Commands Reference

viralunity setup

Pre-builds the per-rule conda environments declared in the Snakemake workflows into a shared cache directory. Run once after installing ViralUnity (or after upgrading); subsequent viralunity consensus / viralunity meta runs reuse the cached envs and skip env creation.

viralunity setup --pipelines all

Options

Option

Default

Description

--conda-prefix

$VIRALUNITY_CONDA_PREFIX or ~/.cache/viralunity/conda-envs

Directory where per-rule envs are cached. Reused by every later pipeline run that points at the same prefix.

--pipelines

all

Repeatable. Pick from consensus-illumina, consensus-nanopore, meta-illumina, meta-nanopore, or all. Segmented variants share envs with their non-segmented counterparts.

--threads

4

Cores given to Snakemake while materializing envs.

--dry-run

off

Print the envs that would be created and exit without invoking conda.

Examples

Build everything once after install:

viralunity setup --pipelines all

Build only what you need:

viralunity setup --pipelines consensus-illumina --pipelines meta-illumina

Inspect first:

viralunity setup --pipelines consensus-illumina --dry-run

Use a shared cache on a cluster:

export VIRALUNITY_CONDA_PREFIX=/shared/viralunity-envs
viralunity setup --pipelines all

Every subsequent viralunity consensus / viralunity meta invocation will pick up $VIRALUNITY_CONDA_PREFIX automatically; no per-run flag needed.

viralunity create-samplesheet

Before running any pipeline you need a CSV sample sheet. The create-samplesheet command generates it automatically from a sequencing run directory.

viralunity create-samplesheet --input /path/to/run/ --output samples.csv

Options

Option

Default

Description

--input

(required)

Run directory: contains FASTQ files directly (level 0) or one subdirectory per sample (level 1).

--output

(required)

Output CSV file path.

--level

1

0 = files in --input, 1 = files in subdirectories (one per sample).

--separator

-

Character used to split the file/directory name and extract the sample ID (-, _, or .).

--pattern

R1

Pattern identifying the first read file at level 0 (R1 for Illumina, barcode for Nanopore).

Output format

The generated CSV has no header row:

# Illumina (3 columns)
sample1,/path/to/sample1_R1.fastq.gz,/path/to/sample1_R2.fastq.gz

# Nanopore (2 columns)
barcode01,/path/to/barcode01.fastq

Examples

Illumina — FASTQs organised in one subdirectory per sample (level 1, default):

viralunity create-samplesheet \
    --input /path/to/illumina_run/ \
    --output /path/to/samples.csv

Illumina — all FASTQs directly in the run directory (level 0):

viralunity create-samplesheet \
    --input /path/to/illumina_run/ \
    --output /path/to/samples.csv \
    --level 0 \
    --separator _ \
    --pattern R1

Nanopore — one FASTQ per barcode subdirectory:

viralunity create-samplesheet \
    --input /path/to/nanopore_run/ \
    --output /path/to/samples_nano.csv \
    --separator _ \
    --pattern barcode

viralunity consensus

The consensus pipeline takes raw reads to processed consensus genome sequences with a single command. Select the data type as a subcommand (illumina or nanopore).

Options — shared (both data types)

Option

Default

Description

--sample-sheet

(required)

CSV file with sample IDs and file paths.

--config-file

(required)

Path for the YAML config file to be created.

--output

(required)

Base output directory.

--reference

—

Reference genome FASTA (mutually exclusive with --segmented-reference).

--segmented-reference

—

Per-segment reference: SEGMENT=PATH (repeatable).

--primer-scheme

—

Primer scheme BED file (amplicon sequencing only).

--minimum-coverage

20

Minimum depth for consensus base inclusion.

--minimum-read-length

50

Minimum read length threshold.

--af-threshold

0.51

Min allele frequency to call variant into consensus.

--run-name

undefined

Name for the sequencing run.

--threads

1

Threads per individual task.

--threads-total

1

Total threads for the workflow.

--create-config-only

off

Only generate the config file; do not run the workflow.

--conda-prefix

~/.cache/viralunity/conda-envs

Cache directory for per-rule conda envs. Picked up from $VIRALUNITY_CONDA_PREFIX if set. Pre-warm with viralunity setup.

Options — Illumina only

Option

Default

Description

--adapters

—

Adapter sequences FASTA (fastp QC).

--trim-head

0

Bases to trim from 5′ end.

