nf-core/funcscan

Edit

Introduction

nf-core/funcscan is a pipeline for efficient and parallelised screening of long nucleotide sequences such as contigs for antimicrobial peptide genes, antimicrobial resistance genes, and biosynthetic gene clusters. It can additionally identify the taxonomic origin of the sequences.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/funcscan --input samplesheet.csv --outdir <OUTDIR> -profile docker --run_<amp/arg/bgc>_screening

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

To run any of the three screening workflows (AMP, ARG, and/or BGC) or taxonomic classification, switch them on by adding the respective flag(s) to the command:

• --run_amp_screening
• --run_arg_screening
• --run_bgc_screening
• --run_taxa_classification

When switched on, all tools of the given workflow will be run by default. If you don’t need specific tools, you can explicitly skip them. The exception is HMMsearch, which needs to be explicitly switched on and provided with HMM screening files (AMP and BGC workflows, see parameter documentation). For the taxonomic classification, MMseqs2 is currently the only tool implemented in the pipline.

Example: You want to run AMP and ARG screening but you don’t need the DeepARG tool of the ARG workflow and the Macrel tool of the AMP workflow. Your command would be:

nextflow run nf-core/funcscan --input samplesheet.csv --outdir <OUTDIR> -profile docker --run_arg_screening --arg_skip_deeparg --run_amp_screening --amg_skip_macrel
 

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

work            # Directory containing temporary files required for the run
<OUTDIR>        # Final results (location specified 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>.

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

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

with params.yaml containing:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>

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

Samplesheet input

nf-core/funcscan takes FASTA files as input, typically contigs or whole genome sequences. To supply these to the pipeline, you will need to create a samplesheet with information about the samples you would like to analyse. Use this parameter to specify its location.

--input '[path to samplesheet file]'

The input samplesheet has to be a comma-separated file (.csv) with 2 (sample, and fasta) or 4 columns (sample, fasta, protein, gbk), and a header row as shown in the examples below.

If you already have annotated contigs with peptide sequences and an annotation file in Genbank format (.gbk. or .gbff), you can supply these to the pipeline using the optional protein and gbk columns. If these additional columns are supplied, pipeline annotation (i.e. with bakta, prodigal, pyrodigal or prokka) will be skipped and the corresponding annotation files used instead.

For two columns (without pre-annotated data):

sample,fasta
sample_1,/<path>/<to>/wastewater_metagenome_contigs_1.fasta.gz
sample_2,/<path>/<to>/wastewater_metagenome_contigs_2.fasta.gz

For four columns (with pre-annotated data):

sample,fasta,protein,gbk
sample_1,/<path>/<to>/wastewater_metagenome_contigs_1.fasta.gz,/<path>/<to>/wastewater_metagenome_contigs_1.faa,/<path>/<to>/wastewater_metagenome_contigs_1.fasta.gbk
sample_2,/<path>/<to>/wastewater_metagenome_contigs_2.fasta.gz,/<path>/<to>/wastewater_metagenome_contigs_2.faa,/<path>/<to>/wastewater_metagenome_contigs_2.fasta.gbk

An example samplesheet has been provided with the pipeline.

Notes on screening tools and taxonomic classification

The implementation of some tools in the pipeline may have some particular behaviours that you should be aware of before you run the pipeline.

MMseqs2

MMseqs2 is currently the only taxonomic classification tool used in the pipeline to assign a taxonomic lineage to the input contigs. The database used to assign the taxonomic lineage can either be:

• A custom based database created by the user using mmseqs createdb externally and beforehand. If this flag is assigned, this database takes precedence over the default database in --mmseqs_db_id.

  --taxa_classification_mmseqs_db '<path>/<to>/<mmsesqs_custom_database>/<directory>'

  The contents of the directory should have files such as <dbname>.version and <dbname>.taxonomy in the top level.

