Running Nextflow Pipelines

This tutorial demonstrates how to use Nextflow pipelines on DNAnexus by importing a Nextflow pipeline from a remote repository or building from local disk space.

A license is required to create a DNAnexus app or applet from the Nextflow script folder. Contact DNAnexus Sales for more information.

This documentation assumes you already have a basic understanding of how to develop and run a Nextflow pipeline. To learn more about Nextflow, consult the official Nextflow Documentation.

To run a Nextflow pipeline on the DNAnexus Platform:

1. Import the pipeline script from a remote repository or local disk

2. Convert the script to an app or applet

3. Run the app or applet

You can do this via either the user interface (UI) or the command-line interface (CLI), using the dx command-line client.

Use the latest version of dx-toolkit to take advantage of recent improvements and bug fixes.

All versions beginning with v0.338.0 support converting Nextflow pipelines to apps or applets.

This documentation covers features available in dx-toolkit versions beginning with v0.370.0.

Quickstart

Pipeline Script Folder Structure

A Nextflow pipeline script is structured as a folder with Nextflow scripts with optional configuration files and subfolders. Below are the basic elements of the folder structure when building a Nextflow executable:

• (Required) A main Nextflow file with the extension .nf containing the pipeline. The default filename is main.nf. A different filename can be specified in the nextflow.config file.

• (Optional) A nextflow.config file.

• (Optional, recommended) A nextflow_schema.json file. If this file is present at the root folder of the Nextflow script when importing or building the executable, the input parameters described in the file will be exposed as the built Nextflow pipeline applet's input parameters. See this section for more information on how the exposed parameters are used at run time.

• (Optional) Subfolders and other configuration files. Subfolders and other configuration files can be referenced by the main Nextflow file or nextflow.config via theinclude or includeConfig keyword. Ensure that all referenced subfolders and files exist under the pipeline script folder at the time of building or importing the pipeline.

An nf-core flavored folder structure is encouraged but not required.

Importing a Nextflow Pipeline

Import via UI

To import a Nextflow pipeline via the UI, click on the Add button on the top-right corner of the project’s Manage tab, then expand the dropdown menu. Select the Import Pipeline/Workflow option.

Once the Import Pipeline/Workflow modal appears, enter the repository URL where the Nextflow pipeline source code resides, for example, "https://github.com/nextflow-io/hello". Then choose the desired project import location. If the repository is private, provide the credentials necessary for accessing it.

An example of the Import Pipeline/Workflow modal:

Note that the “Estimated Price” value shown here is only an example. The actual price depends on the pricing model and runtime of the import job.

Once you’ve provided the necessary information, click the Start Import button and the import process will start as a pipeline import job, in the project specified in the Import To field (default is the current project).

After you've launched the import job, you'll see a status message "External workflow import job started" appear.

You can access information about the pipeline import job in the project’s Monitor tab:

Once the import is complete, you can find the imported pipeline executable as an applet. This is the output of the pipeline import job you previously ran:

You can find the newly created Nextflow pipeline applet - e.g. hello - in the project:

Import via CLI from a Remote Repository

To import a Nextflow pipeline from a remote repository via the CLI, run the following command to specify the repository’s URL. Note that you can also provide optional information, such as a repository tag and an import destination:

$ dx build --nextflow \
  --repository https://github.com/nextflow-io/hello \
  --destination project-xxxx:/applets/hello

Started builder job job-aaaa
Created Nextflow pipeline applet-zzzz

Use the latest version of dx-toolkit to take advantage of recent improvements and bug fixes.

All versions beginning with v0.338.0 support converting Nextflow pipelines to apps or applets.

This documentation covers features available in dx-toolkit versions beginning with v0.370.0.

Your destination project’s billTo feature needs to be enabled for Nextflow pipeline applet building. Contact DNAnexus Sales for more information.

If the Nextflow pipeline is in a private repository, use the option --git-credentials to provide the DNAnexus qualified ID or path of the credential files on the Platform. Read more about this here.

Once the pipeline import job has finished, it will generate a new Nextflow pipeline applet with an applet ID in the form applet-zzzz.

Use dx run -h to get more information about running the applet:

$ dx run project-xxxx:/applets/hello -h
usage: dx run project-xxxx:/applets/hello [-iINPUT_NAME=VALUE ...]

Applet: hello

hello

Inputs:
 Nextflow options
  Nextflow Run Options: [-inextflow_run_opts=(string)]
        Additional run arguments for Nextflow (e.g. -profile docker).

  Nextflow Top-level Options: [-inextflow_top_level_opts=(string)]
        Additional top-level options for Nextflow (e.g. -quiet).

  Soft Configuration File: [-inextflow_soft_confs=(file) [-inextflow_soft_confs=... [...]]]
        (Optional) One or more nextflow configuration files to be appended to the Nextflow pipeline
        configuration set

  Script Parameters File: [-inextflow_params_file=(file)]
        (Optional) A file, in YAML or JSON format, for specifying input parameter values

 Advanced Executable Development Options
  Debug Mode: [-idebug=(boolean, default=false)]
        Shows additional information in the job log. If true, the execution log messages from
        Nextflow will also be included.

  Resume: [-iresume=(string)]
        Unique ID of the previous session to be resumed. If 'true' or 'last' is provided instead of
        the sessionID, will resume the latest resumable session run by an applet with the same name
        in the current project in the last 6 months.

  Preserve Cache: [-ipreserve_cache=(boolean, default=false)]
        Enable storing pipeline cache and local working files to the current project. If true, local
        working files and cache files will be uploaded to the platform, so the current session could
        be resumed in the future

Outputs:
  Published files of Nextflow pipeline: [published_files (array:file)]
        Output files published by current Nextflow pipeline and uploaded to the job output
        destination.

Building from a Local Disk

Through the CLI you can also build a Nextflow pipeline applet from a pipeline script folder stored on a local disk. For example, you may have a copy of the nextflow-io/hello pipeline from the Nextflow Github on your local laptop, stored in a directory named hello, which contains the following files:

$ pwd
/path/to/hello
$ ls
LICENSE         README.md       main.nf         nextflow.config

Ensure that the folder structure is in the required format, as described here.

To build a Nextflow pipeline applet using a locally stored pipeline script, run the following command and specify the path to the folder containing the Nextflow pipeline scripts. You can also provide optional information, such as an import destination:

$ dx build --nextflow /path/to/hello \
  --destination project-xxxx:/applets2/hello
{"id": "applet-yyyy"}

Your destination project’s billTo feature needs to be enabled for Nextflow pipeline applet building. Contact Sales for more information.