--trim-tail

0

Bases to trim from 3′ end.

--cut-front-mean-quality

10

fastp cut_front quality threshold.

--cut-tail-mean-quality

10

fastp cut_tail quality threshold.

--cut-right-window-size

4

fastp cut_right window size.

--cut-right-mean-quality

15

fastp cut_right quality threshold.

--af-isnv-threshold

0.0

Min allele frequency for iSNV analysis.

--run-isnv

off

Run intra-host SNV analysis (LoFreq).

Options — Nanopore only

Option

Default

Description

--chunk-size

10000

Chunk size for clair3 processing.

--clair3-model

r1041_e82_400bps_sup_v500

Clair3 model for variant calling.

--variant-quality

20

Minimum variant quality (clair3).

--variant-depth

10

Minimum alt allele depth (clair3).

--minimum-map-quality

30

Minimum mapping quality (clair3).

Examples

Illumina — single reference:

viralunity consensus illumina \
    --sample-sheet /path/to/example.csv \
    --config-file /path/to/example.yml \
    --run-name example_run \
    --output /path/to/example_output \
    --reference /path/to/references/viral_genome.fasta \
    --primer-scheme /path/to/primers.bed \
    --adapters /path/to/adapters.fa \
    --threads 2 \
    --threads-total 4

Illumina — segmented genome (replace --reference with --segmented-reference):

viralunity consensus illumina \
    --sample-sheet /path/to/example.csv \
    --config-file /path/to/example_segmented.yml \
    --run-name example_run \
    --output /path/to/example_output \
    --segmented-reference L=/path/to/L_segment.fasta \
    --segmented-reference S=/path/to/S_segment.fasta \
    --threads 2 \
    --threads-total 4

Nanopore:

viralunity consensus nanopore \
    --sample-sheet /path/to/samplesheet_nano.csv \
    --config-file /path/to/example_nano.yml \
    --run-name example_run \
    --output /path/to/example_output \
    --reference /path/to/reference.fasta \
    --threads 4 \
    --threads-total 4

Config only (generates config without running the workflow):

viralunity consensus illumina \
    --sample-sheet /path/to/example.csv \
    --config-file /path/to/example.yml \
    --output /path/to/example_output \
    --reference /path/to/reference.fasta \
    --create-config-only

viralunity meta

The metagenomics pipeline takes raw reads to taxonomic classifications and visualizations. You can use Kraken2 only, Diamond only, or both. Each tool can be run on reads and, when assembly is enabled, on contigs.

Pipeline overview (Illumina)

  1. Quality control — fastp: trimming, quality filtering, adapter handling, JSON/HTML reports.

  2. Optional dehosting — minimap2 (if --host-reference is set) or Deacon (if --deacon-index is set).

  3. Read classification — Kraken2 and/or DIAMOND blastx on reads (optional each).

  4. Optional de novo assembly — MEGAHIT on host-filtered pairs.

  5. Contig classification — Kraken2 and/or Diamond on contigs (when assembly is run).

  6. Summaries and filters — Per-sample taxa tables, RPM normalisation (and RPKM when --viral-genomes is supplied), optional max-RPM bleed filter, and negative-control enrichment filter (fold-enrichment, log10-ratio, z-score; replaces the old Poisson filter). Krona plots (both a raw *.krona.html and a post-filter *.filtered.krona.html per sample/classifier/mode) and MultiQC reports.

  7. Optional dynamic reference assembly — Automatic reference sequence selection from taxonomic hits and subsequent consensus assembly.

Pipeline overview (Nanopore)

  1. Optional dehosting — minimap2 or Deacon.

  2. Read classification — Kraken2 and/or Diamond on reads.

  3. Optional de novo assembly — MEGAHIT on host-filtered reads.

  4. Optional polishing — Racon and/or Medaka on MEGAHIT contigs.

  5. Contig classification — Kraken2 and/or Diamond on contigs.

  6. Summaries and filters — Same as Illumina.

  7. Optional dynamic reference assembly — Same as Illumina.

Options — shared (both data types)

Option

Default

Description

--sample-sheet

(required)

CSV with sample IDs and file paths.

--config-file

(required)

Path to YAML config file to be generated.

--output

(required)

Base output directory.

--run-name

undefined

Run name; output goes under {output}/{run_name}/.

--kraken2-database

NA

Path to Kraken2 database.