• An MMseqs2 ready database. These databases were compiled by the developers of MMseqs2 and can be called using their labels. All available options can be found here. Only use those databases that have taxonomy files available (i.e., Taxonomy == Yes). By default mmseqs2 in the pipeline uses ‘Kalamari’, and runs an aminoacid based alignment. However, if the user requires a more comprehensive taxonomic classification, we recommend the use of GTDB, but for that please remember to increase the memory, CPU threads and time required for the process MMSEQS_TAXONOMY.

  --taxa_classification_mmseqs_db_id 'Kalamari'

antiSMASH

antiSMASH has a minimum contig parameter, in which only contigs of a certain length (or longer) will be screened. In cases where no hits are found in these, the tool ends successfully without hits. However if no contigs in an input file reach that minimum threshold, the tool will end with a ‘failure’ code, and cause the pipeline to crash.

When the annotation is run with Prokka, the resulting .gbk file passed to antiSMASH may produce the error translation longer than location allows and end the pipeline run. This Prokka bug has been reported before (see discussion on GitHub) and is not likely to be fixed soon.

Databases and reference files

Various tools of nf-core/funcscan use databases and reference files to operate.

nf-core/funcscan offers the functionality to auto-download databases for you, and as these databases can be very large, and we suggest to store these files in a central place from where you can reuse them across pipeline runs.

If your infrastructure has internet access (particularly on compute nodes), we highly recommend allowing the pipeline to download these databases for you on a first run, saving these to your results directory with --save_db, then moving these to a different location (in case you wish to delete the results directory of this first run). An exception to this is HMM files where no auto-downloading functionality is possible.

As a reference, we will describe below where and how you can obtain databases and reference files used for tools included in the pipeline.

Bakta

nf-core/funcscan offers multiple tools for annotating input sequences. Bakta is a new tool touted as a bacteria-only successor to the well-established Prokka.

To supply the preferred Bakta database (and not have the pipeline download it for every new run), use the flag --annotation_bakta_db. The full or light Bakta database must be downloaded from the Bakta Zenodo archive.

You can do this by installing via conda and using the dedicated command

conda create -n bakta -c bioconda bakta
conda activate bakta
 
bakta_db download --output <LOCATION_TO_STORE> --type <full|light>

Alternatively, you can manually download the files via the links which can be found on the Bakta GitHub repository.

Once downloaded this must be untarred:

tar xvzf db.tar.gz

And then passed to the pipeline with:

--annotation_bakta_db /<path>/<to>/<db>/

The contents of the directory should have files such as *.dmnd in the top level.

hmmsearch

nf-core/funcscan allows screening of sequences for functional genes associated with various natural product types via Hidden Markov Models (HMMs) using hmmsearch.

This requires supplying a list of HMM files ending in .hmm, that have models for the particular molecule(s) or BGCs you are interested in. You can download these files from places such as PFAM for antimicrobial peptides (AMP), or the antiSMASH GitHub repository for biosynthetic gene cluster related HMMs, or create them yourself.

You should place all HMMs in a directory, supply them to the AMP or BGC workflow and switch hmmsearch on:

--amp_run_hmmsearch --amp_hmmsearch_models "/<path>/<to>/<amp>/*.hmm"

AMPcombi

For AMPcombi, nf-core/funcscan will by default download the most recent version of the DRAMP database as a reference database, and modifies the files for aligning the AMP hits in the AMP workflow.

nf-core/funcscan currently provides a python3 helper script to do these steps.

mkdir -p ampcombi/amp_ref_database
cd ampcombi/
wget https://github.com/nf-core/funcscan/raw/<PIPELINE_VERSION>/bin/ampcombi_download.py
python3 ampcombi_download.py

However, the user can also supply their own custom AMP database by following the guidelines in AMPcombi. This can then be passed to the pipeline with:

--amp_ampcombi_db '/<path>/<to>/<ampcombi_database>

The contents of the directory should have files such as *.dmnd and *.fasta in the top level.

