Introduction Usage Parameters Output

Introduction

nf-core/bacmodel is a bioinformatics pipeline for functional annotation and metabolic modeling of bacterial genomes. The pipeline takes genome assemblies (FASTA files) as input and performs:

  • Genome annotation (Prokka or Bakta)
  • Macromolecular system detection (MacSyFinder)
  • Phenotype prediction (TRAITAR)
  • Metabolic model reconstruction (CarveMe and/or Gapseq)

Pipeline workflow

The pipeline processes genome assemblies through multiple tools with the following data flow:

  1. Prokka or Bakta performs primary genome annotation
    • Input: Raw genome FASTA files
    • Output: Protein FASTA (.faa), GFF files, and various annotation formats
  2. CarveMe builds metabolic models using protein-based annotation
    • Input: Protein FASTA (.faa) from Prokka/Bakta
    • Uses annotated proteins to reconstruct metabolic networks
  3. Gapseq builds metabolic models independently
    • Input: Raw genome FASTA files (runs its own internal annotation)
    • Does not depend on Prokka/Bakta output
  4. MacSyFinder searches for macromolecular systems
    • Input: Protein FASTA (.faa) from Prokka/Bakta
    • Detects secretion systems (TXSS) and other molecular machines
  5. TRAITAR predicts phenotypic traits
    • Input: Protein FASTA (.faa) from Prokka/Bakta
    • Predicts metabolic and physiological capabilities

This architecture means that if you choose Prokka (--annotation_tool prokka) or Bakta (--annotation_tool bakta), the annotation will be shared by CarveMe, MacSyFinder, and TRAITAR. Gapseq operates independently with its own annotation step, allowing comparison between different metabolic modeling approaches.

Samplesheet input

You will need to create a samplesheet with information about the genome assemblies you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated or tab-separated file with 2 columns, and a header row as shown in the example below.

--input '[path to samplesheet file]'

Samplesheet format

The samplesheet should contain at minimum two required columns: sample and fasta. Four optional columns - medium_gapseq, medium_gapseq_csv, medium_carveme, and medium_carveme_tsv - can be added to specify growth media for metabolic modeling. An example is shown below:

samplesheet.csv
sample,fasta,medium_gapseq,medium_gapseq_csv,medium_carveme,medium_carveme_tsv
SAMPLE1,/path/to/sample1_assembly.fasta,,,,
SAMPLE2,/path/to/sample2_assembly.fasta,LB,,LB,
SAMPLE3,/path/to/sample3_assembly.fasta,,/path/to/custom_medium.csv,M9,/path/to/custom_mediadb.tsv
Column Description
sample Required. Custom sample name. Cannot contain spaces - use underscores (_) instead, or the pipeline will fail with a validation error.
fasta Required. Path (absolute or relative) or URL to genome assembly file in FASTA format. File can be gzipped (.fasta.gz, .fa.gz, .fna.gz) or uncompressed (.fasta, .fa, .fna).
medium_gapseq Optional. Built-in growth medium name for gapseq (e.g., LB, M9). Mutually exclusive with medium_gapseq_csv - set at most one of the two. If both are empty, gapseq auto-predicts the medium. See Metabolic modeling media for details.
medium_gapseq_csv Optional. Path to a custom gapseq medium CSV file. Mutually exclusive with medium_gapseq - the pipeline errors out if both are set for the same sample. See Metabolic modeling media for details.
medium_carveme Optional. Growth medium name for CarveMe gap-filling (e.g., LB, M9), selected from whichever media database applies (see medium_carveme_tsv and --carveme_mediadb below). If empty, no gap-filling is performed. See Metabolic modeling media for details.
medium_carveme_tsv Optional. Path to a custom per-sample CarveMe media database TSV file. Overrides the global --carveme_mediadb for this sample only; combine with medium_carveme to pick a medium name out of it. See Metabolic modeling media for details.

An example samplesheet has been provided with the pipeline.

Metabolic modeling media

The optional medium_gapseq/medium_gapseq_csv and medium_carveme/medium_carveme_tsv columns allow you to specify growth media for metabolic model reconstruction on a per-sample basis. This is useful when:

  • You have experimental growth data for specific media
  • You want to ensure models can simulate growth under specific conditions
  • Different bacterial species in your dataset require different growth conditions

Gapseq media (medium_gapseq / medium_gapseq_csv)

Note

Gapseq automatically downloads reference sequence databases on first use. The pipeline uses container options to ensure each process has a writable directory for database initialization.

Default behavior (both columns empty): Gapseq uses its built-in anaerobic complete medium for gap-filling, which is a permissive rich medium allowing comprehensive metabolic reconstruction.

