Nextflow configuration

Overview

Questions

• What is the difference between the workflow implementation and the workflow configuration?
• How do I configure a Nextflow workflow?
• How do I assign different resources to different processes?
• How do I separate and provide configuration for different computational systems?
• How do I change configuration settings from the default settings provided by the workflow?

Objectives

• Understand the difference between workflow implementation and configuration.
• Understand the difference between configuring Nextflow and a Nextflow script.
• Create a Nextflow configuration file.
• Understand what a configuration scope is.
• Be able to assign resources to a process.
• Be able to refine configuration settings using process selectors.
• Be able to group configurations into profiles for use with different computer infrastructures.
• Be able to override existing settings.
• Be able to inspect configuration settings before running a workflow.

Nextflow configuration

---

A key Nextflow feature is the ability to decouple the workflow implementation, which describes the flow of data and operations to perform on that data, from the configuration settings required by the underlying execution platform. This enables the workflow to be portable, allowing it to run on different computational platforms such as an institutional HPC or cloud infrastructure, without needing to modify the workflow implementation.

We have seen earlier that it is possible to provide a process with directives. These directives are process specific configuration settings. Similarly, we have also provided parameters to our workflow which are parameter configuration settings. These configuration settings can be separated from the workflow implementation, into a configuration file.

Configuration files

---

Settings in a configuration file are sets of name-value pairs (name = value). The name is a specific property to set, while the value can be anything you can assign to a variable (see nextflow scripting), for example, strings, booleans, or other variables. It is also possible to access any variable defined in the host environment such as $PATH, $HOME, $PWD, etc.

GROOVY

// nextflow.config
my_home_dir = "$HOME"

Accessing variables in your configuration file

Generally, variables and functions defined in a configuration file are not accessible from the workflow script. Only variables defined using the params scope and the env scope (without env prefix) can be accessed from the workflow script.

GROOVY

workflow {
    MY_PROCESS( params.input )
}

Settings are also partitioned into scopes, which govern the behaviour of different elements of the workflow. For example, workflow parameters are governed from the params scope, while process directives are governed from the process scope. A full list of the available scopes can be found in the documentation. It is also possible to define your own scope.

Configuration settings for a workflow are often stored in the file nextflow.config which is in the same directory as the workflow script. Configuration can be written in either of two ways. The first is using dot notation, and the second is using brace notation. Both forms of notation can be used in the same configuration file.

An example of dot notation:

GROOVY

params.input = ''             // The workflow parameter "input" is assigned an empty string to use as a default value
params.outdir = './results'   // The workflow parameter "outdir" is assigned the value './results' to use by default.

An example of brace notation:

GROOVY

params {
    input  = ''
    outdir = './results'
}

Configuration files can also be separated into multiple files and included into another using the includeConfig statement.

GROOVY

// nextflow.config
params {
    input  = ''
    outdir = './results'
}

includeConfig 'system_resources.config'

GROOVY

// system_resources.config
process {
    cpus = 1    // default cpu usage
    time = '1h' // default time limit
}

How configuration files are combined

---

Configuration settings can be spread across several files. This also allows settings to be overridden by other configuration files. The priority of a setting is determined by the following order, ranked from highest to lowest.

1. Parameters specified on the command line (--param_name value).
2. Parameters provided using the -params-file option.
3. Config file specified using the -c my_config option.
4. The config file named nextflow.config in the current directory.
5. The config file named nextflow.config in the workflow project directory ($projectDir: the directory where the script to be run is located).
6. The config file $HOME/.nextflow/config.
7. Values defined within the workflow script itself (e.g., main.nf).

If configuration is provided by more than one of these methods, configuration is merged giving higher priority to configuration provided higher in the list.

Existing configuration can be completely ignored by using -C <custom.config> to use only configuration provided in the custom.config file.

Configuring Nextflow vs Configuring a Nextflow workflow

Parameters starting with a single dash - (e.g., -c my_config.config) are configuration options for nextflow, while parameters starting with a double dash -- (e.g., --outdir) are workflow parameters defined in the params scope.

