Running Nextflow Pipelines

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.

dx build --nextflow --extra-args='{"runSpec": {"release": "20.04"}}'

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. For more information on how the exposed parameters are used at run time, see specifying input values to a Nextflow pipeline executable.

• (Optional) Subfolders and other configuration files. Subfolders and other configuration files can be referenced by the main Nextflow file or nextflow.config via the include 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:

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 in the project, for example, hello.

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

For Nextflow pipelines stored in private repositories, you'll need to provide credentials to access them. Use the --git-credentials option with a DNAnexus file containing your authentication details. The file should be specified using either its qualified ID or path on the Platform. See the Private Nextflow Pipeline Repository section for more details on setting up and formatting these credentials.

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"}

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 --, 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 handles 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.

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 (job-xxxx) enters its terminal state, either "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, which is the 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 in the Nextflow Input Parameter Type Conversion section 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, available as nextflow_pipeline_params. This is an optional "string" class input, available for any Nextflow pipeline executable on 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 in the Nextflow Configuration documentation.

      • 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 nextflow_run_opts. This is an optional "string" class input, available for any Nextflow pipeline executable on 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 nextflow_params_file. This is an 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 nextflow_soft_confs. This is an 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 the nextflow.config file.

      • Pipeline developers may specify a default profile value using --profile <value>, when building the executable. for example 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.

Specifying a Nextflow Job Tree Output Folder

When launching a DNAnexus job, you can specify a job-level output destination such as project-xxxx:/destination/ using the platform-level optional parameter on the UI or on the CLI. For pipelines with publishDir settings, each output file will be saved to <dx_run_path>/<publishDir>/, where <dx_run_path> is the job-level output destination and <publishDir> is the path assigned by the Nextflow script's process.

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

Using an AWS S3 Bucket as a Work Directory for Nextflow Pipeline Runs

You can have your Nextflow pipeline runs use an Amazon Web Services (AWS) S3 bucket as a work directory. To do this, follow the steps outlined below.

Step 1. Configure Your AWS Account to Trust the DNAnexus Platform as an OIDC Identity Provider

Follow the steps outlined here to configure your AWS account to trust the Platform, as an OIDC identity provider. Be sure to take note of the value you enter in the "Audience" field. You'll need to use this value in a configuration file used by your pipeline, to enable pipeline runs to access the S3 bucket.

Step 2. Configure an AWS IAM Role with the Proper Trust and Permissions Policies

Next, configure an AWS Identity and Access Management (IAM) role, such that its permissions and trust policies allow Platform jobs that assume this role, to access and use resources in the S3 bucket.

Permissions Policy

The following example shows how to structure an IAM role's permission policy, to enable the role to use an S3 bucket - accessible via the S3 URI s3://my-nextflow-s3-workdir - as the work directory of Nextflow pipeline runs:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:DeleteObject",
        "s3:GetObject",
        "s3:ListBucket",
        "s3:PutObject"
      ],
      "Resource": [
        "arn:aws:s3:::my-nextflow-s3-workdir",
        "arn:aws:s3:::my-nextflow-s3-workdir/*"
      ]
    }
  ]
}

Note in the above example:

• The "Action" section contains a list of the actions the role is allowed to perform, including deleting, getting, listing, and putting objects.

• The two entries in the list in the "Resource" section enable the role to access all resources in the bucket accessible via the S3 URI my-nextflow-s3-workdir.

Trust Policy

The following example shows how to configure an IAM role's trust policy, to allow only properly configured Platform jobs to assume the role:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sts:AssumeRoleWithWebIdentity",
      "Principal": {
        "Federated": "arn:aws:iam::123456789012:oidc-provider/job-oidc.dnanexus.com/"
        ,
        "Condition": {
         "StringEquals": {
            "job-oidc.dnanexus.com/:aud": "dx_nextflow_s3_scratch_token_aud"
          },
         "StringEquals": {
            "job-oidc.dnanexus.com/:sub": "project_id;project-xxxx;launched_by;user-aaaa"
          }
        }
      }
    }
  ]
}

Note in the above example:

• To assume the role, a job must be launched from within a specific Platform project (in this case, project-xxxx).

• To assume the role, a job must be launched by a specific Platform user (in this case, user-aaaa).

• Via the "Federated" setting in the "Principal" section, the policy configures the role to trust the Platform as an OIDC identity provider, as accessible at job-oidc.dnanexus.com.

