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nf-core/funcprofiler: Usage

⚠️ Please read this documentation on the nf-core website: https://nf-co.re/funcprofiler/usage

Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files.

Introduction

nf-core/funcprofiler performs read-based functional profiling of microbiome sequencing data. It requires two input CSV files: a samplesheet describing your samples and a databases sheet describing the profiling databases to use.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

The samplesheet is a comma-separated file with the following columns:

Column Required Description
sample Yes Sample name. Rows with the same sample name (and different run_accession) are merged before profiling.
run_accession Yes Unique run identifier (e.g. RUN1, SRR12345). Used to distinguish multiple sequencing runs of the same sample.
instrument_platform Yes Sequencing platform. Must be one of: ILLUMINA, OXFORD_NANOPORE, PACBIO_SMRT, ION_TORRENT, BGISEQ, DNBSEQ, or LS454.
fastq_1 No* Full path to gzipped FASTQ file for read 1. Must end in .fastq.gz or .fq.gz.
fastq_2 No Full path to gzipped FASTQ file for read 2 (paired-end only). Leave empty for single-end or Nanopore reads.
fasta No* Full path to gzipped FASTA file. Provide instead of FASTQ if your data is already assembled. Must end in .fa.gz, .fna.gz, or .fasta.gz.

*Either fastq_1 or fasta must be provided for each row.

Example samplesheet

sample,run_accession,instrument_platform,fastq_1,fastq_2,fasta
SAMPLE1,RUN1,ILLUMINA,/data/sample1_R1.fastq.gz,/data/sample1_R2.fastq.gz,
SAMPLE1,RUN2,ILLUMINA,/data/sample1_lane2_R1.fastq.gz,/data/sample1_lane2_R2.fastq.gz,
SAMPLE2,RUN1,ILLUMINA,/data/sample2_R1.fastq.gz,,
SAMPLE3,RUN1,OXFORD_NANOPORE,/data/sample3_nanopore.fastq.gz,,

In this example, SAMPLE1 has two runs which will be merged before profiling. SAMPLE2 is single-end short reads. SAMPLE3 is Oxford Nanopore long reads.

Databases input

--databases '[path to databases file]'

The databases sheet is a comma-separated file that specifies which databases to use for each profiler. Only tools enabled via --run_<tool> flags will use the corresponding database entries.

Column Required Description
tool Yes Profiler name. Must be one of: humann_v3, humann_v4, fmhfunprofiler, mifaser, diamond, rgi, eggnogmapper.
db_name Yes Unique identifier for this database set. All HUMANn database components must share the same db_name.
db_entity No For HUMANn: specifies the component (humann_metaphlan, humann_nucleotide, humann_protein, humann_utility). For EggNOG-mapper: eggnogmapper_db or eggnogmapper_data_dir.
db_params No Additional parameters to pass to the profiler (no quotes allowed).
db_type No Read type this database applies to: short, long, or short;long (default). Use to restrict a database to only short-read or long-read samples.
db_path Yes Absolute path to the database file or directory. Gzipped TAR archives (.tar.gz) are automatically decompressed.

HUMANn databases

HUMANn requires four database components per named database, each as a separate row with the same db_name:

tool,db_name,db_entity,db_params,db_type,db_path
humann_v3,uniref90_v3,humann_metaphlan,,,/data/databases/metaphlan_db
humann_v3,uniref90_v3,humann_nucleotide,,,/data/databases/chocophlan
humann_v3,uniref90_v3,humann_protein,,,/data/databases/uniref90_diamond
humann_v3,uniref90_v3,humann_utility,,,/data/databases/utility_mapping

FMH FunProfiler databases

FMH FunProfiler requires a single sketch database:

tool,db_name,db_entity,db_params,db_type,db_path
fmhfunprofiler,kegg_v1,,,short;long,/data/databases/fmhfunprofiler_kegg.sig.zip

