (on GROUP_COL, else the first two-level factor).
METADATA_ID_COL	—	Design table sample-id column. Auto-detected as the first column.
GROUP_COL	—	Design column the dashboards group, colour and filter by. Auto-detected as the first factor column of METADATA_FILE. The window group comparison tests it when it has exactly two levels.
GENOME	—	UCSC assembly name of the reference the run aligned to, used by the locus tracks' gene annotation and region search.

Auto-detected (set from the run's metadata / params.json; the route flags drive Conditional routes below): GROUP_COL_DISPLAY, ANNOTATION_COLS

Data collections

29 data collections — 27 required 2 optional · 12 direct 17 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
metadata	direct	Table	file {METADATA_FILE}	optional
samples	derived	Table	recipe nf-core/methylseq/samples.py	required
samplesheet	direct	Table	file samplesheet_full.csv$	required
multiqc_data	direct	MultiQC	file (?:.*/)?multiqc(?:/[^/]+)?/multiqc_data/multiqc.parquet$	required
bismark_alignment_raw	direct	Table	file .*bismark[a-z0-9]+_(PE|SE)_report.txt$	required
bismark_alignment_summary	derived	Table	recipe bismark/alignment_summary.py	required
bismark_dedup_raw	direct	Table	file .*.deduplication_report.txt$	required
bismark_deduplication_summary	derived	Table	recipe bismark/deduplication_summary.py	required
bismark_splitting_raw	direct	Table	file .*_splitting_report.txt$	required
bismark_methylation_context_summary	derived	Table	recipe bismark/methylation_context_summary.py	required
bismark_mbias_raw	direct	Table	file .*.M-bias.txt$	required
bismark_mbias_curve	derived	Table	recipe bismark/mbias_curve.py	required
bismark_summary_report	derived	Table	recipe bismark/summary_report.py	required
bismark_mbias_all_contexts	derived	Table	recipe bismark/mbias_all_contexts.py	required
bismark_bedgraph_index	direct	Table	file .*.bedGraph.gz$	required
bismark_binned_methylation	derived	Table	recipe bismark/binned_methylation.py	required
bismark_methylation_density	derived	Table	recipe bismark/methylation_density.py	required
bismark_window_pca	derived	Table	recipe bismark/window_pca.py	required
bismark_window_correlation	derived	Table	recipe bismark/window_correlation.py	required
bismark_top_variable_windows	derived	Table	recipe bismark/top_variable_windows.py	required
bismark_window_group_compare	derived	Table	recipe bismark/window_group_compare.py	optional
qualimap_bamqc_genome_results_raw	direct	Table	file (?:.*/)?genome_results.txt$	required
qualimap_bamqc_genome_results	derived	Table	recipe qualimap/bamqc_genome_results.py	required
qualimap_coverage_across_reference_raw	direct	Table	file .*coverage_across_reference.txt$	required
qualimap_coverage_across_reference	derived	Table	recipe qualimap/coverage_across_reference.py	required
qualimap_coverage_histogram_raw	direct	Table	file .*coverage_histogram.txt$	required
qualimap_coverage_histogram	derived	Table	recipe qualimap/coverage_histogram.py	required
qualimap_genome_fraction_coverage_raw	direct	Table	file .*genome_fraction_coverage.txt$	required
qualimap_genome_fraction_coverage	derived	Table	recipe qualimap/genome_fraction_coverage.py	required

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. (27 collections are unaffected by any variable — present on every run.)