Built-in media names (medium_gapseq): You can specify predefined media names that gapseq recognizes: LB (Luria-Bertani rich medium) or M9 (M9 minimal medium). See the gapseq medium documentation for available options.

Custom media files (medium_gapseq_csv): Provide a path to a CSV file with three columns:

  • compounds: Metabolite IDs (e.g., cpd00027 for D-Glucose)
  • name: Metabolite names
  • maxFlux: Maximum uptake rate in mmol/gDW/h

The maxFlux parameter defines the maximum inflow flux for each compound. Use lower values (5-10) for carbon sources, medium values (10-20) for O2/CO2, and high values (100) for abundant nutrients like water, ions, and cofactors. For detailed information, see the gapseq medium documentation.

Example custom medium CSV:

compounds,name,maxFlux
cpd00027,D-Glucose,5
cpd00007,O2,10
cpd00009,Phosphate,100
cpd00013,NH3,100

An example medium CSV with this exact format is shipped with the pipeline - copy it and adjust the compounds/fluxes for your own medium.

How the medium is chosen: gapseq has no notion of “combining” two media files - per sample, it is either your file, or its own prediction (optionally nudged with -c, see below). medium_gapseq and medium_gapseq_csv are mutually exclusive - the pipeline errors out if both are set for the same sample. The pipeline resolves this as follows, in order:

  1. medium_gapseq_csv is set - that CSV file is used directly for gap-filling. Gapseq’s medium prediction step is skipped entirely for that sample, and any --gapseq_medium_args you’ve set are ignored for it.
  2. Both columns are empty - gapseq predicts the medium itself from the draft model and pathway results. If --gapseq_medium_args is set (e.g. -c "cpd00007:0"), those flags are applied on top of the prediction to add/remove specific compounds.
  3. medium_gapseq is a built-in name (LB, M9) - only honoured in the default doall mode, where it’s passed straight to gapseq doall -m. It has no effect in the custom/stepwise mode described below, since gapseq’s standalone medium command doesn’t accept predefined medium names - only a draft model and pathway table.

Tuning the default doall mode: the default (non-advanced) workflow runs gapseq doall with a fixed -b 200 -A diamond (bit-score cutoff and aligner). Override it with --gapseq_doall_args, e.g. --gapseq_doall_args '-b 150 -A diamond' to loosen the bit-score cutoff, without switching to the full custom/stepwise mode below. Note -b here is gapseq’s alignment bit-score threshold, not a contig-length filter - contig length is controlled separately via --min_contig_length (only used by Bakta annotation).

Advanced customization: For advanced users who need full control over individual gapseq subworkflows (find, find-transport, draft, medium, and fill), provide custom arguments for specific steps. The pipeline automatically switches to custom workflow mode when any of these are set:

nextflow run nf-core/bacmodel \
  --input samplesheet.csv \
  --skip_gapseq false \
  --gapseq_find_args '-b 200 -p nuc' \
  --gapseq_findtransport_args '-b 180' \
  --gapseq_fill_args '-b 100' \
  --outdir results \
  -profile docker

Available parameters for customization:

  • --gapseq_find_args - Pathway prediction (default: -b 200 -A diamond)
  • --gapseq_findtransport_args - Transporter prediction (default: -b 200)
  • --gapseq_draft_args - Draft model construction (default: none)
  • --gapseq_medium_args - Add/remove specific compounds on top of the predicted medium, e.g. -c "cpd00007:0" to remove oxygen (default: none; ignored for samples with their own medium_gapseq_csv file)
  • --gapseq_fill_args - Gap-filling (default: none)

Refer to the gapseq documentation for all available parameters for each command.

Example - adjust bit scores:

--gapseq_find_args '-b 150 -A diamond' \
--gapseq_findtransport_args '-b 150'

Example - custom pathway completion threshold:

--gapseq_draft_args '-c 0.5'

Database pre-download: The pipeline automatically downloads the gapseq reference sequence database once before parallel reconstruction. This prevents race conditions and delays when processing hundreds of genomes simultaneously - a key improvement over running gapseq manually where each process would attempt to download the database independently.

Tip

Only provide custom gapseq_*_args if you need fine-grained control over reconstruction parameters. The default doall mode (no custom args) is recommended for most users as it provides sensible defaults and is well-tested.

CarveMe media (medium_carveme / medium_carveme_tsv)

Default behavior (all columns empty): No gap-filling is performed - CarveMe still produces a draft reconstruction from its reaction scores, but it is not fitted to grow on any particular medium. There is no “complete”/rich-medium fallback: CarveMe’s own bundled media database only defines LB, LB[-O2], M9, M9[-O2], M9[glyc], and PHOTO - passing an unrecognised name (e.g. complete) makes CarveMe print Medium <name> not in database, ignored. and silently skip gap-filling, so the pipeline only passes --gapfill when medium_carveme is actually set.