This command will package the Nextflow pipeline script folder as an applet named hello with ID applet-yyyy, and store the applet in the destination project and path project-xxxx:/applets2/hello. If an import destination is not provided, the current working directory will be used.

The dx run -h command can be run to see information about this applet, similar to the above example.

A Nextflow pipeline applet will have a type “nextflow” under its metadata . This applet acts like a regular DNAnexus applet object, and can be shared with other DNAnexus users who have access to the project containing the applet.

For advanced information regarding the parameters of dx build --nextflow, run dx build --help in the CLI and find the Nextflow section for all arguments that are supported for building an Nextflow pipeline applet.

Building a Nextflow Pipeline App from a Nextflow Pipeline Applet

You can also build a Nextflow pipeline app from a Nextflow pipeline applet by running the command: dx build --app --from applet-xxxx.

Running a Nextflow Pipeline Executable (App or Applet)

Running a Nextflow Pipeline Executable via UI

You can access a Nextflow pipeline applet from the Manage tab in your project, while the Nextflow pipeline app that you built can be accessed by clicking on the Tools Library option from the Tools tab. Once you click on the applet or app, the Run Analysis tab will be displayed. Fill out the required inputs/outputs and click the Start Analysis button to launch the job.

Running a Nextflow Pipeline Applet via CLI

To run the Nextflow pipeline applet, use dx run applet-xxxx or dx run app-xxxx commands in the CLI and specify your inputs:

$ dx run project-yyyy:applet-xxxx \
  -i debug=false \
  --destination project-xxxx:/path/to/destination/ \
  --brief -y

job-bbbb

You can list and see the progress of the Nextflow pipeline job tree, which is structured as a head job with many subjobs, using the following command:

# See subjobs in progress
$ dx find jobs --origin job-bbbb
* hello (done) job-bbbb
│ amy 2023-09-20 14:57:58 (runtime 0:02:03)
├── sayHello (3) (hello:nf_task_entry) (done) job-1111
│   amy 2023-09-20 14:58:57 (runtime 0:00:45)
├── sayHello (1) (hello:nf_task_entry) (done) job-2222
│   amy 2023-09-20 14:58:52 (runtime 0:00:52)
├── sayHello (2) (hello:nf_task_entry) (done) job-3333
│   amy 2023-09-20 14:58:48 (runtime 0:00:53)
└── sayHello (4) (hello:nf_task_entry) (done) job-4444
    amy 2023-09-20 14:58:43 (runtime 0:00:50)

Monitoring Jobs

Each Nextflow pipeline executable run is represented as a job tree with one head job and many subjobs. The head job launches and supervises the entire pipeline execution. Each subjob is responsible for a process in the Nextflow pipeline. You can monitor the progress of the entire pipeline job tree by viewing the status of the subjobs (see example above).

To monitor the detail log of the head job and the subjobs, you can monitor each job’s DNAnexus log via the UI or the CLI.

On the DNAnexus Platform, jobs are limited to a runtime of 30 days. Jobs running longer than 30 days will be automatically terminated.

Monitoring in the UI

Once your job tree is running, you can go to the Monitor tab to view the status of your job tree. From the Monitor tab, you can view the job log of the head job as well as the subjobs by clicking on the Log link in the row of the desired job. You can also view the costs (when your account has permission) and resource usage of a job.

An example of the log of a head job:

An example of the log of a subjob:

Monitoring in the CLI

From the CLI, you can use the dx watch command to check the status and view the log of the head job or each subjob.

Monitoring the head job:

# Monitor job in progress
$ dx watch job-bbbb
Watching job job-bbbb. Press Ctrl+C to stop watching.
* hello (done) job-bbbb
  amy 2023-09-20 14:57:58 (runtime 0:02:03)
... [deleted]
2023-09-20 14:58:29 hello STDOUT dxpy/0.358.0 (Linux-5.15.0-1045-aws-x86_64-with-glibc2.29) Python/3.8.10
2023-09-20 14:58:30 hello STDOUT bash running (job ID job-bbbb)
2023-09-20 14:58:31 hello STDOUT =============================================================
2023-09-20 14:58:31 hello STDOUT === NF projectDir   : /home/dnanexus/hello
2023-09-20 14:58:31 hello STDOUT === NF session ID   : 0eac8f92-1216-4fce-99cf-dee6e6b04bc2
2023-09-20 14:58:31 hello STDOUT === NF log file     : dx://project-xxxx:/applets/nextflow-job-bbbb.log
2023-09-20 14:58:31 hello STDOUT === NF command      : nextflow -log nextflow-job-bbbb.log run /home/dnanexus/hello -name job-bbbb
2023-09-20 14:58:31 hello STDOUT === Built with dxpy : 0.358.0
2023-09-20 14:58:31 hello STDOUT =============================================================
2023-09-20 14:58:34 hello STDOUT N E X T F L O W  ~  version 22.10.7
2023-09-20 14:58:35 hello STDOUT Launching `/home/dnanexus/hello/main.nf` [job-bbbb] DSL2 - revision: 1647aefcc7
2023-09-20 14:58:43 hello STDOUT [0a/6a81ca] Submitted process > sayHello (4)
2023-09-20 14:58:48 hello STDOUT [f5/87df8b] Submitted process > sayHello (2)
2023-09-20 14:58:53 hello STDOUT [4b/21374a] Submitted process > sayHello (1)
2023-09-20 14:58:57 hello STDOUT [f6/8c44f5] Submitted process > sayHello (3)
2023-09-20 14:59:51 hello STDOUT Hola world!
2023-09-20 14:59:51 hello STDOUT 
2023-09-20 14:59:51 hello STDOUT Ciao world!
2023-09-20 14:59:51 hello STDOUT 
2023-09-20 15:00:06 hello STDOUT Bonjour world!
2023-09-20 15:00:06 hello STDOUT 
2023-09-20 15:00:06 hello STDOUT Hello world!
2023-09-20 15:00:06 hello STDOUT 
2023-09-20 15:00:07 hello STDOUT === Execution completed — cache and working files will not be resumable
2023-09-20 15:00:07 hello STDOUT === Execution completed — upload nextflow log to job output destination project-xxxx:/applets/
2023-09-20 15:00:09 hello STDOUT Upload nextflow log as file: file-GZ5ffkj071zqZ9Qj22qv097J
2023-09-20 15:00:09 hello STDOUT === Execution succeeded — upload published files to job output destination project-xxxx:/applets/
* hello (done) job-bbbb
  amy 2023-09-20 14:57:58 (runtime 0:02:03)
  Output: -

