Setup

This page covers everything you need before running either pipeline: installing ViralUnity, generating sample sheets for the example data, and downloading the reference databases. Most of it is one-time work; once you finish, you can move straight to either pipeline walkthrough.

1. Install ViralUnity

ViralUnity is distributed alongside a conda environment that pins all of the underlying bioinformatics tools (Snakemake, minimap2, fastp, Kraken2, DIAMOND, Krona, clair3, Medaka, …). The per-rule conda environments under viralunity/scripts/envs/ are picked up automatically by Snakemake at runtime.

git clone https://github.com/InstitutoTodosPelaSaude/ViralUnity.git
cd ViralUnity
conda env create -n viralunity -f environment.yml
conda activate viralunity
pip install -e .

Verify:

viralunity --version
viralunity --help

See Installation for the macOS Apple Silicon caveat (the clair3 environment can be sensitive there), for development installs (pip install -e ".[dev]"), and for the Troubleshooting note if the first pipeline run fails on conda env creation.

1b. Build per-rule environments

Each pipeline rule runs in its own conda environment. ViralUnity caches these envs across runs so they only need to be built once; pre-warm the cache now:

viralunity setup --pipelines all

This materializes every per-rule env into ~/.cache/viralunity/conda-envs/ (override with --conda-prefix PATH). The subsequent pipeline runs in this tutorial — and every future run on this machine — reuse the cache and skip env creation. Restricting to a single pipeline (e.g. --pipelines consensus-illumina) is faster if you only plan to run one flavor; add the others later.

If setup fails with CreateCondaEnvironmentException, see Troubleshooting.

1c. Download the example data

The example dataset used throughout this tutorial is hosted as a single tarball. Download it once at the repo root:

curl -L -o my_test_data.tar.gz https://itps-nimbus.nyc3.cdn.digitaloceanspaces.com/my_test_data.tar.gz
tar -xzf my_test_data.tar.gz
rm my_test_data.tar.gz

You should now have:

my_test_data/
├── illumina_data/
│   ├── itps-0001_R1.fastq.gz
│   ├── itps-0001_R2.fastq.gz
│   ├── itps-0002_R1.fastq.gz
│   └── itps-0002_R2.fastq.gz
└── nanopore_data/
    ├── barcode05.itps-0003.fastq.gz
    └── barcode09.itps-0004.fastq.gz

my_test_data/ is gitignored, so the download stays out of version control. The consensus tutorial additionally needs the ARTIC nCoV-2019 V3 reference FASTA and primer BED — grab them from the ARTIC primer-schemes repo (raw URLs: nCoV-2019.reference.fasta, nCoV-2019.scheme.bed) and place them somewhere you can point --reference / --primer-scheme at (the rest of this tutorial uses databases/refs/).

2. Generate sample sheets

A sample sheet is a no-header CSV that tells ViralUnity which FASTQ files belong to which sample:

• Illumina — 3 columns: sample_id,R1_path,R2_path

• Nanopore — 2 columns: sample_id,fastq_path

You can write the CSV by hand, but viralunity create-samplesheet builds it for you by scanning a run directory.

The bundled test data has all FASTQs directly inside the run directory (rather than one subdirectory per sample), so we pass --level 0 and a small naming convention:

# Illumina: FASTQs in my_test_data/illumina_data/, sample ID = portion before first '_'
viralunity create-samplesheet \
    --input  my_test_data/illumina_data/ \
    --output samples_illumina.csv \
    --level 0 \
    --separator _ \
    --pattern R1

This writes:

itps-0001,my_test_data/illumina_data/itps-0001_R1.fastq.gz,my_test_data/illumina_data/itps-0001_R2.fastq.gz
itps-0002,my_test_data/illumina_data/itps-0002_R1.fastq.gz,my_test_data/illumina_data/itps-0002_R2.fastq.gz

# Nanopore: FASTQs in my_test_data/nanopore_data/, sample ID = portion before first '.'
viralunity create-samplesheet \
    --input  my_test_data/nanopore_data/ \
    --output samples_nanopore.csv \
    --level 0 \
    --separator . \
    --pattern barcode

Which writes:

barcode05,my_test_data/nanopore_data/barcode05.itps-0003.fastq.gz
barcode09,my_test_data/nanopore_data/barcode09.itps-0004.fastq.gz

Tip

The reference sample sheets that ship with this checkout are at my_results/samples_illumina.csv and my_results/samples_nanopore.csv — use them to compare against what you just generated.