Media names (medium_carveme): Specify a media name from the CarveMe media database (e.g., LB, M9) to gap-fill for a specific growth medium. This column always holds a name, never a file path - it selects an entry out of whichever media database applies to that sample, checked in this order: a per-sample medium_carveme_tsv first, then the run-wide --carveme_mediadb, then CarveMe’s own bundled default database (LB, LB[-O2], M9, M9[-O2], M9[glyc], PHOTO).

Custom media database, run-wide (--carveme_mediadb): To use a custom CarveMe media database for every sample, provide the --carveme_mediadb parameter pointing to a TSV file with four columns: medium (the name referenced by medium_carveme), description, compound (BiGG-style metabolite ID), and name. One row per compound, so a single file can define several media by repeating the medium value across their rows.

Custom media database, per-sample (medium_carveme_tsv): To use a different media database for one specific sample (e.g. a species with unusual growth requirements), set medium_carveme_tsv to a TSV file in that same four-column format. It overrides --carveme_mediadb for that sample only - other samples keep using the run-wide database (or CarveMe’s default, if --carveme_mediadb isn’t set).

An example media database with this format (covering LB and M9) is shipped with the pipeline - copy it and extend it with your own media/compounds. Use it as either the --carveme_mediadb value or a medium_carveme_tsv value; the format is identical.

Example usage:

nextflow run nf-core/bacmodel \
  --input samplesheet.csv \
  --outdir results \
  --carveme_mediadb /path/to/custom_media_db.tsv \
  -profile docker
Note

The medium_carveme column should contain media names that exist in the applicable media database, not file paths - use medium_carveme_tsv (per-sample) or --carveme_mediadb (run-wide) for the file itself.

Tip

Leaving all media columns empty gives each tool its own default: gapseq falls back to its permissive built-in complete medium, while CarveMe skips gap-filling entirely and returns an ungap-filled draft reconstruction (see above). Specify media explicitly when you need comparable, gap-filled models from both tools, or when constraining to experimentally validated growth conditions (e.g., use M9 for both tools for minimal medium experiments, or LB for rich medium cultures).

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/bacmodel --input ./samplesheet.csv --outdir ./results -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Analysis options

The pipeline provides several options to customize the analysis:

Annotation tool

Choose between Prokka (default) and Bakta for genome annotation:

--annotation_tool prokka  # Default
--annotation_tool bakta   # Requires --baktadb or --baktadb_download

Enable/disable analysis tools

Control which functional analysis tools to run:

--skip_macsyfinder true  # Default: false
--skip_traitar true      # Default: false
--skip_carveme true      # Default: false
--skip_gapseq false      # Default: true
--skip_memote false      # Default: true - Evaluate metabolic model quality
Note

MEMOTE requires at least one metabolic modeling tool (skip_carveme=false or skip_gapseq=false) to be enabled. MEMOTE will evaluate the quality of all generated metabolic models (SBML XML format) and produce HTML reports with comprehensive quality metrics.

MacSyFinder search mode

MacSyFinder’s detection power depends on whether gene order (synteny) is known, controlled via --macsyfinder_db_type:

--macsyfinder_db_type unordered         # Default - MAGs / draft assemblies with unreliable gene order
--macsyfinder_db_type ordered_replicon  # A single complete (or nearly complete) genome
--macsyfinder_db_type gembase           # Multiple complete replicons in one gembase-formatted database

unordered is the safe default for the fragmented assemblies and MAGs this pipeline typically handles. If your input assemblies are complete, single-replicon genomes, ordered_replicon enables MacSyFinder’s synteny-aware detection rules for more precise system calls. See the MacSyFinder documentation for details on how each mode affects detection.

Database options

Specify or download required databases:

--pfamdb /path/to/pfam      # Required if skip_traitar=false
--pfamdb_download true      # Download Pfam database automatically


--baktadb /path/to/bakta    # Required if annotation_tool=bakta
--baktadb_download true     # Download Bakta database automatically

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

Warning

Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/bacmodel -profile docker -params-file params.yaml

with:

params.yaml
input: './samplesheet.csv'
outdir: './results/'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/bacmodel

Reproducibility

It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/bacmodel releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Tip

If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments

Note

These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen)

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to check if your system is supported, please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer environment.

  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters
  • docker
    • A generic configuration profile to be used with Docker
  • singularity
    • A generic configuration profile to be used with Singularity
  • podman
    • A generic configuration profile to be used with Podman
  • shifter
    • A generic configuration profile to be used with Shifter
  • charliecloud
    • A generic configuration profile to be used with Charliecloud
  • apptainer
    • A generic configuration profile to be used with Apptainer
  • wave
    • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
  • conda
    • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'
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