--krona-database

NA

Path to Krona taxonomy (required for any classification).

--taxdump

NA

NCBI taxdump dir (nodes.dmp, names.dmp).

--remove-human-reads

off

Remove human reads from summaries/Krona.

--remove-unclassified-reads

off

Remove unclassified reads from summaries/Krona.

--host-reference

NA

Host genome FASTA for minimap2 dehosting.

--deacon-index

NA

Deacon minimizer index for host depletion.

--run-denovo-assembly

off

Run MEGAHIT de novo assembly.

--run-kraken2-reads/--no-kraken2-reads

on

Kraken2 on reads.

--run-kraken2-contigs/--no-kraken2-contigs

on

Kraken2 on contigs.

--run-diamond-reads/--no-diamond-reads

off

Diamond on reads.

--run-diamond-contigs/--no-diamond-contigs

off

Diamond on contigs.

--diamond-database

NA

Protein FASTA for Diamond.

--taxids

NA

NCBI taxid mapping for Diamond taxonomy.

--diamond-sensitivity

sensitive

sensitive / mid-sensitive / more-sensitive / ultra-sensitive.

--evalue

1e-10

Diamond E-value threshold.

--bleed-fraction

0.005

Max-RPM bleed filter fraction.

--negative-controls

(empty)

Comma-separated sample IDs used as negative controls.

--enrichment-pseudocount

1.0

Pseudocount for fold-enrichment and log10-ratio vs negative controls.

--z-score-threshold

3.0

Z-score threshold for neg_pass (used when ≥2 negative controls are present).

--log10-ratio-threshold

1.0

Log10-ratio threshold for neg_pass; the default 1.0 marks a 10-fold enrichment over controls. Used when exactly 1 negative control is present, or when z-score is undefined due to zero control variance.

--minimum-hit-group

4

Kraken2 minimum-hit-group parameter.

--run-ictv-host-filter/--no-ictv-host-filter

off

Keep only vertebrate-infecting viruses (drop phages, plant/fungal/algal/invertebrate-only viruses). See Taxonomic false-positive filters below.

--ictv-vertebrate-taxids-file

NA

Vertebrate-virus taxid allowlist (built by the build_ictv_vertebrate_taxids.py script — see Taxonomic false-positive filters below). Required with --run-ictv-host-filter.

--run-nr-validation/--no-nr-validation

off

Re-search de novo viral contigs against NCBI nr and keep only NR-confirmed viral contigs. Contig tracks only; requires --run-denovo-assembly and --run-diamond-contigs.

--nr-diamond-database

NA

nr database for NR validation: a BLAST+ nr db (searched via diamond prepdb) or a native .dmnd.

--nr-evalue

1e-10

E-value threshold for the NR DIAMOND search.

--nr-max-target-seqs

10

Top hits kept per contig for the NR LCA consensus.

--nr-sensitivity

fast

DIAMOND sensitivity for the NR search.

--nr-consensus-threshold

0.5

Fraction of a contig’s hits that must agree at a rank for the LCA consensus.

--run-reference-assembly/--no-run-reference-assembly

off

Enable reference assembly from filtered taxonomic hits.

--method

(none)

Method used for reference assembly (kraken2, diamond, both). Required when --run-reference-assembly is set.

--source

(none)

Source of taxonomy data for reference assembly (reads, contigs, both). Required when --run-reference-assembly is set.

--reads-count

100

Minimum reads assigned to a viral family to trigger reference assembly.

--contigs-count

1

Minimum contigs assigned to a viral family to trigger reference assembly.

--families

Coronaviridae,...

Comma-separated list of targeted viral families for reference assembly.

--reference-selection-strategy

taxid

Strategy to associate a reference genome (taxid or similarity).

--blast-qcov

80

Minimum query coverage for similarity strategy.

--blast-pident

80

Minimum percent identity for similarity strategy.

--viral-genomes

NA

FASTA of viral genomes for reference assembly database.

--viral-taxids

NA

TSV of genome to TaxID mapping for reference assembly.

--threads

1

Threads per task.

--threads-total

1

Total threads for the workflow.

--create-config-only

off

Only generate the config; do not run the workflow.

--conda-prefix

~/.cache/viralunity/conda-envs

Cache directory for per-rule conda envs. Picked up from $VIRALUNITY_CONDA_PREFIX if set. Pre-warm with viralunity setup.