The majority of Nextflow configuration settings must be provided on the command-line, however a handful of settings can also be provided within a configuration file, such as workdir = '/path/to/work/dir' (-w /path/to/work/dir) or resume = true (-resume), and do not belong to a configuration scope.

Determine script output

Determine the outcome of the following script executions. Given the script print_message.nf:

GROOVY

nextflow.enable.dsl = 2

params.message = 'hello'

workflow {
    PRINT_MESSAGE(params.message)
}

process PRINT_MESSAGE {
    echo true

    input:
    val my_message

    script:
    """
    echo $my_message
    """
}

and configuration (print_message.config):

params.message = 'Are you tired?'

What is the outcome of the following commands?

1. nextflow run print_message.nf
2. nextflow run print_message.nf --message '¿Que tal?'
3. nextflow run print_message.nf -c print_message.config
4. nextflow run print_message.nf -c print_message.config --message '¿Que tal?'

Show me the solution

1. ‘hello’ - Workflow script uses the value in print_message.nf
2. ‘¿Que tal?’ - The command-line parameter overrides the script setting.
3. ‘Are you tired?’ - The configuration overrides the script setting
4. ‘¿Que tal?’ - The command-line parameter overrides both the script and configuration settings.

Configuring process behaviour

---

Earlier we saw that process directives allow the specification of settings for the task execution such as cpus, memory, conda and other resources in the pipeline script. This is useful when prototyping a small workflow script, however this ties the configuration to the workflow, making it less portable. A good practice is to separate the process configuration settings into another file.

The process configuration scope allows the setting of any process directives in the Nextflow configuration file.

For example:

GROOVY

// nextflow.config
process {
    cpus = 2
    memory = 8.GB
    time = '1 hour'
    publishDir = [ path: params.outdir, mode: 'copy' ]
}

Unit values

Memory and time duration units can be specified either using a string based notation in which the digit(s) and the unit can be separated by a space character, or by using the numeric notation in which the digit(s) and the unit are separated by a dot character and not enclosed by quote characters.

String syntax	Numeric syntax	Value
‘10 KB’	10.KB	10240 bytes
‘500 MB’	500.MB	524288000 bytes
‘1 min’	1.min	60 seconds
‘1 hour 25 sec’	-	1 hour and 25 seconds

These settings are applied to all processes in the workflow. A process selector can be used to apply the configuration to a specific process or group of processes.

Process selectors

The resources for a specific process can be defined using withName: followed by the process name e.g., 'FASTQC', and the directives within curly braces. For example, we can specify different cpus and memory resources for the processes INDEX and FASTQC as follows:

GROOVY

// process_resources.config
process {
    withName: INDEX {
        cpus = 4
        memory = 8.GB
    }
    withName: FASTQC {
        cpus = 2
        memory = 4.GB
    }
}

When a workflow has many processes, it is inconvenient to specify directives for all processes individually, especially if directives are repeated for groups of processes. A helpful strategy is to annotate the processes using the label directive (processes can have multiple labels). The withLabel selector then allows the configuration of all processes annotated with a specific label, as shown below:

GROOVY

// configuration_process_labels.nf
nextflow.enable.dsl=2

process P1 {

    label "big_mem"

    script:
    """
    echo P1: Using $task.cpus cpus and $task.memory memory.
    """
}

process P2 {

    label "big_mem"

    script:
    """
    echo P2: Using $task.cpus cpus and $task.memory memory.
    """
}

workflow {

    P1()
    P2()

}

GROOVY

// configuration_process-labels.config
process {
    withLabel: big_mem {
        cpus = 16
        memory = 64.GB
    }
}

Another strategy is to use process selector expressions. Both withName: and withLabel: allow the use of regular expressions to apply the same configuration to all processes matching a pattern. Regular expressions must be quoted, unlike simple process names or labels.