+ included− removed⇄ repointed

Data collection	METADATA_FILE
metadata	+
bismark_window_group_compare	+

Cross-DC links

16 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	→	bismark_alignment_summary	Filter the alignment summary by the samples picked in the left panel
samples · sample_id	→	bismark_deduplication_summary	Filter the deduplication summary by the samples picked in the left panel
samples · sample_id	→	bismark_methylation_context_summary	Filter the per-context methylation summary by the samples picked in the left panel
samples · sample_id	→	bismark_mbias_curve	Filter the M-bias curves by the samples picked in the left panel
samples · sample_id	→	bismark_summary_report	Filter the run summary by the samples picked in the left panel
samples · sample_id	→	bismark_mbias_all_contexts	Filter the per-context M-bias curves by the samples picked in the left panel
samples · sample_id	→	bismark_binned_methylation	Filter the binned methylation track by the samples picked in the left panel
samples · sample_id	→	bismark_methylation_density	Filter the per-CpG methylation density curves by the samples picked in the left panel
samples · sample_id	→	bismark_window_pca	Filter the library PCA by the samples picked in the left panel
samples · sample_id	→	qualimap_bamqc_genome_results	Filter the Qualimap BamQC summary by the samples picked in the left panel
samples · sample_id	→	qualimap_coverage_across_reference	Filter the depth track by the samples picked in the left panel
samples · sample_id	→	qualimap_coverage_histogram	Filter the depth histogram by the samples picked in the left panel
samples · sample_id	→	qualimap_genome_fraction_coverage	Filter the genome-fraction curves by the samples picked in the left panel
samples · sample_id	→	bismark_window_correlation	Filter the window-correlation matrix rows by the samples picked in the left panel
bismark_window_group_compare · chromosome	→	bismark_binned_methylation	A region brushed or typed on the group-comparison navigator narrows the binned per-library windows

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
bismark/alignment_summary.py	Per-sample Bismark alignment summary, one row per library.	sample, pairs_analysed, unique_best_hit, mapping_efficiency_pct, no_alignment, not_unique
bismark/binned_methylation.py	Genome-wide CpG methylation in fixed windows, one row per sample per window.	sample, chromosome, start, end, position, methylation_pct, n_cpg, cpg_density_class
bismark/deduplication_summary.py	Per-sample Bismark deduplication summary, one row per library.	sample, total_alignments, duplicates_removed, pct_duplication, leftover_sequences
bismark/mbias_all_contexts.py	M-bias in all three cytosine contexts, read 1 and read 2, one long frame.	sample, context, read, series, position, pct_methylation, coverage
bismark/mbias_curve.py	CpG M-bias curve, one row per sample x read x position.	sample, read, series, position, pct_methylation, coverage
bismark/methylation_context_summary.py	Per-sample, per-context methylation summary, long format (one row per	sample, context, methylated_c, unmethylated_c, pct_methylated
bismark/methylation_density.py	Distribution of per-CpG methylation, one curve per library.	sample, series, methylation_bin, n_cpgs, fraction_of_cpgs
bismark/summary_report.py	Bismark's own run-level summary table, one row per library.	sample, total_reads, aligned_reads, unaligned_reads, ambiguous_reads, duplicate_reads, unique_reads, total_cs, methylated_cpg, unmethylated_cpg, methylated_chg, unmethylated_chg, methylated_chh, unmethylated_chh, cpgs_called, pct_aligned, pct_duplicate, pct_cpg_methylation, pct_chg_methylation, pct_chh_methylation, conversion_efficiency_pct
bismark/top_variable_windows.py	The windows whose methylation moves most across the cohort.	window_id, chromosome, window_variance
bismark/window_correlation.py	Pairwise correlation of the libraries over their binned methylomes.	sample
bismark/window_group_compare.py	Window-by-window methylation comparison between the run's two groups.	window_id, chromosome, position, start, end, group_a, group_b, mean_a, mean_b, delta_methylation, t_statistic, p_value, padj, neg_log10_padj, direction, n_cpg_min
bismark/window_pca.py	Where each library sits in the space of its own binned methylome.	sample_id, dim_1, dim_2, dim_3
nf-core/methylseq/samples.py	One row per methylseq sample: the samplesheet, joined to the run's design.	sample_id, fastq_1, fastq_2
qualimap/bamqc_genome_results.py	One row per sample from a Qualimap BamQC genome_results.txt report.	sample, total_reads, mapped_reads, percentage_aligned, mean_coverage, mean_mapping_quality, general_error_rate, gc_percentage, duplication_rate
qualimap/coverage_across_reference.py	Read depth along the reference, from Qualimap BamQC's own windowed table.	sample, chromosome, position, coverage, coverage_std, global_position
qualimap/coverage_histogram.py	How many bases of the reference sit at each depth, from Qualimap BamQC.	sample, coverage, n_genomic_locations, fraction
qualimap/genome_fraction_coverage.py	What share of the reference is covered at least X deep, from Qualimap BamQC.	sample, min_coverage, genome_fraction_pct