Options — Illumina only (fastp QC)

Option

Default

Description

--adapters

NA

Adapter FASTA; NA = auto-detect.

--minimum-read-length

50

Minimum read length after trimming.

--trim-head

0

Bases to trim from 5′.

--trim-tail

0

Bases to trim from 3′.

--cut-front-mean-quality

20

fastp cut_front quality threshold.

--cut-tail-mean-quality

20

fastp cut_tail quality threshold.

--cut-right-window-size

4

fastp cut_right window size.

--cut-right-mean-quality

20

fastp cut_right quality threshold.

Options — Nanopore only (polishing)

Option

Default

Description

--run-polish-racon/--no-polish-racon

off

Racon polishing on MEGAHIT assembly.

--run-polish-medaka/--no-polish-medaka

off

Medaka polishing on assembly.

--medaka-model

—

Medaka model (e.g. r941_min_high_g360).

Examples

Illumina — Kraken2 on reads (default):

viralunity meta illumina \
    --sample-sheet /path/to/example.csv \
    --config-file /path/to/example_meta.yml \
    --run-name example_run \
    --output /path/to/example_output \
    --kraken2-database /path/to/kraken2_db \
    --krona-database /path/to/krona_taxonomy \
    --taxdump /path/to/taxdump \
    --threads 2 \
    --threads-total 4

Illumina — with adapters and dehosting:

viralunity meta illumina \
    --sample-sheet samples.csv \
    --config-file config.yaml \
    --output /path/to/output \
    --kraken2-database /path/to/kraken2_db \
    --krona-database /path/to/krona_taxonomy \
    --taxdump /path/to/taxdump \
    --adapters /path/to/adapters.fa \
    --host-reference /path/to/host_genome.fa \
    --threads 4 --threads-total 8

Illumina — full pipeline (assembly + Kraken2 + Diamond, reads and contigs):

viralunity meta illumina \
    --sample-sheet samples.csv \
    --config-file config.yaml \
    --output /path/to/output \
    --kraken2-database /path/to/kraken2_db \
    --krona-database /path/to/krona_taxonomy \
    --taxdump /path/to/taxdump \
    --run-diamond-reads --run-diamond-contigs \
    --diamond-database /path/to/proteins.faa \
    --taxids /path/to/protein2taxid.tsv \
    --run-denovo-assembly \
    --host-reference /path/to/host.fa \
    --threads 4 --threads-total 8

Illumina — with reference assembly (Kraken2 hits drive consensus assembly):

viralunity meta illumina \
    --sample-sheet samples.csv \
    --config-file config.yaml \
    --output /path/to/output \
    --kraken2-database /path/to/kraken2_db \
    --krona-database /path/to/krona_taxonomy \
    --taxdump /path/to/taxdump \
    --run-reference-assembly \
    --method kraken2 \
    --source reads \
    --families Coronaviridae,Orthomyxoviridae \
    --reads-count 100 \
    --viral-genomes databases/virus_genomes/viral.genomes.fasta \
    --viral-taxids databases/virus_genomes/genome2taxid.tsv \
    --threads 4 --threads-total 8

Nanopore — full pipeline (dehosting, assembly, Medaka polishing, Kraken2 + Diamond):

viralunity meta nanopore \
    --sample-sheet /path/to/samplesheet_nano.csv \
    --config-file /path/to/config_nano.yml \
    --run-name example_run \
    --output /path/to/output_nano \
    --kraken2-database /path/to/kraken2_db \
    --krona-database /path/to/krona_taxonomy \
    --taxdump /path/to/taxdump \
    --diamond-database /path/to/proteins.faa \
    --taxids /path/to/protein2taxid.tsv \
    --host-reference /path/to/host.fa \
    --run-denovo-assembly \
    --run-kraken2-reads --run-kraken2-contigs \
    --run-diamond-reads --run-diamond-contigs \
    --run-polish-medaka --medaka-model r941_min_high_g360 \
    --threads 4 --threads-total 4

Krona outputs (raw vs. filtered)

For every classifier/mode that runs, the pipeline emits two Krona HTMLs per sample:

  • samples/<sample>/<classifier>_<mode>.krona.html — built directly from the per-sample taxonomic classification before any cross-sample filtering. This is the raw view of what the classifier reported.