• The | matches either-or, e.g., withName: 'INDEX|FASTQC' applies the configuration to any process matching the name INDEX or FASTQC.
• The ! inverts a selector, e.g., withLabel: '!small_mem' applies the configuration to any process without the small_mem label.
• The .* matches any number of characters, e.g., withName: 'NFCORE_RNASEQ:RNA_SEQ:BAM_SORT:.*' matches all processes of the workflow NFCORE_RNASEQ:RNA_SEQ:BAM_SORT.

A regular expression cheat-sheet can be found here if you would like to write more expressive expressions.

Selector priority

When mixing generic process configuration and selectors, the following priority rules are applied (from highest to lowest):

1. withName selector definition.
2. withLabel selector definition.
3. Process specific directive defined in the workflow script.
4. Process generic process configuration.

Process selectors

Create a Nextflow config, process-selector.config, specifying different cpus and memory resources for the two processes P1 (cpus 1 and memory 2.GB) and P2 (cpus 2 and memory 1.GB), where P1 and P2 are defined as follows:

GROOVY

// process-selector.nf
nextflow.enable.dsl=2

process P1 {
    echo true

    script:
    """
    echo P1: Using $task.cpus cpus and $task.memory memory.
    """
}

process P2 {
    echo true

    script:
    """
    echo P2: Using $task.cpus cpus and $task.memory memory.
    """
}

workflow {
   P1()
   P2()
}

Show me the solution

GROOVY

// process-selector.config
process {
    withName: P1 {
        cpus = 1
        memory = 2.GB
    }
    withName: P2 {
        cpus = 2
        memory = 1.GB
    }
}

BASH

$ nextflow run process-selector.nf -c process-selector.config -process.debug

OUTPUT

N E X T F L O W  ~  version 21.04.0

Launching `process-selector.nf` [clever_borg] -
revision: e765b9e62d
executor >  local (2)
[de/86cef0] process > P1 [100%] 1 of 1 ✔
[bf/8b332e] process > P2 [100%] 1 of 1 ✔
P2: Using 2 cpus and 1 GB memory.

P1: Using 1 cpus and 2 GB memory.

Dynamic expressions

A common scenario is that configuration settings may depend on the data being processed. Such settings can be dynamically expressed using a closure. For example, we can specify the memory required as a multiple of the number of cpus. Similarly, we can publish results to a subfolder based on the sample name.

GROOVY

process FASTQC {

    input:
    tuple val(sample), path(reads)

    script:
    """
    fastqc -t $task.cpus $reads
    """
}

GROOVY

// nextflow.config
process {
    withName: FASTQC {
        cpus = 2
        memory = { 2.GB * task.cpus }
        publishDir = { "fastqc/$sample" }
    }
}

Configuring execution platforms

---

Nextflow supports a wide range of execution platforms, from running locally, to running on HPC clusters or cloud infrastructures. See https://www.nextflow.io/docs/latest/executor.html for the full list of supported executors.

The default executor configuration is defined within the executor scope (https://www.nextflow.io/docs/latest/config.html#scope-executor). For example, in the config below we specify the executor as Sun Grid Engine, sge and the number of tasks the executor will handle in a parallel manner (queueSize) to 10.

GROOVY

// nextflow.config
executor {
    name = 'sge'
    queueSize = 10
}

The process.executor directive allows you to override the executor to be used by a specific process. This can be useful, for example, when there are short running tasks that can be run locally, and are unsuitable for submission to HPC executors (check for guidelines on best practice use of your execution system). Other process directives such as process.clusterOptions, process.queue, and process.machineType can be also be used to further configure processes depending on the executor used.

GROOVY

//nextflow.config
executor {
    name = 'sge'
    queueSize = 10
}
process {
    withLabel: 'short' {
        executor = 'local'
    }
}

Configuring software requirements

---

An important feature of Nextflow is the ability to manage software using different technologies. It supports the Conda package management system, and container engines such as Docker, Singularity, Podman, Charliecloud, and Shifter. These technologies allow one to package tools and their dependencies into a software environment such that the tools will always work as long as the environment can be loaded. This facilitates portable and reproducible workflows. Software environment specification is managed from the process scope, allowing the use of process selectors to manage which processes load which software environment. Each technology also has its own scope to provide further technology specific configuration settings.

