arog-bioinfo/MeeW-Annotation

None

Overview

Latest release: None, Last update: 2026-07-05

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

Quality control: linting: failed formatting: failed

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/MeeW-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 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

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, domains, and optional genome types.

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.

Optional columns:

  • genome_type: use mag for metagenome-assembled genomes or isolate for isolate genomes. Missing values default to mag. Funannotate2 is only targeted for eukaryotic isolate genomes, so eukaryotic isolates require domain: euk and genome_type: isolate. Eukaryotic MAGs use the existing MetaEuk/reCOGnizer path.

The workflow dynamically processes all rows defined in this sheet.

Metabolic Modeling handoff

The final workflow targets include results/stage_sheets/annotation_to_metabolic_modeling.tsv for downstream MeeW Metabolic Modeling. This Annotation-owned TSV has exactly two columns, mag and path, and contains one row per prokaryotic MAG/bin (domain: prok) only. Each path value is an absolute path to the corresponding Bakta protein FASTA output at results/bakta/{sample}/{sample}.faa. Eukaryotic samples are deliberately excluded because the current Metabolic Modeling workflow is prokaryotic-only.

Example:

sample	path	domain	genome_type
sample_a	data/sample_a.fna	prok	mag
sample_b	data/sample_b.fasta	euk	mag
fungal_isolate	data/fungal_isolate.fasta	euk	isolate

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.enabled: when true, run optional GTDB-Tk classification for prokaryotic genomes only. Eukaryotic samples are not staged or classified.

  • gtdbtk.data_dir: path to the GTDB-Tk reference database directory. The recommended path is /home/argomes/resources/gtdbtk_db; the database is not downloaded automatically and this path is only required when gtdbtk.enabled is true.

  • 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.

  • funannotate2.enabled: when true, target Funannotate2 for eukaryotic isolate genomes (domain: euk, genome_type: isolate).

  • funannotate2.db_dir: Funannotate2 database directory. The default is /home/argomes/resources/funannotate2_db.

  • funannotate2.install_db: when true, a real workflow run installs missing Funannotate2 databases into funannotate2.db_dir; dry-runs only plan this step.

  • funannotate2.databases: Funannotate2 database names to install, for example all.

  • funannotate2.extra_install: optional extra CLI options passed to funannotate2 install.

  • funannotate2.species, funannotate2.strain, funannotate2.params, funannotate2.pretrained: optional values passed to Funannotate2 steps when provided.

  • funannotate2.extra_clean, funannotate2.extra_train, funannotate2.extra_predict, funannotate2.extra_annotate: optional extra CLI options for each Funannotate2 step.

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:
  enabled: false
  data_dir: "/home/argomes/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: ""

# --------------------
# Funannotate2
# --------------------
funannotate2:
  enabled: true
  db_dir: "/home/argomes/resources/funannotate2_db"
  install_db: true
  databases:
    - all
  extra_install: ""
  species: ""
  strain: ""
  params: ""
  pretrained: ""
  extra_clean: ""
  extra_train: ""
  extra_predict: ""
  extra_annotate: ""

# ====================
# 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

deferred_sample_sheet

boolean

allow sample sheet to be generated by an upstream checkpoint

false

sample_sheet_checkpoint

string

optional upstream checkpoint name for orchestrated deferred mode

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

. enabled

boolean

enable optional GTDB-Tk classification for prokaryotic genomes only

false

. data_dir

string

path to GTDB-Tk database directory

. 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

funannotate2

parameters for Funannotate2 eukaryotic isolate annotation

yes

. enabled

boolean

enable Funannotate2 targets for eukaryotic isolate genomes

yes

true

. db_dir

string

path to the Funannotate2 database directory

yes

. install_db

boolean

install missing Funannotate2 databases during real workflow execution

yes

true

. databases

array

Funannotate2 database names to install when db_dir is missing

yes

[‘all’]

. extra_install

[‘string’, ‘null’]

extra CLI options passed to Funannotate2 install

. species

[‘string’, ‘null’]

species name passed to Funannotate2 when provided

. strain

[‘string’, ‘null’]

strain name passed to Funannotate2 when provided

. params

[‘string’, ‘null’]

Funannotate2 parameters file passed to predict when provided

. pretrained

[‘string’, ‘null’]