  • samples/<sample>/<classifier>_<mode>.filtered.krona.html — built from the same krona input, but pruned to taxa that survive the filters recorded in the fully-filtered taxa-summary table (the longest-named file in the cumulative chain, e.g. _taxa_summary_RPKM.nr.ctgstats.bleed.neg.ictv.tsv) for that (sample, tool, mode).

Filtering is lineage-aware: a contig/read is kept when any ancestor of its leaf taxid at the family, genus, or species rank passes bleed_pass (and, when negative controls are configured, also neg_pass). This is the inverse of how summarize_krona_taxa.py aggregates rows up the lineage, so the filtered Krona shows exactly the contigs/reads that contributed to a passing rank-row. Strain and sub-species hits whose species/genus/family passes are preserved; rows with taxid==0 are dropped.

neg_pass semantics — set by the negative-control enrichment filter:

  • neg_pass = NA — no negative controls configured (zero-control mode); treated as keep by the Krona filter.

  • neg_pass = True with neg_decision = "absent_from_controls" — controls are configured but the taxon is absent from every one of them (control_max == 0). The enrichment ratios (fold_enrichment, log10_ratio, agg_fold_enrichment, agg_log10_ratio) would only track the sample’s own abundance against the pseudocount, so they are blanked to NA and every enrichment gate auto-passes — absence from the controls is itself the evidence the taxon is not control-borne. The z-score gates (neg_pass_5/neg_pass_10) stay NA (a z-score is undefined without control signal).

  • neg_pass = True/False with neg_decision = "log10_ratio" — one negative control; gate is log10_ratio ≥ --log10-ratio-threshold.

  • neg_pass = True/False with neg_decision = "z_score" — two or more controls; gate is z_score ≥ --z-score-threshold.

  • neg_pass = True/False with neg_decision = "log10_ratio_fallback" — two or more controls but all identical (SD = 0); falls back to the log10-ratio gate.

Additional diagnostic columns added by the negative-control step (the .neg suffix): neg_metric (rpkm or rpm), control_mean, control_sd, control_median, control_max, fold_enrichment, log10_ratio, z_score, enrichment_pseudocount, z_score_threshold_used, log10_ratio_threshold_used, n_negative_controls.

Convenience pass/fail flags are also emitted (>= inclusive; NA where the underlying statistic is NA): fold_enrichment_10x_pass (fold_enrichment >= 10), fold_enrichment_100x_pass (fold_enrichment >= 100), neg_pass_5 (z_score >= 5) and neg_pass_10 (z_score >= 10).

A complementary aggregate (pooled) control view is also emitted (diagnostic only — it does not change neg_pass): pooled_control_metric treats all controls as one pooled library (raw reads pooled, i.e. each control weighted by its library size; absent controls contribute 0), plus agg_fold_enrichment, agg_log10_ratio, and agg_fold_enrichment_10x_pass/agg_fold_enrichment_100x_pass. This catches widespread, high-variance contamination that inflates per-control SD and drags the z-score down.

viralunity/scripts/python/filter_krona_by_pass_taxids.py also exposes a CLI for standalone use:

python viralunity/scripts/python/filter_krona_by_pass_taxids.py \
    --summary path/to/diamond_contigs_taxa_summary_RPKM.nr.ctgstats.bleed.neg.ictv.tsv \
    --krona-input path/to/{sample}.diamond.supported.krona_input.tsv \
    --out path/to/{sample}.diamond.supported.filtered.krona_input.tsv \
    --sample {sample} --tool diamond --mode contigs \
    --taxdump path/to/taxdump

Taxonomic false-positive filters (optional)

Open classifiers (kraken2 + viral index, DIAMOND + viral RefSeq) can report taxa that are artifacts of imperfect taxonomic identification — bacteriophages, giant algal viruses, endogenous retroviruses, and bacterial/eukaryal contigs mislabelled as viral. Three optional, tunable filters remove these. They are off by default, and each writes a *.dropped.tsv audit sidecar next to its output listing the removed rows and why.