For your own data, the same command works with --level 1 (one subdirectory per sample, the default) if your run is organised that way; see the Commands reference for the full table of options.

3. Decide which databases you need

Different pipelines need different reference data:

Pipeline	Required	Optional (enable extra features)
consensus	reference FASTA	primer-scheme BED (amplicon data), adapter FASTA (custom adapters)
meta (minimum)	Kraken2 index, Krona taxonomy	NCBI taxdump (filters), host FASTA or Deacon index (dehosting)
meta + DIAMOND	Kraken2, Krona, taxdump, DIAMOND DB, DIAMOND taxids	(same as above)
meta + --run-reference-assembly	the above, viral genomes FASTA + genome2taxid.tsv, taxdump	BLAST index (built automatically) — for the similarity selection strategy

The consensus pipeline does not need any database downloads — only a reference FASTA you already have. The rest of this section sets you up for the metagenomic walkthrough.

4. Download the metagenomic databases

The shortcut command grabs the four most-used databases in one go (Kraken2 + Krona + taxdump + DIAMOND). The conda environment must be active because the Krona step needs ktUpdateTaxonomy.sh:

conda activate viralunity
viralunity get-databases all --path databases/ --threads 4

Plan for the download. Approximate sizes on disk after extraction:

Database	Path created	Approx. size	Used by
Kraken2 viral	databases/kraken2/	~1.1 GB	--kraken2-database
Krona taxonomy	databases/krona/taxonomy/	~150 MB	--krona-database
NCBI taxdump	databases/taxdump/	~600 MB	--taxdump (filters, reference assembly)
DIAMOND viral	databases/diamond/	~400 MB	--diamond-database, --taxids

If you only plan to use Kraken2 on reads, you can skip get-databases diamond and only run the first three. Each subcommand is also available standalone — see [viralunity get-databases --help](../commands.md#viralunity-get-databases).

Three more databases for the full meta walkthrough

The all subcommand intentionally leaves out three databases that are large and only needed for specific features:

# Viral genomes + per-genome taxid map + BLAST index — required for --run-reference-assembly
viralunity get-databases virus-genome --path databases/

# Deacon human host index — recommended for dehosting (faster than minimap2 vs a host FASTA)
viralunity get-databases deacon-index --path databases/ --index-name panhuman-1

# NCBI nr protein database — required for --run-nr-validation (false-positive reduction).
# WARNING: 100+ GB, hours to download. aws/gcp mirrors are usually faster than ncbi.
viralunity get-databases nr --path databases/ --source gcp --threads 8

virus-genome ships with viral.genomes.fasta, genome2taxid.tsv, and the BLAST .nhr/.nin/.nsq index files (used for the similarity reference-selection strategy). The Deacon index lands at databases/deacon_indexes/panhuman-1.idx. nr downloads NCBI’s preformatted BLAST+ nr into databases/diamond/nr.* and prepares it for DIAMOND; pass --nr-diamond-database databases/diamond/nr. If you already have nr (or work air-gapped), use --from-blastdb <prefix> or --from-fasta <nr.faa> instead of downloading — see [commands.md](../commands.md#nr--ncbi-nr-database-for-nr-validation).

Tip

If you want to dehost against a non-human genome, use viralunity get-databases host-genome --accession <NCBI accession> to download a host FASTA, then either pass it directly via --host-reference (minimap2 dehosting) or build a Deacon index with viralunity build-deacon-index --input <fasta> for a faster path.

5. Verify the setup

After downloading, you should have:

databases/
├── kraken2/           # Kraken2 index files
├── krona/taxonomy/    # taxonomy.tab and friends
├── taxdump/           # nodes.dmp, names.dmp, …
├── diamond/           # viral.dmnd, protein2taxid.tsv
├── virus_genomes/     # viral.genomes.fasta(.nhr/.nin/.nsq), genome2taxid.tsv
└── deacon_indexes/    # panhuman-1.idx

A quick sanity check:

ls databases/
ls databases/diamond/ databases/virus_genomes/

You are now ready to:

• Run the consensus pipeline, or

• Run the metagenomics pipeline.