Software configuration using Conda

Conda is a software package and environment management system that runs on Linux, Windows, and Mac OS. Software packages are bundled into Conda environments along with their dependencies for a particular operating system (Not all software is supported on all operating systems). Software packages are tied to conda channels, for example, bioinformatic software packages are found and installed from the BioConda channel.

A Conda environment can be configured in several ways:

• Provide a path to an existing Conda environment.
• Provide a path to a Conda environment specification file (written in YAML).
• Specify the software package(s) using the <channel>::<package_name>=<version> syntax (separated by spaces), which then builds the Conda environment when the process is run.

GROOVY

process {
    conda = "/home/user/miniconda3/envs/my_conda_env"
    withName: FASTQC {
        conda = "environment.yml"
    }
    withName: SALMON {
        conda = "bioconda::salmon=1.5.2"
    }
}

There is an optional conda scope which allows you to control the creation of a Conda environment by the Conda package manager. For example, conda.cacheDir specifies the path where the Conda environments are stored. By default this is in conda folder of the work directory.

Define a software requirement in the configuration file using conda

We are going to define a software requirement using the “conda” directive.

The software we are going to uses is fastp, a tool used for fast processing of next-generation sequencing data (like RNA or DNA sequences).

Each time the process is called we are going to run fastp -A -i ${read} -o out.fq 2>&1

• fastp is the main command that invokes the fastp program.
• -A: option tells fastp to automatically detect and trim adapters. Adapters are short, artificially added sequences used in sequencing that are not part of the target DNA or RNA. -i ${read}:
• The -i flag specifies the input file. ${read} represents a nextflow variable that contains the path to the sequencing reads.
• -o: The -o flag specifies the output file. In this case, the processed sequencing data will be written to a file named out.fq.
• 2>&1: This is a shell redirection command, it means that both the regular output and error messages will be sent to the console.

1. Create a config file for the Nextflow script configuration_fastp.nf.

2. Add a conda directive for the process name FASTP that includes the bioconda package fastp, version 0.12.4-0.

Hint You can specify the conda packages using the syntax <channel>::<package_name>=<version> e.g. bioconda::salmon=1.5.2

3. Run the Nextflow script configure_fastp.nf with the configuration file using the -c option.

GROOVY

// configuration_fastp.nf
nextflow.enable.dsl = 2

params.input = "data/yeast/reads/ref1_1.fq.gz"

workflow {
    FASTP( Channel.fromPath( params.input ) ).view()
}

process FASTP {

   input:
   path read

   output:
   stdout

   script:
   """
   fastp -A -i ${read} -o out.fq 2>&1
   """
}

Show me the solution

GROOVY

// fastp.config
process {
    withName: 'FASTP' {
        conda = "bioconda::fastp=0.12.4-0"
    }
}

BASH

nextflow run configure_fastp.nf -c fastp.config

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `configuration_fastp.nf` [berserk_jepsen] - revision: 28fadd2486
executor >  local (1)
[c1/c207d5] process > FASTP (1) [100%] 1 of 1 ✔
Creating Conda env: bioconda::fastp=0.12.4-0 [cache /home/training/work/conda/env-a7a3a0d820eb46bc41ebf4f72d955e5f]
ref1_1.fq.gz 58708
Read1 before filtering:
total reads: 14677
total bases: 1482377

Q20 bases: 1466210(98.9094%)
Q30 bases: 1415997(95.5221%)

Read1 after filtering:
total reads: 14671
total bases: 1481771
Q20 bases: 1465900(98.9289%)
Q30 bases: 1415769(95.5457%)

Filtering result:
reads passed filter: 14671
reads failed due to low quality: 6
reads failed due to too many N: 0
reads failed due to too short: 0

JSON report: fastp.json
HTML report: fastp.html

Software configuration using Docker

Docker is a container technology. Container images are lightweight, standalone, executable package of software that includes everything needed to run an application: code, runtime, system tools, system libraries and settings. Containerized software is intended to run the same regardless of the underlying infrastructure, unlike other package management technologies which are operating system dependant (See the published article on Nextflow). For each container image used, Nextflow uses Docker to spawn an independent and isolated container instance for each process task.