Order. The taxa summary flows through one cumulative chain; each enabled step appends exactly one suffix, in this order:

_RPM/_RPKM → .nr → .ctgstats → .bleed → .neg → .ictv

so a fully-loaded contig track ends at ..._taxa_summary_RPKM.nr.ctgstats.bleed.neg.ictv.tsv and the fully-filtered table is always the file with the longest name. NR validation and the .ctgstats largest-contig statistics are contig-tracks only (and .ctgstats additionally requires --viral-genomes); the ICTV host filter applies to all tracks. The order is result-neutral with respect to the surviving taxa: .ctgstats, .bleed and .neg only add per-taxon columns (they never remove rows and don’t depend on which other taxa are present), while the row-removing steps (.nr, .ictv) use criteria independent of those columns. With every filter off the chain is just ..._RPM.bleed.tsv (or _RPKM.bleed.tsv with --viral-genomes), byte-identical in content to before. Row-removing steps write a *.dropped.tsv audit sidecar.

Filter

Flag

Scope

What it drops

ICTV host

--run-ictv-host-filter + --ictv-vertebrate-taxids-file

all four tracks

Taxa not in a vertebrate-infecting virus lineage (phages, plant/fungal/algal/invertebrate-only, giant viruses). Lineage-aware against an ICTV-derived taxid allowlist (built by build_ictv_vertebrate_taxids.py, see below).

NR validation

--run-nr-validation + --nr-diamond-database

contig tracks only

Contigs the NR (full nr) LCA consensus confidently calls non-viral. Runs one aggregated diamond blastx over all samples’ de novo viral contigs; requires --run-denovo-assembly and --run-diamond-contigs.

NR validation also writes <track>_nr_flags.tsv surfacing (not filtering) species-level RefSeq-vs-NR disagreements — misid_novel (an NR virus species absent from that sample’s RefSeq calls, highest surveillance interest) and misid_known.

Building the ICTV allowlist. The vertebrate-virus taxid allowlist is derived from the ICTV Virus Metadata Resource (VMR; keep taxa whose “Host source” contains vertebrates) and is a plain, user-editable list of NCBI taxids. Download the current VMR spreadsheet from https://ictv.global/vmr, then:

python viralunity/scripts/python/build_ictv_vertebrate_taxids.py \
    --vmr VMR_MSLxx.xlsx --names <taxdump>/names.dmp \
    --output databases/ictv/vertebrate_virus_taxids.txt

The script maps ICTV family/genus names to taxids via the taxdump names.dmp; regenerate it against a new VMR/taxdump release, or edit the taxid list by hand to tune coverage.

Aggregated contig search (config-only). combine_contig_search: true in the YAML runs the per-sample contig DIAMOND search once over all samples’ contigs combined (sample-prefixed headers), splitting the output per sample — often faster (one DB load). Off by default; toggle via --create-config-only, edit the YAML, and rerun. Benchmark against the per-sample path using the logs/diamond_contigs/*.benchmark.txt wall-times before adopting it.

Not yet in scope (future). Physically slimmed diamond/kraken2 databases, an empirical REVISA-trained FP classifier, and a targeted mapping/genotyping track for a curated virus-of-interest panel are documented directions, not implemented.

viralunity get-databases

The get-databases command downloads and sets up the reference databases required by the meta pipeline. Each subcommand takes an optional --path argument (default: databases in the current directory), inside which a named subdirectory is created.

viralunity get-databases --help
viralunity get-databases kraken2 --help

kraken2 — Kraken2 pre-built index

Option

Default

Description

--path

databases

Parent directory; creates {path}/kraken2/.

--url

k2_viral_20240112

URL of the pre-built index archive. Check available indexes.

 
viralunity get-databases kraken2 --path /data/dbs
# database at /data/dbs/kraken2/
# use: --kraken2-database /data/dbs/kraken2

krona — Krona taxonomy

Requires the viralunity conda environment to be active (CONDA_PREFIX must be set).

Option

Default

Description

--path

databases

Parent directory; creates {path}/krona/taxonomy/.

 
conda activate viralunity
viralunity get-databases krona --path /data/dbs
# taxonomy at /data/dbs/krona/taxonomy/
# use: --krona-database /data/dbs/krona/taxonomy

taxdump — NCBI taxdump

Option

Default

Description

--path

databases

Parent directory; creates {path}/taxdump/.

--url

NCBI taxdump

URL of the taxdump archive.

 
viralunity get-databases taxdump --path /data/dbs
# nodes.dmp, names.dmp, etc. at /data/dbs/taxdump/
# use: --taxdump /data/dbs/taxdump

diamond — Diamond protein database

Option

Default

Description

--path

databases

Parent directory; creates {path}/diamond/.