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Dashboard tabs

One dashboard: the Overview, then seven child tabs in two groups, read as a funnel from the reads to the windows whose methylation differs between groups. 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, Run QC, Coverage
Methylome	Bias and context, Methylation levels, Cohort structure, Group comparison

Each child tab opens with a short intro and a strip of two to four cards, then at most three open sections; tables, details and the locus view follow, collapsed. The design is a table beside the run (METADATA_FILE), and GROUP_COL is the factor every tab colours and filters by. The persistent Sample filters (the group, then the sample id, both on the sample hub) sit in the left panel and narrow every tab through the project links; each tab's own filters sit under them. The Sample sheet is pinned, collapsed, to the bottom of every child tab.

Overview MultiQC Run QC Coverage Bias and context Methylation levels Cohort structure Group comparison

Bisulfite methylomes, from read QC to the windows that differ between groups.

A short hero links the run's provenance: methylseq 2.3.0 writes no params.json, so the dialog holds the software versions. About this dashboard says how the two filter levels work, The run lists the samples, read pairs, pairs aligned and CpG calls, and Pipeline walks the five steps from trimming to the binned methylome, each linked to the version of its tool and its tab. The findings are live values: they follow the filters. Without a design table the windows row and the Manhattan drop out.

Filters and components

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

Section	What it holds
Top	Hero, About this dashboard, The run, Pipeline
Key figures	4 headline cards: samples, reads aligned, CpG methylation, the lowest conversion
Findings	Live result rows, then 4 figures, one per Methylome tab: the CpG M-bias curves, the per-CpG methylation density, the library PCA and the Manhattan of the tested windows
How to read this dashboard	The tabs by group, each with its question

Data & QC · Did the reads trim, align and deduplicate cleanly in every library?

MultiQC panels only, from the report regenerated with MultiQC 1.35. Open: general statistics, FastQC base quality and the reads Cutadapt kept, then Bismark's alignment rates, deduplication, cytosine methylation and M-bias. A bisulfite library fails the FastQC sequence content and GC checks by design, since conversion turns unmethylated cytosines into thymines. The other FastQC panels with the trimmed read lengths, and Bismark's strand alignment with the four Qualimap BamQC panels, are collapsed.

Filters and components

Filters · Sample ID, read from the MultiQC report.

Section	What it holds
QC overview	3 MultiQC panels
Bisulfite alignment	4 MultiQC panels
Read quality details (collapsed)	10 MultiQC panels
Strand and coverage details (collapsed)	5 MultiQC panels

Data & QC · Which libraries fail the alignment, duplication or conversion floors?

The read pairs Bismark analysed, followed to those aligned and kept; the worst mapping efficiency, which passes at 70% since a bisulfite aligner tops out in the seventies; the worst duplication (10% passes) and the worst conversion, read as 100 - %CHH. Then the conversion per library, coloured by group, and five run-summary metrics on parallel axes: a library that crosses the others on conversion is a bisulfite problem, on alignment and duplication a library preparation one. Bismark's three report tables are collapsed.

Filters and components

Filters · Read pairs analysed, CpG methylation and Conversion efficiency ranges on bismark_summary_report, Mapping efficiency on bismark_alignment_summary, and Duplication rate and Alignments kept after deduplication on bismark_deduplication_summary.