EggNOG-mapper databases

EggNOG-mapper requires two database entries per named database: the search database and the EggNOG data directory. The db_params field of the eggnogmapper_db row must specify the search mode (e.g. diamond, mmseqs, hmmer).

tool,db_name,db_entity,db_params,db_type,db_path
eggnogmapper,eggnog_v5,eggnogmapper_db,diamond,,/data/databases/eggnog_mapper/eggnog_proteins.dmnd
eggnogmapper,eggnog_v5,eggnogmapper_data_dir,,,/data/databases/eggnog_mapper/data

The EggNOG data directory can be downloaded with download_eggnog_data.py from the eggnog-mapper package. See the EggNOG-mapper documentation for details.

Full example databases sheet

tool,db_name,db_entity,db_params,db_type,db_path
humann_v3,uniref90_v3,humann_metaphlan,,,/data/databases/metaphlan_db
humann_v3,uniref90_v3,humann_nucleotide,,,/data/databases/chocophlan
humann_v3,uniref90_v3,humann_protein,,,/data/databases/uniref90_diamond
humann_v3,uniref90_v3,humann_utility,,,/data/databases/utility_mapping
humann_v4,uniref90_v4,humann_metaphlan,,,/data/databases/metaphlan4_db
humann_v4,uniref90_v4,humann_nucleotide,,,/data/databases/chocophlan_v4
humann_v4,uniref90_v4,humann_protein,,,/data/databases/uniref90_v4_diamond
humann_v4,uniref90_v4,humann_utility,,,/data/databases/utility_mapping_v4
fmhfunprofiler,kegg_v1,,,short;long,/data/databases/fmhfunprofiler_kegg.sig.zip

RGI BWT

RGI (Resistance Gene Identifier) uses the Comprehensive Antibiotic Resistance Database (CARD) to identify AMR genes. The bwt subcommand aligns reads directly to CARD using Bowtie2/BWA. Enable with --run_rgi.

Database preparation

Download the CARD database and extract it to a directory:

wget https://card.mcmaster.ca/latest/data
tar -xvf data ./card.json
rgi load --card_json card.json --local

The db_path in the databases CSV must point to the directory containing card.json and the pre-built CARD annotation files (card_database_v*.fasta).

tool,db_name,db_entity,db_params,db_type,db_path
rgi,card_v3,,,,/data/databases/card

Note

Wildcard variant databases are not currently supported by the pipeline. Only the core CARD database is used.

DIAMOND blastx

DIAMOND is a high-throughput sequence aligner for translated (nucleotide-vs-protein) alignment. Enable it with --run_diamond.

Database preparation

The database supplied in the --databases CSV must already be in DIAMOND binary format (.dmnd). Build it from a protein FASTA using diamond makedb:

diamond makedb --in proteins.faa --db proteins
# produces proteins.dmnd

See the DIAMOND makedb documentation for all available options (e.g. adding taxonomy, setting block size).


> [!IMPORTANT]
> The path should point to the **directory** containing the `.dmnd` file, not the file itself. The pipeline will automatically locate the `.dmnd` file within that directory.


## Running the pipeline

The typical command for running the pipeline is as follows:

```bash
nextflow run nf-core/funcprofiler \
   --input samplesheet.csv        \
   --databases databases.csv      \
   --outdir results               \
   --run_humann_v3                \
   --run_fmhfunprofiler           \
   -profile docker

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

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.

Enabling profilers

At least one profiler must be enabled via command-line flags. The pipeline will only run the profilers you explicitly turn on:

Flag Profiler Status
--run_humann_v3 HUMANn v3 Available
--run_humann_v4 HUMANn v4 Available
--run_fmhfunprofiler FMH FunProfiler Available
--run_mifaser mifaser Available
--run_diamond diamond Available
--run_eggnogmapper EggNOG-mapper Available
--run_rgi RGI BWT Available

Parameters

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/funcprofiler -profile docker -params-file params.yaml

with:

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

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/funcprofiler

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/funcprofiler 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 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.

Important

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'