Monitoring a subjob:

# Monitor job in progress
$ dx watch job-cccc
Watching job job-cccc. Press Ctrl+C to stop watching.
sayHello (1) (hello:nf_task_entry) (done) job-cccc
amy 2023-09-20 14:58:52 (runtime 0:00:52)
... [deleted]
2023-09-20 14:59:28 sayHello (1) STDOUT dxpy/0.358.0 (Linux-5.15.0-1045-aws-x86_64-with-glibc2.29) Python/3.8.10
2023-09-20 14:59:30 sayHello (1) STDOUT bash running (job ID job-cccc)
2023-09-20 14:59:33 sayHello (1) STDOUT file-GZ5ffQj047j3Vq7QX220Q5vQ
2023-09-20 14:59:34 sayHello (1) STDOUT Bonjour world!
2023-09-20 14:59:36 sayHello (1) STDOUT file-GZ5ffVQ047j2QXZ2ZkFx4YxG
2023-09-20 14:59:38 sayHello (1) STDOUT file-GZ5ffX0047j2QXZ2ZkFx4YxK
2023-09-20 14:59:41 sayHello (1) STDOUT file-GZ5ffXQ047jGYZ91x6KG32Jp
2023-09-20 14:59:43 sayHello (1) STDOUT file-GZ5ffY8047jF2PY3609JPBKB
sayHello (1) (hello:nf_task_entry) (done) job-cccc
amy 2023-09-20 14:58:52 (runtime 0:00:52)
Output: exit_code = 0

Advanced Options: Running a Nextflow Pipeline Executable (App or Applet)

Nextflow Execution on DNAnexus

The Nextflow pipeline executable is launched as a job tree, with one head job running the Nextflow executor, and multiple subjobs running a single process each. Throughout the pipeline’s execution, the head job remains in “running” state and supervises the job tree’s execution.

Nextflow Execution Log File

When a Nextflow head job (i.e. job-xxxx) enters its terminal state (i.e. "done" or "failed"), a Nextflow log file with filename as nextflow-<job-xxxx>.log will be written to the destination path of the head job.

Private Docker Repository

DNAnexus supports Docker container engines for the Nextflow pipeline execution environment. The pipeline developer may refer to a public Docker repository or a private one. When the pipeline is referencing a private Docker repository, you should provide your Docker credential file as a file input of docker_creds to the Nextflow pipeline executable when launching the job tree.

Syntax of a private Docker credential:

{
  "docker_registry": {
    "registry": "url-to-registry",
    "username": "name123",
    "token": "12345678"
  }
}

It is encouraged to save this credential file in a separate project where only limited users have permission to access it for privacy reasons.

Nextflow Pipeline Executable Inputs and Outputs

Specifying Input Values to a Nextflow Pipeline Executable

Below are all possible means that you can specify an input value at build time and runtime. They are listed in order of precedence (items listed first have greater precedence and override items listed further down the list):

1. Executable (app or applet) run time

   1. DNAnexus Platform app or applet input.

      • CLI example: dx run project-xxxx:applet-xxxx -i reads_fastqgz=project-xxxx:file-yyyy

      • reads_fastqgz is an example of an executable input parameter name. All Nextflow pipeline inputs can be configured and exposed by the pipeline developer using an nf-core flavored pipeline schema file (nextflow_schema.json).

      • When the input parameter is expecting a file, you need to specify the value in a certain format based on the class of the input parameter. When the input is of the “file” class, use DNAnexus qualified ID (i.e. absolute path to the file object such as “project-xxxx:file-yyyy”); when the input is of the “string” class, use the DNAnexus URI (“dx://project-xxxx:/path/to/file”). See table below for full descriptions of the formatting of PATHs.

      • You can use dx run <app(let)> --help to query the class of each input parameter at the app(let) level. In the example code block below, fasta is an input parameter of a file object, while fasta_fai is an input parameter of a string object. You will then use DNAnexus qualifiedID format for fasta, and DNAnexus URI format for fasta_fai.

      • The DNAnexus object class of each input parameter is based on the “type” and “format” specified in the pipeline’s nextflow_schema.json, when it exists. See additional documentation here to understand how Nextflow input parameter’s type and format (when applicable) converts to an app or applet’s input class.

      • It is recommended to always use the app/applet means for specifying input values. The platform validates the input class and existence before the job is created.

      • All inputs for a Nextflow pipeline executable are set as “optional” inputs. This allows users to have flexibility to specify input via other means.

   2. Nextflow pipeline command line input parameter (i.e. nextflow_pipeline_params). This is an optional "string" class input, available for any Nextflow pipeline executable upon it being built.

      • CLI example: dx run project-xxxx:applet-xxxx -i nextflow_pipeline_params="--foo=xxxx --bar=yyyy", where "--foo=xxxx --bar=yyyy" corresponds to the "--something value" pattern of Nextflow input specification referenced here.

      • Because nextflow_pipeline_params is a string type parameter with file-path format, use the DNAnexus URI format when the file is stored on DNAnexus.

   3. Nextflow options parameter (i.e. nextflow_run_opts). This is a optional "string" class input, available for any Nextflow pipeline executable upon it being built.

      • CLI example: dx run project-xxxx:applet-xxxx -i nextflow_run_opts=“-profile test”, where -profile is single-dash prefix parameter that corresponds to the Nextflow run options pattern, specifying a preset input configuration.

   4. Nextflow parameter file (i.e. nextflow_params_file). This is a optional "file" class input, available for any Nextflow pipeline executable that is being built.

      • CLI example: dx run project-xxxx:applet-xxxx -i nextflow_params_file=project-xxxx:file-yyyy, where project-xxxx:file-yyyy is the DNAnexus qualified ID of the file being passed to nextflow run -params-file <file>. This corresponds to -params-file option of nextflow run.

   5. Nextflow soft configuration override file (i.e. nextflow_soft_confs). This is a optional "array:file" class input, available for any Nextflow pipeline executable that is being built.

      • CLI example: dx run project-xxxx:applet-xxxx -i nextflow_soft_confs=project-xxxx:file-1111 -i nextflow_soft_confs=project-xxxx:file-2222, where project-xxxx:file-1111 and project-xxxx:file-2222 are the DNAnexus qualified IDs of the file being passed to nextflow run -c <config-file1> -c <config-file2>. This corresponds to -c option of nextflow run, and the order specified for this array of file input is preserved when passing to the nextflow run execution.

      • The soft configuration file can be used for assigning default values of configuration scopes (such as process).