Section	What it holds
Run QC at a glance	4 cards
Conversion per library	Bisulfite conversion per library
Library QC profile	1 advanced visualization
Library detail (collapsed)	Bismark run summary, Alignment summary, Deduplication summary

Data & QC · How deep and how evenly do the alignments cover the reference?

Qualimap BamQC on the deduplicated alignments. The mean depth, the share of the reference covered at least once, Qualimap's duplicate estimate and the mean mapping quality on Bowtie 2's 0 to 42 scale. Mean depth counts the bases at zero, so on a shallow run judge the breadth. Then the depth along the reference, one lane per library, and side by side the bases at each depth and the share of the reference covered at least X deep. The depth threshold in that section's bar narrows that section only. The BamQC table is collapsed.

Filters and components

Filters · Contig on qualimap_coverage_across_reference; the Depth threshold range sits in the bar of Depth distribution.

Section	What it holds
Coverage at a glance	4 cards
Depth along the reference	1 advanced visualization
Depth distribution	2 advanced visualizations, under a filter bar
Coverage detail (collapsed)	Qualimap BamQC summary

Methylome · Does the methylation extraction need a read-position trim?

CpG and CHG methylation, the worst CHH methylation against a 2% line (the 98% conversion floor on a mammalian genome) and the methylated calls by context. Then one M-bias explorer over the six tables of Bismark's M-bias file; the bar of its section picks the context and the read, opens on CpG and narrows the explorer only. A CpG curve that has not flattened by the end of the read calls for an --ignore or --ignore_r2 trim and a re-extraction. The raw M-bias positions and the per-context counts are collapsed.

Filters and components

Filters · a Read position range on bismark_mbias_all_contexts; Cytosine context and Read sit in the bar of M-bias.

Section	What it holds
Context at a glance	4 cards
M-bias	1 advanced visualization, under a filter bar
Context detail (collapsed)	M-bias, every context and read, Methylation by context

Methylome · Is each methylome bimodal, and are CpG-dense regions unmethylated?

Bismark's per-CpG calls, streamed into 2% buckets per library and into 10 kb windows. The share of CpGs at 98 to 100%, the share at 0 to 2%, the median window methylation and the median window per CpG-density class. Then the per-CpG methylation density, one curve per library, and window methylation by CpG-density class. A healthy mammalian methylome is bimodal. The CpG-density tertiles stand in for a CpG-island annotation, which nf-core/methylseq does not ship. Nothing on this tab is collapsed.

Filters and components

Filters · CpG density class and a Window methylation range on bismark_binned_methylation.

Section	What it holds
Levels at a glance	4 cards
Per-CpG methylation	1 advanced visualization
CpG density	Methylation by CpG-density class

Methylome · Do the libraries group by the design?

The library-by-window matrix read three ways. The libraries placed by the PCA, as a ring by group, and the 150 most variable windows ranked by contig. Then the PCA coloured by group, where a lasso makes an analysis group, and the pairwise Pearson correlation, clustered. The heatmap of the 150 windows is collapsed, too tall to open by default. A library that lands with the wrong block in all three is a swap, a mislabelled sheet or a conversion failure.

Filters and components

Filters · Contig on bismark_top_variable_windows.

Section	What it holds
Cohort at a glance	2 cards
Library relationships	2 advanced visualizations
Variable windows (collapsed)	1 advanced visualization

Methylome · Which windows differ in methylation between the two groups?

Every 10 kb window covered in every library is tested between the two levels of the design factor, a t-test on arcsine-transformed proportions with Benjamini-Hochberg correction, and called at padj below 0.05 with at least ten points of difference. The windows tested and called, the median difference and the strongest call. Then the Manhattan of every tested window, and the volcano (its View switch draws a QQ plot) beside the spread of the differences. The locus view, a navigator with two tracks on its region, and the window table are collapsed.

Filters and components

Filters · Call, and Methylation difference and Adjusted p ranges, on bismark_window_group_compare.