Step 3. Configure Your Nextflow Pipeline's Configuration File to Access the S3 Bucket

Next you need to configure your pipeline so that when it's run, it can access the S3 bucket. To do this, add, in a configuration file, a dnanexus config scope that includes the properties shown in this example:

# In a nextflow configuration file:

aws { region = '<aws region>'}

dnanexus {
 workDir = '<S3 URI path>'
 jobTokenAudience = '<OIDC_audience_name>'
 jobTokenSubjectClaims = '<list of claims separated by commas>'
 iamRoleArnToAssume = '<arn of the role who is set with permission>'
}

Note in the above example:

• workDir is the path to the bucket to be used as a work directory, in S3 URI format.

• jobTokenAudience is the value of "Audience" you defined in Step 1 above.

• jobTokenSubjectClaims is an ordered, comma-separated list of DNAnexus job identity token custom claims - for example, project_id, launched_by - that the job must present, to assume the role that enables bucket access.

• iamRoleArnToAssume is the Amazon Resource Name (ARN) for the role that you configured in Step 2 above, and that will be assumed by jobs to access the bucket.

• You need also to configure your pipeline to access the bucket within the appropriate AWS region, which you specify via the region parameter, within an aws config scope.

Using Subject Claims to Control Bucket Access

When configuring the trust policy for the role that allows access to the S3 bucket, use custom subject claims to control which jobs can assume this role. Here are some typical combinations that we recommend, with their implications:

Having included custom subject claims in the trust policy for the role, you need then, in the aforementioned Nextflow configuration file, to set the value of jobTokenSubjectClaims to equal a comma-separated list of claims, entered in the same order in which you entered them in the trust policy.

For example, if you configured a role's trust policy per the above example, you are requiring a job, to assume the role, to present custom subject claims project_id and launched_by, in that order. In your Nextflow configuration file, set the value of jobTokenSubjectClaims, within the dnanexus config scope, as follows:

# In a nextflow configuration file:
dnanexus {
 ...
 jobTokenSubjectClaims = 'project_id,launched_by'
 ...
}

Note that you must also, within the dna config scope, set the value of iamRoleArnToAssume to that of the appropriate role:

# In a nextflow configuration file:
dnanexus {
 ...
 iamRoleArnToAssume = arn:aws:iam::123456789012:role/NextflowRunIdentityToken
 ...
}

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, 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

Additional options exist for dx build --nextflow:

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'
  }
}

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. Also, 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

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"
}

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:

Option A: Reference each tarball by its unique Platform ID such as 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'
    }
}

Option B: 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.

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

Option C: 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. When referring to a tarball file on the Platform using this method, the file must have an object property image_digest assigned to it. A typical format would be "image_digest":"<IMAGE_DIGEST_HERE>".

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).

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 the Formats of Path to File, Folder or Wildcards section for an acceptable PATH format for each class.

Converting a URL path to a String

When converting a file reference from a URL format to a String, you will use the method toUriString(). An example of a URL format would be dx://project-xxxx:/path/to/file for a DNAnexus URI. The method toURI().toString() does not give the same result because toURI() removes the context ID, such as project-xxxx, and toString() removes the scheme, such as dx://. More information about the Nextflow methods is available in the Nextflow Opening Files documentation.

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, which is 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.

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 when the head job enters its terminate state - "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, for example, "publishDir path: ./path/to/nf/publish_dir/",

• A dynamic string value defined as a pipeline input parameter such as "params.outdir", where "outdir" is a string-class input. This allows 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 handles 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.

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 way. 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). Alternatively, you can use a set of system requirements such as cpus, memory, disk, and other resource parameters 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>
  """
}

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 uses 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 such as Docker image, conda environment, environment modules, and executed scripts in the bin directory, when applicable.

You can use 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:

  • Copy

    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, job status, and configuration data 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 begin without any cached data. 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>"

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

Cache Preserve Limitations and Cleaning Up workDir

It is a good practice to often 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 preserve_cache is true. 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 can resume the session's cached results and preserve its own progress to this session. Multiple jobs can resume and preserve different sessions without limitations, as long as each job preserves a different session. Similarly, multiple jobs can resume the same session without limitations, 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 (the default is errorStrategy terminate). This forces the entire pipeline execution to be terminated.

Four error strategy options exist for 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.

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 subjobs will also apply the finish errorStrategy, ignoring any other error strategies 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 subjobs, 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 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>. In this URL, jobID is the subjob's ID such as job-yyyy. You can also 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 such as 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 such as 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 of params.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 of publishDir, 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 way:

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.