      • It is highly recommended to use nextflow_params_file as a replacement to using nextflow_soft_confs for the use case of specifying parameter values, especially when running Nextflow DSL2 nf-core pipelines. Read more about this at nf-core documentation.

2. Pipeline source code:

   1. nextflow_schema.json

      • Pipeline developers may specify default values of inputs in the nextflow_schema.json file.

      • If an input parameter is of Nextflow’s string type with file-path format, use DNAnexus URI format when the file is stored on DNAnexus.

   2. nextflow.config

      • Pipeline developers may specify default values of inputs in thenextflow.config file.

      • Pipeline developers may specify a default profile value using --profile <value>, when building the executable. e.g. dx build --nextflow --profile test.

   3. main.nf , sourcecode.nf

      • Pipeline developers may specify default values of inputs in the Nextflow source code file (*.nf).

      • If an input parameter is of Nextflow’s string type with file-path format, use the DNAnexus URI format when the file is stored on DNAnexus.

# Query for the class of each input parameter
$ dx run project-yyyy:applet-xxxx --help
usage: dx run project-yyyy:applet-xxxx [-iINPUT_NAME=VALUE ...]

Applet: example_applet

example_applet

Inputs:
…
  fasta: [-ifasta=(file)]
…

  fasta_fai: [-ifasta_fai=(string)]
…


# Assign values of the parameter based on the class of the parameter
$ dx run project-yyyy:applet-xxxx -ifasta=”project-xxxx:file-yyyy” -ifasta_fai=”dx://project-xxxx:/path/to/file”

Formats of PATH to File, Folder, or Wildcards

While you can specify a file input parameter’s value at different places as seen above, the valid PATH format referring to the same file will be different depending on the level (DNAnexus API/CLI level or Nextflow script-level) and the class (file object or string) of the executable’s input parameter. Examples of this are given below.

Scenarios	Valid PATH format
• App or applet input parameter class as file object • CLI/API level (e.g. dx run --destination PATH)	DNAnexus qualified ID (i.e. absolute path to the file object). • E.g. (file): project-xxxx:file-yyyy, project-xxxx:/path/to/file • E.g. (folder): project-xxxx:/path/to/folder/
• App or applet input parameter class as string • Nextflow configuration and source code files (e.g. nextflow_schema.json, nextflow.config, main.nf, sourcecode.nf)	DNAnexus URI. • E.g. (file): dx://project-xxxx:/path/to/file • E.g. (folder): dx://project-xxxx:/path/to/folder/ • E.g. (wildcard): dx://project-xxxx:/path/to/wildcard_files

Specifying a Nextflow Job Tree Output Folder

When launching a DNAnexus job, you can specify a job-level output destination (e.g. project-xxxx:/destination/) using the platform-level optional parameter on the UI or on the CLI. In addition, when there is publishDir specified in the pipeline, each output file will be located at <dx_run_path>/<publishDir>/, where <dx_run_path> is the job-level output destination, and <publishDir> is the path assigned per Nextflow script’s process.

Read more detail about the output folder specification and publishDir here. Find an example on how to construct output paths of an nf-core pipeline job tree at run time from our FAQ.

Advanced Options: Building a Nextflow Pipeline Executable

Nextflow Pipeline Executable Permissions

By default, the Platform limits apps' and applets' ability to read and write data. Nextflow pipeline apps and applets have the following capabilities that are exceptions to these limits:

• External internet access ("network": ["*"]) - This is required for Nextflow pipeline apps and applets to be able to pull Docker images from external docker registries at runtime.

• UPLOAD access to the project in which a Nextflow pipeline job is run ("project": "UPLOAD") - This is required in order for Nextflow pipeline jobs to record the progress of executions, and preserve the run cache, in order to enable resume functionality.

You can modify a Nextflow pipeline app or applet's permissions by overriding the default values when building from a local disk, using the --extra-args flag with dx build. An example:

$ dx build --nextflow /path/to/hello --extra-args \
    '{"access":{"network": [], "allProjects":"VIEW"}}'
...
{"id": "applet-yyyy"}

In this example, note:

• "network": [] prevents jobs from accessing the internet.

• "allProjects":"VIEW" increases jobs' access permission level to VIEW. This means that each job will have "read" access to projects that can be accessed by the user running the job. Use this carefully. This permission setting can be useful when expected input file PATHs are provided as DNAnexus URIs - via a samplesheet.csv, for example - from projects other than the one in which a job is being run.

Advanced Building and Importing Pipelines

There are additional options for dx build --nextflow:

Options	Class	Description
--profile PROFILE	string	Set default profile for the Nextflow pipeline executable.
--repository REPOSITORY	string	Specifies a Git repository of a Nextflow pipeline. Incompatible with --remote.
--repository-tag TAG	string	Specifies tag for Git repository. Can be used only with --repository.
--git-credentials GIT_CREDENTIALS	file	Git credentials used to access Nextflow pipelines from private Git repositories. Can be used only with --repository. More information about the file syntax can be found here.
--cache-docker	flag	Stores a container image tarball in the currently selected project in /.cached_dockerImages. Currently only docker engine is supported. Incompatible with --remote.
--nextflow-pipeline-params NEXTFLOW_PIPELINE_PARAMS	string	Custom pipeline parameters to be referenced when collecting the docker images.
--docker-secrets DOCKER_SECRETS	file	A dx file id with credentials for a private docker repository.

Use dx build --help for more information.

Private Nextflow Pipeline Repository

When the Nextflow pipeline to be imported is from a private repository, you must provide a file object that contains the credentials needed to access the repository. Via the CLI, use the--git-credentials flag, and format the object as follows:

providers {
  github {
    user = 'username'
    password = 'ghp_xxxx'
  }
}

To safeguard this credentials field object, store it in a separate project that only you can access.

Platform File Objects as Runtime Docker Images

When building a Nextflow pipeline executable, you can replace any Docker container with a Platform file object in tarball format. These Docker tarball objects serve as substitutes for referencing external Docker repositories.

This approach enhances the provenance and reproducibility of the pipeline by minimizing reliance on external dependencies, thereby reducing associated risks. Additionally, it fortifies data security by eliminating the need for internet access to external resources, during pipeline execution.

Two methods are available for preparing Docker images as tarball file objects on the platform: Built-in Docker image caching or Manually preparing the tarballs.