Section	What it holds
Calls at a glance	4 cards
Along the genome	1 advanced visualization
Effect and significance	1 advanced visualization + How the differences spread
Methylation at a locus (collapsed)	3 advanced visualizations: the locus navigator, the binned methylation per library and the group difference on the same region
Window detail (collapsed)	Tested windows

A window screen, not a DMR caller

nf-core/methylseq ships no differential-methylation caller. The unit is a 10 kb window, not a CpG or a called DMR, and with few libraries a side the screen is low-powered by construction: read clusters of windows, not a lone hit. Without a design table, or without a factor of exactly two levels, the comparison is pruned and only the per-library lanes of the locus section remain.

A picked row or point becomes a filter that narrows the other tiles of its collection and follows the project links to the collections they reach. The pinned sample sheet selects on sample_id; the Bismark, Qualimap and M-bias tables and the per-library profiles on sample; the PCA on sample_id; the tested-window table and the two window tracks on window_id; the Manhattan on chromosome. The cohort-level collections (correlation, variable windows, comparison) hold the libraries as columns or test across them, so the sample filters do not narrow them; their own tab filters do.

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Running the pipeline

Depictio reads the output of nf-core/methylseq, it does not run the pipeline. Run the pipeline first on the default Bismark route:

nextflow run nf-core/methylseq -r 2.3.0 \
  --input samplesheet_full.csv \
  --genome GRCh38 \
  -profile docker --outdir results

Then place the samplesheet and a design table beside the results, regenerate the MultiQC report and point Depictio at them:

mkdir -p results/input && cp samplesheet_full.csv design.tsv results/input/
python -m depictio.dev_scripts.multiqc_reprocess --src results/ --dest results/
depictio ingest results/ --template nf-core/methylseq/latest \
  --var METADATA_FILE=results/input/design.tsv

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

---

Required data structure

Point depictio ingest at the directory holding the pipeline output. Depictio scans recursively and matches on file name, never on the bismark/ prefix.

<DATA_ROOT>/
├── input/
│   ├── samplesheet_full.csv                   # the hub (required)
│   └── design.tsv                             # METADATA_FILE (optional)
├── pipeline_info/software_versions.yml
├── multiqc/multiqc_data/multiqc.parquet       # written by multiqc_reprocess
├── fastqc/*.zip                               # raw MultiQC inputs
├── trimgalore/*.{txt,zip}
├── bismark/
│   ├── alignments/logs/*_bismark_bt2_PE_report.txt
│   ├── deduplicated/logs/*.deduplication_report.txt
│   ├── methylation_calls/
│   │   ├── splitting_report/*_splitting_report.txt
│   │   ├── mbias/*.M-bias.txt
│   │   └── bedGraph/*.bedGraph.gz             # the methylome and the comparison
│   └── summary/                               # bismark2summary
└── qualimap/<sample>/
    ├── genome_results.txt
    └── raw_data_qualimapReport/*.txt

---

Validation runs

The template was validated on the nf-core AWS megatest of the 2.3.0 release, s3://nf-core-awsmegatests/methylseq/results-93bc5811603c287c766a0ff7e03b5b41f4483895/bismark/, seven paired-end libraries, and the screenshots above come from that run. Its design table is vendored under the template's input/, built once from the samplesheet:

DEST=/tmp/methylseq_test
bash depictio/projects/nf-core/methylseq/2.3.0/download_test_data.sh "$DEST"
mkdir -p "$DEST/input" && cp depictio/projects/nf-core/methylseq/2.3.0/input/sample_metadata.tsv "$DEST/input/"
python -m depictio.dev_scripts.multiqc_reprocess --src "$DEST" --dest "$DEST"
depictio ingest "$DEST" --template nf-core/methylseq/latest \
  --var METADATA_FILE="$DEST/input/sample_metadata.tsv"

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/methylseq: official pipeline documentation
• nf-co.re/methylseq/2.3.0/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/methylseq 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.