To use Docker, we must provide a container image path using the process.container directive, and also enable docker in the docker scope, docker.enabled = true. A container image path takes the form (protocol://)registry/repository/image:version--build. By default, Docker containers run software using a privileged user. This can cause issues, and so it is also a good idea to supply your user and group via the docker.runOptions.

GROOVY

process.container = 'quay.io/biocontainers/salmon:1.5.2--h84f40af_0'
docker.enabled = true
docker.runOptions = '-u $(id -u):$(id -g)'

Software configuration using Singularity

Singularity is another container technology, commonly used on HPC clusters. It is different to Docker in several ways. The primary differences are that processes are run as the user, and certain directories are automatically “mounted” (made available) in the container instance. Singularity also supports building Singularity images from Docker images, allowing Docker image paths to be used as values for process.container.

Singularity is enabled in a similar manner to Docker. A container image path must be provided using process.container and singularity enabled using singularity.enabled = true.

GROOVY

process.container = 'https://depot.galaxyproject.org/singularity/salmon:1.5.2--h84f40af_0'
singularity.enabled = true

Container protocols

The following protocols are supported:

• docker://: download the container image from the Docker Hub and convert it to the Singularity format (default).
• library://: download the container image from the Singularity Library service.
• shub://: download the container image from the Singularity Hub.
• docker-daemon://: pull the container image from a local Docker installation and convert it to a Singularity image file.
• https://: download the singularity image from the given URL.
• file://: use a singularity image on local computer storage.

Configuration profiles

---

One of the most powerful features of Nextflow configuration is to predefine multiple configurations or profiles for different execution platforms. This allows a group of predefined settings to be called with a short invocation, -profile <profile name>.

Configuration profiles are defined in the profiles scope, which group the attributes that belong to the same profile using a common prefix.

GROOVY

//configuration_profiles.config
profiles {

    standard {
        params.genome = '/local/path/ref.fasta'
        process.executor = 'local'
    }

    cluster {
        params.genome = '/data/stared/ref.fasta'
        process.executor = 'sge'
        process.queue = 'long'
        process.memory = '10GB'
        process.conda = '/some/path/env.yml'
    }

    cloud {
        params.genome = '/data/stared/ref.fasta'
        process.executor = 'awsbatch'
        process.container = 'cbcrg/imagex'
        docker.enabled = true
    }

}

This configuration defines three different profiles: standard, cluster and cloud that set different process configuration strategies depending on the target execution platform. By convention the standard profile is implicitly used when no other profile is specified by the user. To enable a specific profile use -profile option followed by the profile name:

BASH

nextflow run <your script> -profile cluster

Configuration order

Settings from profiles will override general settings in the configuration file. However, it is also important to remember that configuration is evaluated in the order it is read in. For example, in the following example, the publishDir directive will always take the value ‘results’ even when the profile hpc is used. This is because the setting is evaluated before Nextflow knows about the hpc profile. If the publishDir directive is moved to after the profiles scope, then publishDir will use the correct value of params.results.

GROOVY

params.results = 'results'
process.publishDir = params.results
profiles {
    hpc {
        params.results = '/long/term/storage/results'
    }
}

Key Points

• Nextflow configuration can be managed using a Nextflow configuration file.
• Nextflow configuration files are plain text files containing a set of properties.
• You can define process specific settings, such as cpus and memory, within the process scope.
• You can assign different resources to different processes using the process selectors withName or withLabel.
• You can define a profile for different configurations using the profiles scope. These profiles can be selected when launching a pipeline execution by using the -profile command-line option
• Nextflow configuration settings are evaluated in the order they are read-in.