Built-in Docker Image Caching vs. Manually Preparing Tarballs

	Built-in Docker image caching	Manually preparing tarballs
Requires running a "building job" with external internet access?	Yes, if building an applet for the first time or if any image is going to be updated. No internet access required upon rebuild.	No
Docker images packaged as bundledDepends?	Yes. For Docker images that will be used in the execution, they are cached and bundled at build time.	No. Docker tarballs resolved at runtime.
At runtime	Job will attempt to access Docker cached as bundledDepends. If this fails, the job will attempt to find the image on the Platform. If this fails, the job will try to pull the images from the external repository, via the internet.	Job will attempt to locate the Docker image based on the Docker cache path referenced. If this fails, the job will attempt to pull from the external repository, via the internet.

Built-in Docker Image Caching

This method initiates a building job that begins by taking the pipeline script, then identifying Docker containers by scanning the script's source code based on the final execution tree. Next, the job converts the containers to tarballs, and saves those tarballs to the project in which the job is running. Finally, the job builds the Nextflow pipeline executable, bundling in the tarballs, as bundledDepends.

You can use built-in caching via the CLI by using the flag --cache-docker at build time. All cached Docker tarballs are stored as file objects, within the Docker cache path, at project-xxxx:/.cached_docker_images/<image_name>/<image_name>_<version>.

An example:

$ dx build --nextflow /path/to/hello \
--cache-docker \
--nextflow-pipeline-params "--alpha=1 --beta=foo" \ # when required
--destination project-xxxx:/applets2/hello
...
{"id:"applet-yyyy"}

$ dx tree /.cached_docker_images/
/.cached_docker_images/
├── samtools
│   └── samtools_1.16.1--h6899075_1
├── multiqc
│   └── multiqc_1.18--pyhdfd78af_0
└── fastqc
    └── fastqc_0.11.9--0

If you need to access a Docker container that's stored in a private repository, you must provide, along with the flag --docker-secrets, a file object that contains the credentials needed to access the repository. This object must be in the following format:

"docker_registry": {
  "registry": "url-to-registry",
  "username": "name123",
  "token": "12345678"
}

• When a pipeline requires specific inputs, such as file objects, sample values must be present within the project in which building job is to execute. These values must be provided along with the flag --nextflow-pipeline-params.

  • It's crucial that these sample values be structured in the same way as actual input data will be structured. This ensures that the execution logic of the Nextflow pipeline remains intact. During the build process, use small files, containing data representative of the larger dataset, as sample data, in order to reduce file localization overhead.

• For pipelines featuring conditional process trees determined by input values, you may provide mocked input values for caching Docker containers used by processes affected by the condition.

• A building job requires CONTRIBUTE or higher permission to the destination project, i.e. the project in which it will place tarballs created from Docker containers.

• Pipeline source code will be saved at/.nf_source/<pipeline_folder_name>/ in the destination project. The user is responsible for cleaning up this folder after the executable has been built.

Manually Preparing Tarballs

You can manually convert Docker images to tarball file objects. Within Nextflow pipeline scripts, you must then reference the location of each such tarball, in one of the following three ways:

1. Reference each tarball by its unique Platform ID (e.g. dx://project-xxxx:file-yyyy). Use this approach if you want deterministic execution behavior. You can use Platform IDs in Nextflow pipeline scripts (*.nf) or configuration files (*.config), as follows:

# In a Nextflow pipeline script:
process foo {
  container 'dx://project-xxxx:file-yyyy'
  
  '''
  do this
  '''
}

# In nextflow.config    // at root folder of the nextflow pipeline:
process {
    withName:foo {
        container = 'dx://project-xxxx:file-yyyy'
    }   
}

When accessing a Platform project, a Nextflow pipeline job needsVIEW or higher permission to the project.

2. Within a Nextflow pipeline script, you can also reference a Docker image by using its full image name. Use this name within a path that's in the following format: project-xxxx:/.cached_docker_images/<image_name>/<image_name>_<version> An example:

# In nextflow configuration file:
docker.enabled = true
docker.registry = 'quay.io'

# In the Nextflow pipeline script:
process bar {
  container 'quay.io/biocontainers/tabix:1.11--hdfd78af_0'

  '''
  do this
  '''
}

Note that no file extension is necessary, and that project-xxxx is the project where the Nextflow pipeline executable was built and will be executed. For.cached_docker_images, substitute the name of the folder in which these images have been stored.Note as well that an exact <version> reference must be included - latest is not an accepted tag in this context.

At Nextflow pipeline executable runtime:

1. If no image is found at the path provided, the Nextflow pipeline job will attempt to pull the Docker image from the remote external registry, based on the image name. This pull attempt requires internet access.

2. If the version is referenced as latest, or if no version tag is provided, the Nextflow pipeline job will attempt to search the digest of the image’s latest reference from the external Docker repository and use it to search for the corresponding tarball on the platform. This digest search requires internet access. If no digest is found, or if there is no internet access, the execution will fail.

Here are several examples of tarball file object paths and names, as constructed from image names and version tags:

Image Name	Version Tag	Tarball File Object Path and Name
quay.io/biocontainers/tabix	1.11--hdfd78af_0	project-xxxx:/.cached_docker_images/tabix/tabix_1.11--hdfd78af_0
python	3.9-slim	project-xxxx:/.cached_docker_images/python/python_3.9-slim
python	latest	Nextflow pipeline job will attempt to pull from remote external registry

3. You can also reference Docker image names in pipeline scripts by digest - for example, <Image_name>@sha256:XYZ123…). Note that no file extension is necessary, and that project-xxxx is the project where the Nextflow pipeline executable was built and will be executed. For.cached_docker_images, substitute the name of the folder in which these images have been stored. Note as well that an exact <version> reference must be included - latest is not an accepted tag in this context. In addition, to refer to a tarball file on the Platform in this way, an object property image_digest - for example, “image_digest”:”<IMAGE_DIGEST_HERE>”- needs to have been assigned to it.

   An example:

# In nextflow configuration file:
docker.enabled = true
docker.registry = 'quay.io'

# In the Nextflow pipeline script:
process bar {
  container 'quay.io/biocontainers/tabix@sha256:XYZ123…'
  '''
  do this
  '''
}

Nextflow Input Parameter Type Conversion to DNAnexus Executable Input Parameter Class

Based on the input parameter’s type and format (when applicable) defined in the corresponding nextflow_schema.json file, each parameter will be assigned to the corresponding class (ref1, ref2).

From: Nextflow Input Parameter (defined at nextflow_schema.json) Type	Format	To: DNAnexus Input Parameter Class
string	file-path	file
string	directory-path	string
string	path	string
string	NA	string
integer	NA	int
number	NA	float
boolean	NA	boolean
object	NA	hash

File Input as String or File Class

As a pipeline developer, you can specify a file input variable as {“type”:“string”, “format”:“file-path”} or {“type”:“string”, “format”:“path”}, which will be assign to “file” or “string” class, respectively. When running the executable, based on the class (file or string) of the executable’s input parameter, you will use a specific PATH format to specify the value. See documentation here for an acceptable PATH format for each class.