Abricate

The default ABRicate installation comes with a series of ‘default’ databases:

• NCBI AMRFinderPlus (ncbi)
• CARD (card)
• ResFinder (resfinder)
• ARG-ANNOT (argannot)
• MEGARES (megares)
• EcOH (echo)
• PlasmidFinder (plasmidfinder)
• VFDB (vfdb)
• Ecoli_VF (ecoli_vf)

Each can be specified by using the nf-core/funcscan flag, for example for card: --arg_abricate_db_id card.

ABRicate also allows you to download additional and/or use custom databases. For both of these, you will need to have your own local installation of ABRicate. You then can download/add the custom database to the local installation’s database directory, and supply this directory to the pipeline with the flag --arg_abricate_db, in combination with the name of the new database to --arg_abricate_db_id <db_name>.

For example, if you want to use the bacmet2 database that does not come with the default installation, you could do:

## Create conda environment
conda create -n abricate -c bioconda abricate
conda activate abricate
 
## Download the bacmet2 database
abricate-get_db --db bacmet2 ## the logging will tell you where the database is downloaded to, e.g. /home/<user>/bin/miniconda3/envs/abricate/db/bacmet2/sequences

The resulting directory and database name can be passed to the pipeline as follows

--arg_abricate_db /<path>/<to>/<abricate>/db/ --arg_abricate_db_id bacmet2

The contents of the directory should have a directory named with the database name in the top level (e.g. bacmet2/).

AMRFinderPlus

AMRFinderPlus relies on NCBI’s curated Reference Gene Database and curated collection of Hidden Markov Models.

nf-core/funcscan will download this database for you, unless the path to a local version is given with:

--arg_amrfinderplus_db '/<path>/<to>/<amrfinderplus_db>/latest'

You must give the latest directory to the pipeline, and the contents of the directory should include files such as *.nbd, *.nhr, versions.txt etc. in the top level.

To obtain a local version of the database:

1. Install AMRFinderPlus from bioconda. To ensure database compatibility, please use the same version as is used in your nf-core/funcscan release (check version in file <installation>/<path>/funcscan/modules/nf-core/amrfinderplus/run/environment.yml).

conda create -n amrfinderplus -c bioconda ncbi-amrfinderplus=3.12.8
conda activate amrfinderplus

2. Run amrfinder --update, which will download the latest version of the AMRFinderPlus database to the default location (location of the AMRFinderPlus binaries/data). It creates a directory in the format YYYY-MM-DD.version (e.g., <installation>/<path>/data/2024-01-31.1/).

<YYYY-MM-DD.v>/
├── AMR_CDS.*
├── AMR_DNA-Campylobacter.*
├── AMR_DNA-Clostridioides_difficile.*
├── AMR_DNA-Enterococcus_faecalis.*
├── AMR_DNA-Enterococcus_faecium.*
├── AMR_DNA-Escherichia.*
├── AMR_DNA-Neisseria.*
├── AMR_DNA-Salmonella.*
├── AMR_DNA-Staphylococcus_aureus.*
├── AMR_DNA-Streptococcus_pneumoniae.*
├── AMR.LIB.*
├── AMRProt.*
├── changes.txt
├── database_format_version.txt
├── fam.tab
├── taxgroup.tab
└── version.txt

DeepARG

DeepARG requires a database of potential antimicrobial resistance gene sequences based on a consensus from UNIPROT, CARD, and ARDB.

nf-core/funcscan can download this database for you, however it is very slow and pipeline runtime will be improved if you download this separately and supply it to the pipeline.

You can either:

1. Install DeepARG from bioconda

conda create -n deeparg -c bioconda deeparg
conda activate deeparg

2. Run deeparg download_data -o /<path>/<to>/<database_location>/

Or download the files directly from

1. the DeepARG FTP site
2. the DeepARG database Zenodo archive

Note that more recent database versions maybe available from the ARGMiner service.

You can then supply the path to resulting database directory with:

--arg_deeparg_db '/<path>/<to>/<deeparg>/<db>/'

The contents of the directory should include directories such as database, model, and files such as deeparg.gz etc. in the top level.