Funannotate2 pretrained parameters passed to predict when provided

. extra_clean

[‘string’, ‘null’]

extra CLI options passed to Funannotate2 clean

. extra_train

[‘string’, ‘null’]

extra CLI options passed to Funannotate2 train

. extra_predict

[‘string’, ‘null’]

extra CLI options passed to Funannotate2 predict

. extra_annotate

[‘string’, ‘null’]

extra CLI options passed to Funannotate2 annotate

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

Linting and formatting

Linting results
 1Lints for snakefile /tmp/tmpisep44c4/workflow/rules/prok.smk:
 2    * Mixed rules and functions in same snakefile.:
 3      Small one-liner functions used only once should be defined as lambda
 4      expressions. Other functions should be collected in a common module, e.g.
 5      'rules/common.smk'. This makes the workflow steps more readable.
 6      Also see:
 7      https://snakemake.readthedocs.io/en/latest/snakefiles/modularization.html#includes
 8
 9Lints for snakefile /tmp/tmpisep44c4/workflow/rules/euk.smk:
10    * Absolute path "/home/argomes/resources/funannotate2_db" in line 61:
11      Do not define absolute paths inside of the workflow, since this renders
12      your workflow irreproducible on other machines. Use path relative to the
13      working directory instead, or make the path configurable via a config
14      file.
15      Also see:
16      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
17    * Absolute path "/home/argomes/resources/funannotate2_db" in line 90:
18      Do not define absolute paths inside of the workflow, since this renders
19      your workflow irreproducible on other machines. Use path relative to the
20      working directory instead, or make the path configurable via a config
21      file.
22      Also see:
23      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
24    * Absolute path "/home/argomes/resources/funannotate2_db" in line 115:
25      Do not define absolute paths inside of the workflow, since this renders
26      your workflow irreproducible on other machines. Use path relative to the
27      working directory instead, or make the path configurable via a config
28      file.
29      Also see:
30      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
31    * Absolute path "/home/argomes/resources/funannotate2_db" in line 142:
32      Do not define absolute paths inside of the workflow, since this renders
33      your workflow irreproducible on other machines. Use path relative to the
34      working directory instead, or make the path configurable via a config
35      file.
36      Also see:
37      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
38    * Absolute path "/home/argomes/resources/funannotate2_db" in line 171:
39      Do not define absolute paths inside of the workflow, since this renders
40      your workflow irreproducible on other machines. Use path relative to the
41      working directory instead, or make the path configurable via a config
42      file.
43      Also see:
44      https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
45
46Lints for snakefile /tmp/tmpisep44c4/workflow/rules/stage_sheets.smk:
47    * Mixed rules and functions in same snakefile.:
48      Small one-liner functions used only once should be defined as lambda
49      expressions. Other functions should be collected in a common module, e.g.
50      'rules/common.smk'. This makes the workflow steps more readable.
51      Also see:
52      https://snakemake.readthedocs.io/en/latest/snakefiles/modularization.html#includes
53
54Lints for rule stage_gtdbtk_genomes (line 107, /tmp/tmpisep44c4/workflow/rules/prok.smk):
55    * No log directive defined:
56      Without a log directive, all output will be printed to the terminal. In
57      distributed environments, this means that errors are harder to discover.
58      In local environments, output of concurrent jobs will be mixed and become
59      unreadable.
60      Also see:
61      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
62    * Migrate long run directives into scripts or notebooks:
63      Long run directives hamper workflow readability. Use the script or
64      notebook directive instead. Note that the script or notebook directive
65      does not involve boilerplate. Similar to run, you will have direct access
66      to params, input, output, and wildcards.Only use the run directive for a
67      handful of lines.
68      Also see:
69      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
70      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
71
72Lints for rule annotation_to_metabolic_modeling_stage_sheet (line 56, /tmp/tmpisep44c4/workflow/rules/stage_sheets.smk):
73    * No log directive defined:
74      Without a log directive, all output will be printed to the terminal. In
75      distributed environments, this means that errors are harder to discover.
76      In local environments, output of concurrent jobs will be mixed and become
77      unreadable.
78      Also see:
79      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
80    * Migrate long run directives into scripts or notebooks:
81      Long run directives hamper workflow readability. Use the script or
82      notebook directive instead. Note that the script or notebook directive
83      does not involve boilerplate. Similar to run, you will have direct access
84      to params, input, output, and wildcards.Only use the run directive for a
85      handful of lines.
86      Also see:
87      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
88      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
Formatting results
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 2[DEBUG] In file "/tmp/tmpisep44c4/workflow/rules/stage_sheets.smk":  Formatted content is different from original
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 9[INFO] 1 file(s) would be changed 😬
10[INFO] 6 file(s) would be left unchanged 🎉
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12snakefmt version: 0.11.5