Converting a URL path to a String

When converting a file reference from a URL format (e.g. dx://project-xxxx:/path/to/file of a DNAnexus URI) to a String, you will use the method toUriString(). Method toURI().toString() does not give the same result, as toURI() removes the context ID (e.g. project-xxxx), and toString() removes the scheme (e.g. dx://). More info about the Nextflow methods here.

Managing intermediate files and publishing outputs

Pipeline Output Setting Using output: block and publishDir

All files generated by a Nextflow job tree will be stored in its session’s corresponding workDir (i.e. the path where the temporary results are stored). On DNAnexus, when the Nextflow pipeline job is run with “preserve_cache=true”, the workDir is set at the path: project-xxxx:/.nextflow_cache_db/<session_id>/work/. project-xxxx is the project where the job took place, and you can follow the path to access all preserved temporary results. It is useful to be able to access these results for investigating the detailed pipeline progress, and use them for resuming job runs for pipeline development purposes. More info about workDir is described here.

When the Nextflow pipeline job was run with “preserve_cache=false” (default), temporary files will be stored in the job’s temporary workspace which will be deconstructed upon the head job enters its terminate state (i.e. “done”, “failed”, or “terminated”). Since a lot of these files are intermediate input/output being passed between processes and expected to be cleaned up after the job is completed, running with “preserve_cache=false” will help reduce project storage cost for files that are not of interest, and also save you from remembering to clean up all temporary files.

To save the final results of interest, and to display them as the Nextflow pipeline executable’s output, you can declare output files matching the declaration under the script’s output: block, and use Nextflow’s optional publishDir directive to publish them.

This will make the published output files as the Nextflow pipeline head job’s output, under the executable’s formally defined placeholder output parameter, published_files, as array:file class. Then the files will be organized under the relative folder structure assigned via publishDir. This works for both “preserve_cache=true” and “preserve_cache=false”. Only the “copy” publish mode is supported on DNAnexus.

Values of publishDir

At pipeline development time, the valid value of publishDir can be:

• A local path string , e.g. “publishDir path: ./path/to/nf/publish_dir/”,

• A dynamic string value defined as a pipeline input parameter (e.g. “params.outdir”, where “outdir” is a string-class input), allowing pipeline users to determine parameter values at runtime. For example, “publishDir path: '${params.outdir}/some/dir/'” or './some/dir/${params.outdir}/' or './some/dir/${params.outdir}/some/dir/' .

  • When publishDir is defined this way, the user who launches the Nextflow pipeline executable is responsible for constructing the publishDir to be a valid relative path.

Find an example on how to construct output paths for an nf-core pipeline job tree at run time from our FAQ.

publishDir is NOT supported on DNAnexus when assigned as an absolute path (e.g. /path/to/nf/publish_dir/, which starts at root (/)). If an absolute path is defined for the publishDir, no output files will be generated as the job’s output parameter “published_files”.

Queue Size Configuration

The queueSize option is part of Nextflow’s executor configuration. It defines how many tasks the executor will handle in a parallel manner. On DNAnexus, this represents the number of subjobs being created at a time (5 by default) by the Nextflow pipeline executable’s head job. If the pipeline’s executor configuration has a value assigned to queueSize, it will override the default value. If the value exceeds the upper limit (1000) on DNAnexus, the root job will error out. See the Nextflow executor configuration page for examples.

Instance Type Determination

Head job instance type determination

The head job of the job tree defaults to running on instance type mem2_ssd1_v2_x4 in AWS regions and azure:mem2_ssd1_x4 in Azure regions. It is possible for users to change to a different instance type than the default, but is not recommended. The head job executes and monitors the subjobs. Changing the instance type for the head job will not affect the computing resources available for subjobs, where most of the heavy computation takes place (see below where to configure instance types for Nextflow processes). Changing the instance type for the head job may be necessary only if it is running out of memory or disk space when staging input files, collecting pipeline output files, or uploading pipeline output files to the project.

Subjob instance type determination

Each subjob’s instance type is determined based on the profile information provided in the Nextflow pipeline script. You can specify required instances by instance type name via Nextflow’s machineType directive (example below), or using a set of system requirements (e.g. cpus, memory, disk, etc.) according to the official Nextflow documentation. The executor will choose the corresponding instance type that matches the minimal requirement of what is described in the Nextflow pipeline profile using the following logic:

1. Choose the cheapest instance that satisfies the system requirements.

2. Use only SSD type instances.

3. For all things equal (price and instance specifications), it will prefer a version2 (v2) instance type.

Order of precedence for subjob instance type determination:

1. The value assigned to machineType directive.

2. Values assigned to cpus, memory, and disk directives in their configuration.

An example command for specifying machineType by DNAnexus instance type name is provided below:

process foo {
  machineType 'mem1_ssd1_v2_x36'

  """
  <your script here>
  """
}

In addition to being used for instance type determination, values assigned to cpus, memory, and disk directives can also be recalled by Nextflow's process implicit variables of task object (e.g. ${task.cpus}, ${task.memory}, ${task.disk}) at runtime for task allocation.

• It is possible the actual selected instance type’s CPUs, memory, or disk capacity being inconsistent with what is allocated by task. The former follows above precedence to determine the instance type, the latter is returning the value assigned in the configuration file.

• When using Docker as the runtime container, Nextflow executor is propagating task execution settings to the Docker run command. For example, if task.memory is specified, this would then be the maximum amount of memory the container is allowed to use, for example: docker run --memory ${task.memory}

Nextflow Resume

Preserve Run Caches and Resuming Previous Jobs

Nextflow’s resume feature enables skipping the processes that have been finished successfully and cached in previous runs. The new run can directly jump to downstream processes without needing to start from the beginning of the pipeline. By retrieving cached progress, Nextflow resume helps pipeline developers to save both time and compute costs. It is helpful for testing and troubleshooting when building and developing a Nextflow pipeline.

Nextflow utilizes a scratch storage area for caching and preserving each task’s temporary results. The directory is called “working directory”, and the directory’s path is defined by

• The session id, a universally unique identifier (UUID) associated with current execution

• Each task’s unique hash ID: a hash number composed of each task’s input values, input files, command line strings, container ID (e.g. Docker image), conda environment, environment modules, and executed scripts in the bin directory, when applicable.

