arog-bioinfo/MAW-Annotation

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Overview

Latest release: None, Last update: 2026-06-14

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=arog-bioinfo/MAW-Annotation

Quality control: linting: passed formatting: passed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/arog-bioinfo/MAW-Annotation . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow using apptainer/singularity, use

snakemake --cores all --sdm apptainer

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Workflow configuration

The workflow processes one or more Metagenome-Assembled Genomes (MAGs) per run. Configure inputs and tool options in config/config.yaml.

General input

sample_sheet: path to a TSV file containing sample names, input FASTA paths, and domains.

Sample sheet format

The sample sheet is a tab-separated file. Required columns:

sample: unique identifier/name for the MAG or isolate.
path: path to the input genome file in FASTA format (.fasta, .fna, .fa).
domain: annotation path for the sample. Use prok for prokaryotic samples and euk for eukaryotic samples.

The workflow dynamically processes all rows defined in this sheet.

Example:

sample	path	domain
sample_a	data/sample_a.fna	prok
sample_b	data/sample_b.fasta	euk

Optional QA filtering

qa_filter.enabled: when true, filter samples before annotation targets are expanded.
qa_filter.min_completeness: minimum completeness required for a sample to pass.
qa_filter.max_contamination: maximum contamination allowed for a sample to pass.
qa_filter.checkm2_reports: CheckM2 TSV reports for prokaryotic samples. Reports must include Name, Completeness, and Contamination.
qa_filter.eukcc_reports: EukCC CSV reports for eukaryotic samples. Reports must include bin, completeness, and contamination.
qa_filter.missing_sample: behavior when a sample is absent from QA reports. Supported values are error, keep, and drop.

Prokaryotic annotation path

prodigal.extra: optional extra options string passed to the Prodigal wrapper.
bakta.db: path to the Bakta database directory.
bakta.extra: optional extra options string passed to the Bakta wrapper.
gtdbtk.data_dir: path to the GTDB-Tk reference database directory.
gtdbtk.extra: optional extra options string passed to the GTDB-Tk wrapper.
recognizer_prok.resources_dir: path to the prokaryotic reCOGnizer resources database directory.
recognizer_prok.extra: optional extra options string passed to the prokaryotic reCOGnizer wrapper.
upimapi.db: UPIMAPI built-in database name to use, for example swissprot. Leave empty when using upimapi.db_custom.
upimapi.db_custom: path to a custom UPIMAPI database FASTA. Leave empty when using upimapi.db.
upimapi.resources_dir: path to the UPIMAPI resources database directory.
upimapi.extra: optional extra options string passed to the UPIMAPI wrapper.
upimapi.skip_db_check_if_exists: when true, automatically add --skip-db-check only if the selected UPIMAPI database FASTA already exists in upimapi.resources_dir or upimapi.db_custom exists.

Eukaryotic annotation path

metaeuk.db: path to the MetaEuk reference database, such as a UniProt database.
metaeuk.extra: optional extra options string passed to the MetaEuk wrapper.
recognizer_euk.resources_dir: path to the eukaryotic reCOGnizer resources database directory.
recognizer_euk.custom_db: path to a KOG/custom database for eukaryotic reCOGnizer. Leave empty to disable a custom eukaryotic database.
recognizer_euk.extra: optional extra options string passed to the eukaryotic reCOGnizer wrapper.

Thread presets

threads: dictionary containing computational resource presets.
threads.high: thread count for high-resource steps.
threads.medium: thread count for medium-resource steps.
threads.low: thread count for low-resource steps.

Example config

# ====================
# General Input
# ====================
sample_sheet: "config/samples.tsv"

# ====================
# Quality Filtering
# ====================
qa_filter:
  enabled: false
  min_completeness: 50.0
  max_contamination: 10.0
  checkm2_reports: []
  eukcc_reports: []
  missing_sample: "error"

# ====================
# Prokaryotic Annotation
# ====================

# --------------------
# Prodigal
# --------------------
prodigal:
  extra: "-p meta -f gff"

# --------------------
# Bakta
# --------------------
bakta:
  db: "resources/bakta_db/db-light"
  extra: ""

# --------------------
# GTDB-Tk
# --------------------
gtdbtk:
  data_dir: "resources/gtdbtk_db"
  extra: ""

# --------------------
# reCOGnizer Prokaryotic
# --------------------
recognizer_prok:
  resources_dir: "resources/recognizer_db"
  extra: ""

# --------------------
# UPIMAPI
# --------------------
upimapi:
  db: "swissprot"
  db_custom: ""
  resources_dir: "resources/upimapi_db"
  extra: ""
  skip_db_check_if_exists: true

# ====================
# Eukaryotic Annotation
# ====================

# --------------------
# MetaEuk
# --------------------
metaeuk:
  db: "resources/metaeuk_db/uniprot_db"
  extra: ""

# --------------------
# reCOGnizer Eukaryotic
# --------------------
recognizer_euk:
  resources_dir: "resources/recognizer_db"
  custom_db: ""
  extra: ""

# ====================
# Computational Resources
# ====================
threads:
  high: 16
  medium: 8
  low: 1

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter	Type	Description	Required	Default
sample_sheet	string	path to sample sheet, mandatory	yes	config/samples.tsv
qa_filter		external CheckM2/EukCC QA filtering applied before target expansion	yes
. enabled	boolean	enable filtering by external QA reports		false
. min_completeness	number	minimum completeness required to keep a sample		50.0
. max_contamination	number	maximum contamination allowed to keep a sample		10.0
. checkm2_reports	array	CheckM2 TSV report paths for prokaryotic samples		[]
. eukcc_reports	array	EukCC CSV report paths for eukaryotic samples		[]
. missing_sample	string	behavior when a sample is missing from its QA report		error
prodigal		parameters for Prodigal gene prediction	yes
. extra	string	extra CLI options passed to Prodigal wrapper
bakta		parameters for Bakta annotation	yes
. db	string	path to Bakta database directory	yes
. extra	string	extra CLI options passed to Bakta wrapper
gtdbtk		parameters for GTDB-Tk classification	yes
. data_dir	string	path to GTDB-Tk database directory	yes
. extra	string	extra CLI options passed to GTDB-Tk wrapper
metaeuk		parameters for MetaEuk gene prediction	yes
. db	string	path to MetaEuk reference database	yes
. extra	string	extra CLI options passed to MetaEuk wrapper
recognizer_prok		parameters for prokaryotic reCOGnizer domain annotation	yes
. resources_dir	string	path to prokaryotic reCOGnizer database directory	yes
. extra	string	extra CLI options passed to prokaryotic reCOGnizer wrapper
recognizer_euk		parameters for eukaryotic reCOGnizer domain annotation	yes
. resources_dir	string	path to eukaryotic reCOGnizer database directory	yes
. custom_db	string	path to KOG/custom database for eukaryotic reCOGnizer; empty disables custom DB
. extra	string	extra CLI options passed to eukaryotic reCOGnizer wrapper
upimapi		parameters for UPIMAPI functional annotation	yes
. db	string	UPIMAPI built-in database name to use (for example, swissprot)
. db_custom	string	path to a custom UPIMAPI database FASTA; leave empty when using db
. resources_dir	string	path to UPIMAPI resources database directory
. extra	string	extra CLI options passed to UPIMAPI wrapper
. skip_db_check_if_exists	boolean	automatically pass –skip-db-check when the selected UPIMAPI database FASTA already exists in the configured resources directory		true
threads		computational resources presets	yes
. high	integer
. medium	integer
. low	integer