Note that if you supply your own database that is not downloaded by the pipeline, make sure to also supply --arg_deeparg_db_version along with the version number so hAMRonization will correctly display the database version in the summary report.

MMSeqs2

To download MMSeqs2 databases for taxonomic classification, you can install mmseqs via conda:

conda create -n mmseqs2 -c bioconda mmseqs2
conda activate mmseqs2

Then to download the database of your choice

mmseqs databases <DATABASE_NAME> <LOCATION_TO_STORE> tmp/

RGI

RGI requires the database CARD which can be downloaded by nf-core/funcscan or supplied by the user manually. To download and supply the database yourself, do:

1. Download CARD

wget https://card.mcmaster.ca/latest/data

2. Extract the (.tar.bz2) archive.

tar -xjvf data

You can then supply the path to resulting database directory with:

--arg_rgi_db '/<path>/<to>/<card>/'

The contents of the directory should include files such as card.json, aro_index.tsv, snps.txt etc. in the top level.

antiSMASH

antiSMASH requires several databases for the detection of potential biosynthetic gene cluster (BGC) sequences (ClusterBlast, MIBiG, Pfam, Resfams, TIGRFAMs).

nf-core/funcscan can download these databases for you, however this is very slow and pipeline runtime will be improved if you download them separately and supply them to the pipeline.

The same applies for the antiSMASH installation directory, which is also a required parameter for the pipeline when using containers, due to some slight incompatibility when using such engines.

To supply the database directories to the pipeline:

1. Install antiSMASH from bioconda. To ensure database compatibility, please use the same version as is used in your nf-core/funcscan release (check version in file <pipeline_installation>/<path>/funcscan/modules/nf-core/antismash/antismashlite/environment.yml).

conda create -n antismash-lite -c bioconda antismash-lite
conda activate antismash-lite

2. Run the command download-antismash-databases. Use --database-dir to specify a new location.
3. You can then supply the paths to the resulting databases and the whole installation directory with:

--bgc_antismash_db '/<path>/<to>/<antismash>/<db>/'
--bgc_antismash_installdir '/<path>/<to>/<antismash>/<dir>/antismash'

Note that the names of the supplied folders must differ from each other (e.g. antismash_db and antismash_dir). The contents of the database directory should include directories such as as-js/, clusterblast/, clustercompare/ etc. in the top level. The contents of the installation directory should include directories such as common/ config/ and files such as custom_typing.py custom_typing.pyi etc. in the top level.

Note that the names of the two required folders must differ from each other (i.e., the --bgc_antismash_db directory must not be called antismash). If they are not provided, the databases will be auto-downloaded upon each BGC screening run of the pipeline.

DeepBGC

DeepBGC relies on trained models and Pfams to run its analysis. nf-core/funcscan will download these databases for you. If the flag --save_db is set, the downloaded files will be stored in the output directory under databases/deepbgc/.

Alternatively, you can download the database locally with:

conda create -n deepbgc -c bioconda deepbgc
conda activate deepbgc
export DEEPBGC_DOWNLOADS_DIR=<PREFERRED_CACHE_DIRECTORY>
deepbgc download

You can then indicate the path to the database folder in the pipeline with --bgc_deepbgc_db <path>/<to>/<deepbgc_db>/. The contents of the database directory should include directories such as common, 0.1.0 in the top level:

deepbgc_db/
├── common
  └── Pfam-hmm-models*.hmm.*
└── <version-num>[0.1.0]
  ├── classifier
  | └── myClassifiers*.pkl
  └── detector
    └── myDetectors*.pkl

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/funcscan

Reproducibility

It is a good idea to specify a 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/funcscan 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. For example, at the bottom of the MultiQC reports.

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

Core Nextflow arguments

-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.

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 see if your system is available in these configs 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 steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (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 which container or conda environment a step of the pipeline uses 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.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

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'