You can utilize the Nextflow resume feature with the following Nextflow pipeline executable parameters:

• preserve_cache Boolean type. Default value is false. When set to true, the run will be cached in the current project for future resumes. For example:

  • dx run applet-xxxx -i reads_fastqgz=project-xxxx:file-yyyy -i preserve_cache=true

  • This enables the Nextflow job tree to preserve cached information as well as all temporary results in the project where it is executed under the following paths, based on its session ID and each subjob’s unique ID.

  • The session's cache directory containing information on the location of the workDir, the session progress, etc. are saved to project-xxxx:/.nextflow_cache_db/<session_id>/cache.tar , where project-xxxx is the project where the job tree is executed.

  • Each task's working directory will be saved to project-xxxx:/.nextflow_cache_db/<session_id>/work/<2digit>/<30characters>/ , where <2digit>/<30characters>/ is technically the task’s unique ID, and project-xxxx is the project where the job tree is executed.

• resume String type. Default value is an empty string, and the run will start from scratch. When assigned with a session id, the run will resume from what is cached for the session id on the project. When assigned with “true” or “last”, the run will determine the session id that corresponds to the latest valid execution in the current project and resume the run from it. For example:

  • dx run applet-xxxxm -i reads_fastqgz=“project-xxxx:file-yyyy” -i resume="<session_id>”

• When preserve_cache=true, DNAnexus executor overrides the value of workDir of the job tree to be project-xxxx:/.nextflow_cache_db/<session_id>/work/, where project-xxxx is the project where the job tree was executed.

• When a new job is launched and resumes a cached session (session_id may be formatted as 12345678-1234-1234-1234-123456789012 for example), the new job not only resumes from where the cache left at, but also shares the same session_id with the cached session it resumes. When a new job makes progress in a session and if the job is being cached, it creates temporary results to the same session’s workDir. This will generate a new cache directory (cache.tar) with the latest cache information.

• You can have many Nextflow job trees sharing the same sessionID and writing to the same path for workDir and creating its own cache.tar, while only the latest job that ends in “done” or “failed” state will be preserved on the project.

• When the head job enters its terminal state (e.g. “failed” or “terminated”) that is not caused by the executor, no cache directory will be preserved, even when the job was run with preserve_cache=true. Subsequent new jobs will not be able to resume from this job run. Examples: a job tree fails due to exceeding a cost limit; a user terminates a job of the job tree, etc.

Below are four possible scenarios and the recommended use cases for –i resume:

Scenarios	Parameters	Use Cases	Note
1 (default)	resume=“” (empty string) and preserve_cache=false	Production data processing; most high volume use cases
2	resume=“” (empty string) and preserve_cache=true	Pipeline development; only happens for the first few pipeline tests. During development; it would be useful to see all intermediate results in workDir.	Only up to 20 Nextflow sessions can be preserved per project.
3	resume=<session_ID>|“true”|“last”and preserve_cache=false	Pipeline development; pipeline developers can investigate the job workspace with --delay_workspace_destruction and --ssh
4	resume=<session_ID>|“true”|“last” and preserve_cache=true	Pipeline development; only happens for the first few tests.	Only 1 job with the same <session_ID> can run at each time point.

Cache Preserve Limitations and Cleaning Up workDir

It is a good practice to frequently clean up the workDir to save on storage costs. The maximum number of sessions that can be preserved in a DNAnexus project is 20 sessions. If you exceed the limit, the job will generate an error with the following message:

“The number of preserved sessions is already at the limit (N=20) and is true. Please remove the folders in <project-id>:/.nextflow_cache_db/ to be under the limit, if you want to preserve the cache of this run. “

To clean up all preserved sessions under a project, you can delete the entire ./nextflow_cache_db folder. To clean up a specific session’s cached folder, you can delete the specific .nextflow_cache_db/<session_id>/ folder. To delete a folder in UI, you can follow the documentation on deleting objects. To delete a folder in CLI, you can run:

dx rm -r project-xxxx:/.nextflow_cache_db/              # cleanup ALL sessions caches
dx rm -r project-xxxx:/.nextflow_cache_db/<session_id>/ # clean up a specific session’s cache

Note that deleting an object on UI or using CLI dx rm cannot be undone. Once the session work directory is deleted or moved, subsequent runs will not be able to resume from the session.

For each session, only one job is allowed to resume the session’s cached results, and preserve its own progress to this session. There is no limit if multiple jobs resume and preserve multiple different sessions, as long as each job is preserving a different session. There is also no limit for multiple jobs to resume the same session, as long as only one or none is preserving the progress to the session.

Nextflow’s errorStrategy

Nextflow’s errorStrategy directive allows you to define how the error condition is managed by the Nextflow executor at the process level. When an error status is returned, by default, the process and other pending processes stop immediately (i.e. errorStrategy terminate), and this in turn forces the entire pipeline execution to be terminated.

There are four error strategy options of Nextflow executor: terminate, finish, ignore, and retry. Below is a table of behaviors for each strategy. Note that "all other subjobs" in the third column have not yet entered their terminal states.

errorStrategy	Subjob Error	Head Job	All Other Subjobs
terminate	Job properties set with: "nextflow_errorStrategy”:"terminate”, "nextflow_errored_subjob”:"self” End in “failed” state immediately	Job properties set with: "nextflow_errorStrategy”:”terminate”, "nextflow_errored_subjob”:”job-xxxx", "nextflow_terminated_subjob”:”job-yyyy, job-zzzz" , where job-xxxx is the errored subjob, and job-yyyy is the other subjobs that were terminated due to this error. End in “failed” state immediately, with error message, “Job was terminated by Nextflow with terminate errorStrategy for job-xxxx, check the job log to find the failure”	End in “failed” state immediately.
finish	Job properties set with: "nextflow_errorStrategy”:"finish”, "nextflow_errored_subjob”:"self” End in “done” state immediately	Job properties set with: "nextflow_errorStrategy:finish”, "nextflow_errored_subjob”:”job-xxxx, job-2xxx" , where job-xxxx and job-2xxxx are the the errored subjobs, Not create new subjobs after the time point of error End in “failed” state eventually, after other existing subjobs enter their terminal states, with error message “Job was ended with finish errorStrategy for job-xxxx, check the job log to find the failure.”.	Keep on running until entering their terminal states. If error occurs in any of these subjobs (e.g. job-2xxx), finish errorStrategy will be applied to the subjob because a finish errorStrategy was hit first, ignoring any other error strategies set in the pipeline’s source code or configuration, as Nextflow’s default behavior.
retry	Job properties set with: "nextflow_errorStrategy”:”retry”, "nex tflow_errored_subjob”:”self" End in “done” state immediately	Spin off a new subjob which retries the errored job, with the following job name: <name> (retry: <RetryCount>) , where <name> is the original subjob name and <RetryCount> is the order of this retry (ex. retry:1, retry:2). End in a terminal state depending on the terminal states of other currently existing subjobs that are not yet in their terminal states. Can be either “done”, “failed” or “terminated”.	Keep on running until enter their terminal states. If error occurs in one of these subjobs, their errorStrategy set in the subjob’s corresponding Nextflow process is applied.
ignore	Job properties set with: "nextflow_errorStrategy”:”ignore”, "nextflow_errored_subjob”:”self" End in “done” state immediately	Have job properties set with: "nextflow_erorrStrategy”:”ignore”, "nextflow_errorred_subjob”:”job-1xxx, job-2xxx" Shows “subjob(s) <job-1xxxx>, <job-2xxxx> runs into Nextflow process errors’ ignore errorStrategy were applied” in the end of the job log. End in a terminal state depending on the terminal states of other currently existing subjobs that are not yet in their terminal states. Can be either “done”, “failed” or “terminated”.	Keep on running until they enter their terminal states. If error occurs in one of these subjobs, their errorStrategy set in the subjob’s corresponding Nextflow process is applied.