--taxon

Viruses

NCBI taxon name to download (e.g. Viruses, coronaviridae).

--refseq/--no-refseq

on

Limit to RefSeq genomes only.

--threads

1

Threads for diamond makedb.

--skip-makedb

off

Download and reformat files only; skip diamond makedb.

Requires the NCBI Datasets CLI (conda install -c conda-forge ncbi-datasets-cli).

viralunity get-databases diamond --path /data/dbs --threads 4
# /data/dbs/diamond/viral.dmnd
# /data/dbs/diamond/protein2taxid.tsv
# use: --diamond-database /data/dbs/diamond/viral.dmnd
#      --taxids /data/dbs/diamond/protein2taxid.tsv

NCBI Datasets occasionally bundles a small number of nucleotide CDS records (with a genome accession such as NC_xxxxxx.1 as the header) inside the protein.faa it returns for --include protein. The diamond subcommand automatically detects and drops these records before running diamond makedb and prints a warning summarizing how many were skipped.

clean-protein-fasta — Strip nucleotide records from a protein FASTA

Option

Default

Description

--input

(required)

Input protein FASTA file (e.g. databases/diamond/viral.protein.faa).

--output

<input>.cleaned.faa

Output FASTA path. If equal to --input, the file is replaced atomically.

--no-backup

off

When replacing the input in place, do not keep a .with_dna.bak backup.

--min-dna-length

20

Minimum sequence length to classify a record as DNA.

Use this command to recover an already-downloaded viral.protein.faa that fails diamond makedb with:

Error: The sequences are expected to be proteins but only contain DNA letters.

viralunity get-databases clean-protein-fasta \
    --input databases/diamond/viral.protein.faa \
    --output databases/diamond/viral.protein.faa

diamond makedb \
    --in databases/diamond/viral.protein.faa \
    --db databases/diamond/viral.dmnd \
    --threads 4

nr — NCBI nr database for NR validation

Downloads and configures the full NCBI nr protein database used by meta --run-nr-validation (--nr-diamond-database). By default it fetches NCBI’s preformatted, md5-verified BLAST+ nr volumes with the official update_blastdb.pl tool and runs diamond prepdb so DIAMOND can search them directly; BLAST v5 databases carry taxonomy, so the staxids the NR-validation step needs are populated with no extra download.

Option

Default

Description

--path

databases

Parent directory; creates/uses {path}/diamond/.

--db-name

nr

BLAST database name to fetch with update_blastdb.pl.

--source

ncbi

Download mirror (ncbi, aws, gcp; aws/gcp are often faster).

--threads

1

Threads for update_blastdb.pl and diamond prepdb.

--skip-prepdb

off

Download only; let the pipeline run diamond prepdb at runtime.

--from-blastdb

(none)

Bring-your-own: register an existing BLAST+ nr prefix; skip download.

--from-fasta

(none)

Bring-your-own: build nr.dmnd from a protein FASTA via diamond makedb.

--taxonmap / --taxonnodes / --taxonnames

(none)

With --from-fasta: prot.accession2taxid + taxdump nodes.dmp/names.dmp so staxids populate.

nr is very large (100+ GB decompressed) and can take hours to download. It is intentionally not part of get-databases all.

# Default: download preformatted BLAST+ nr and prepare it for DIAMOND
viralunity get-databases nr --path databases/ --source gcp --threads 8
# use: --nr-diamond-database databases/diamond/nr

# Bring-your-own: register an nr you already downloaded
viralunity get-databases nr --from-blastdb /data/blast/nr

# Bring-your-own: build a native .dmnd from a FASTA (with taxonomy for staxids)
viralunity get-databases nr --from-fasta nr.faa \
    --taxonmap prot.accession2taxid.FULL.gz \
    --taxonnodes taxdump/nodes.dmp --taxonnames taxdump/names.dmp
# use: --nr-diamond-database databases/diamond/nr.dmnd

virus-genome — Viral genomes database

Option

Default

Description

--path

databases

Parent directory; creates {path}/virus_genomes/.

--taxon

Viruses

NCBI taxon name to download (e.g. Viruses, coronaviridae).

--refseq/--no-refseq

on

Limit to RefSeq genomes only.

--skip-makeblastdb

off

Download and reformat files only; skip makeblastdb index creation.

Requires the NCBI Datasets CLI and BLAST+ (makeblastdb).