When more than one errorStrategy directives are applied to a pipeline job tree, the following rules will be applied depending on the first errorStrategy used.

• When terminate is the first errorStrategy directive to be triggered in a subjob, all the other ongoing subjobs will result in the "failed" state immediately.

• When finish is the first errorStrategy directive to be triggered in a subjob, any other errorStrategy that is reached in the remaining ongoing subjob(s) will also apply the finish errorStrategy, ignoring any other error stategies set in the pipeline’s source code or configuration.

• If the retry errorStrategy is the first directive triggered in a subjob, if any of the remaining subjobs trigger a terminate, finish, or ignore errorStrategy, these other errorStrategy directives will be applied to the corresponding subjob.

• When ignore is the first errorStrategy directive to trigger in a subjob , and if any of terminate, finish, or retry errorStrategy directives applies to the remaining subjob(s), that other errorStrategy will be applied to the corresponding subjob.

Independent from Nextflow process-level error conditions, when a Nextflow subjob encounters platform-related restartable errors, such as "ExecutionError", "UnresponsiveWorker", "JMInternalError", "AppInternalError", or "JobTimeoutExceeded", the subjob will follow the executionPolicy determined to the subjob and and restart itself. It will not restart from the head job.

FAQ

My Nextflow job tree failed, how do I find where the errors are?

A: You can find the errored subjob’s job ID from the head job’s nextflow_errored_subjob and nextflow_errorStrategy properties to investigate which subjob failed and which errorStrategy was applied. To query these errorStrategy related properties in CLI, you can run the following command:

$ dx describe job-xxxx --json | jq -r .properties.nextflow_errored_subjob
job-yyyy
$ dx describe job-xxxx --json | jq -r .properties.nextflow_errorStrategy
terminate

where job-xxxx is the head job’s job ID.

Once you find the errored subjob, you can investigate the job log using the Monitor page by accessing the URL "https:/platform.dnanexus.com/projects/<projectID>/monitor/job/<jobID>", where jobID is the subjob's ID (e.g. job-yyyy), or watch the job log in CLI using dx watch job-yyyy.

If you have the preserve_cache value set to true when start running the Nextflow pipeline executable, you can trace the cache workDir (e.g. project-xxxx:/.nextflow_cache_db/<session_id>/work/) and investigate the intermediate results of this run.

What is the version of Nextflow that is used?

A: You can find the Nextflow version used by reading the log of the head job. Each built Nextflow executable is locked down to the specific version of Nextflow executor.

What container runtimes are supported?

A: DNAnexus supports Docker as the container runtime for Nextflow pipeline applets. It is recommended to set docker.enabled=true in the Nextflow pipeline configuration, which enables the built Nextflow pipeline applet to execute the pipeline using Docker.

My job hangs at the end of the analysis. What can I do to avoid this problem?

A: There can be many possibilities causing the head job to hang. One of the known reasons is caused by the trace report file being written directly to a DNAnexus URI (e.g. dx://project-xxxx:/path/to/file). To avoid this cause, we suggest you to specify ​​-with-trace path/to/tracefile (using a local path string) to the Nextflow pipeline applet’s nextflow_run_opts input parameter.

Can I have an example of how to construct an output path when I run a Nextflow pipeline with params.outdir, publishDir and job-level destination?

Taking nf-core/sarek (3.3.1) as an example, start with reading the pipeline's logic:

1. The pipeline's publishDir is constructed with a prefix of the params.outdir variable followed by each task's name for each subfolder: publishDir = [ path: { "${params.outdir}/${...}" }, ... ]

2. params.outdir is a required input parameter to the pipeline, and the default value ofparams.outdir is null. The user running the corresponding Nextflow pipeline executable must specify a value to params.outdir which will:

   1. Meet the input requirement for executing the pipeline.

   2. Resolve the value ofpublishDir, with outdir as the leading path and each task's name as the subfolder name.

To specify a value of params.outdir for the Nextflow pipeline executable built from the nf-core/sarek pipeline script, you can use the following command:

dx run project-xxxx:applet-zzzz \
-i outdir=./local/to/outdir \   # assign "./local/to/outdir" params.outdir
--brief -y

You can also set a job tree's output destination using --destination :

dx run project-xxxx:applet-zzzz \
-i outdir=./local/to/outdir \   # assign "./local/to/outdir" params.outdir
--destination project-xxxx:/path/to/jobtree/destination/ \ 
--brief -y

This above command will construct the final output paths in the following manner:

1. project-xxxx:/path/to/jobtree/destination/ as the destination of the job tree's shared output folder.

2. project-xxxx:/path/to/jobtree/destination/local/to/outdir as the shared output folder of the all tasks/processes/subjobs of this pipeline.

3. project-xxxx:/path/to/jobtree/destination/local/to/outdir/<task_name> as the output folder of each specific task/process/subjob of this pipeline.

1. This example is built based on Specifying A Nextflow Job Tree Output Folder and Managing intermediate files and publishing outputs.

2. Not all Nextflow pipelines haveparams.outdir as input, nor do all of them use params.outdir in publishDir. Read the source script of the Nextflow pipeline for the actual context of usage and requirements forparams.outdir andpublishDir.