After downloading and reformatting genome sequences, the command automatically runs makeblastdb to build a nucleotide BLAST index alongside the FASTA. This index is required when using the similarity reference selection strategy (--reference-selection-strategy similarity).

viralunity get-databases virus-genome --taxon Viruses
# FASTA at databases/virus_genomes/viral.genomes.fasta
# BLAST DB index files at databases/virus_genomes/viral.genomes.fasta.{nhr,nin,nsq,...}
# taxids at databases/virus_genomes/genome2taxid.tsv
# use: --viral-genomes databases/virus_genomes/viral.genomes.fasta
#      --viral-taxids databases/virus_genomes/genome2taxid.tsv

host-genome — Host genome download

Option

Default

Description

--path

databases

Parent directory; creates {path}/host_genomes/.

--accession

(required)

NCBI genome accession ID (e.g. GCA_000001405.29).

 
viralunity get-databases host-genome --accession GCA_000001405.29
# fasta at databases/host_genomes/GCA_000001405.29.fasta

deacon-index — Pre-built Deacon index

Option

Default

Description

--path

databases

Parent directory; creates {path}/deacon_indexes/.

--index-name

panhuman-1

Pre-built index name (panhuman-1 or panmouse-1).

 
viralunity get-databases deacon-index --index-name panhuman-1
# index at databases/deacon_indexes/panhuman-1.idx
# use: --deacon-index databases/deacon_indexes/panhuman-1.idx

Download all databases at once

viralunity get-databases all --threads 4

viralunity build-deacon-index

Build a Deacon minimizer index from a host genome FASTA.

Option

Default

Description

--path

databases

Parent directory; creates {path}/deacon_indexes/.

--input

(required)

Input FASTA file to index.

--threads

8

Number of threads to use.

 
viralunity build-deacon-index \
    --input databases/host_genomes/GCA_000001405.29.fasta \
    --threads 4
# output at databases/deacon_indexes/GCA_000001405.29.idx

Configuration file overrides

A few tool-level parameters are tunable only through the YAML config file produced by --config-file (they are not exposed as CLI flags because they rarely need to change). The defaults preserve the historical behaviour, so most users can ignore this section.

Open the generated YAML config file after running with --create-config-only and edit the corresponding key under the # parameters section:

Config key

Default

Effect

minimap2_consensus_align_flags

-a --sam-hit-only --secondary=no --score-N=0

Flags passed to minimap2 when re-aligning the per-sample consensus back to the reference for the final multiple-sequence alignment. Used by the consensus pipelines.

diamond_max_target_seqs

1

Value of DIAMOND --max-target-seqs. Raise it if you need multiple protein hits per query (will slow the run and increase output size). Used by meta.

kraken2_extra_flags

--report-minimizer-data

Extra flags appended to every Kraken2 invocation alongside --threads and --minimum-hit-group. Set to "" to drop the minimizer-data column from the report. Used by meta.

Example: to bump DIAMOND to keep up to five protein hits per query and drop Kraken2’s minimizer column, edit the YAML config to:

diamond_max_target_seqs: 5
kraken2_extra_flags: ""

Then re-run the same viralunity meta ... invocation (without --create-config-only) and pass the edited config to Snakemake.

Per-rule CPU and RAM overrides

Both viralunity consensus and viralunity meta auto-generate a --<rule>-cpus and a --<rule>-ram option for each computationally significant Snakemake rule, so you can size individual steps without touching the global --threads / --threads-total. Every such option defaults to 2 CPUs / 4 GB and is written into the generated YAML as {rule}_cpus / {rule}_ram.

The exact set depends on the pipeline and data type — run the relevant subcommand’s --help to list them all. Representative rules (flags replace _ with - and append -cpus / -ram):

  • consensus — map_reads, trim_primer_sequences, perform_qc + detect_isnv (Illumina), infer_consensus_sequence (Nanopore). e.g. --map-reads-cpus, --map-reads-ram.

  • meta — remove_host_reads, run_megahit, run_kraken2_reads, run_kraken2_contigs, run_diamond_reads, run_diamond_contigs, run_diamond_nr, perform_qc (Illumina), run_racon / run_medaka (Nanopore), … e.g. --run-megahit-cpus, --run-kraken2-reads-ram.

viralunity meta illumina ... \
    --run-megahit-cpus 8 --run-megahit-ram 32 \
    --run-kraken2-reads-cpus 8