Introduction

This tutorial shows you how to

• Import any nf-core pipeline from their public repository.

• Run the pipeline in Bench.

  • monitor the execution

• .

• Start Bench workspace

  • For this tutorial, the instance size depends on the flow you import, and whether you use a Bench cluster:

    • If using a cluster, choose standard-small or standard-medium for the workspace master node

If conda and/or nextflow are not installed, pipeline-dev will offer to install them.

The Nextflow files are pulled into the nextflow-src subfolder.

All nf-core pipelines conveniently define a "test" profile that specifies a set of validation inputs for the pipeline.

The following command runs this test profile. If a Bench cluster is active, it runs on your Bench cluster, otherwise it runs on the main workspace instance.

When a pipeline is running locally (i.e. not on a Bench cluster), you can monitor the task execution from another terminal with docker ps

When a pipeline is running on your Bench cluster, a few commands help to monitor the tasks and cluster. In another terminal, you can use:

• qstat to see the tasks being pending or running

• tail /data/logs/sge-scaler.log.<latest available workspace reboot time> to check if the cluster is scaling up or down (it currently takes 3 to 5 minutes to get a new node)

• The output of the pipeline is in the outdir folder

• Nextflow work files are under the work folder

• Log files are .nextflow.log* and output.log

After generating a few ICA-specific files (JSON input specs for Flow launch UI + list of inputs for next step's validation launch), the tool identifies which previous versions of the same pipeline have already been deployed (in ICA Flow, pipeline versioning is done by including the version number in the pipeline name, so that's what is checked here). It then asks if you want to update the latest version or create a new one.

Choose "3" and enter a name of your choice to avoid conflicts with other users following this same tutorial.

At the end, the URL of the pipeline is displayed. If you are using a terminal that supports it, Ctrl+click or middle-click can open this URL in your browser.

This launches an analysis in ICA Flow, using the same inputs as the nf-core pipeline's "test" profile.

Some of the input files will have been copied to your ICA project to allow the launch to take place. They are stored in the folder bench-pipeline-dev/temp-data.

Introduction

This tutorial shows you how to

• import an existing ICA Flow pipeline with a supporting validation analysis

• run the pipeline in Bench

  • the execution

• Iterative development: and validate in Bench

  • Modify code

  • Modify Docker image contents ( or method)

Make sure you have access in ICA Flow to:

• the pipeline you want to work with

• an analysis exercising this pipeline, preferably with a short execution time, to use as validation test

For this tutorial, the instance size depends on the flow you import, and whether you use a Bench cluster:

• When using a cluster, choose standard-small or standard-medium for the workspace master node

• Otherwise, choose at least standard-large if you re-import a pipeline that originally came from nf-core, as they typically need 4 or more CPUs to run.

• Select the "single user workspace" permissions (aka "Access limited to workspace owner "), which allows us to deploy pipelines

The starting point is the analysis id that is used as pipeline validation test (the pipeline id is obtained from the analysis metadata).

If no --analysis-id is provided, the tool lists all the successfull analyses in the current project and lets the developer pick one.

• If conda and/or nextflow are not installed, pipeline-dev will offer to install them.

• A folder called imported-flow-analysis is created.

• Pipeline Nextflow assets are downloaded into the nextflow-src sub-folder.

The following command runs this test profile. If a Bench cluster is active, it runs on your Bench cluster, otherwise it runs on the main workspace instance:

When a pipeline is running on your Bench cluster, a few commands help to monitor the tasks and cluster. In another terminal, you can use:

• qstat to see the tasks being pending or running

• tail /data/logs/sge-scaler.log.<latest available workspace reboot time> to check if the cluster is scaling up or down (it currently takes 3 to 5 minutes to get a new node)

• The output of the pipeline is in the outdir folder

• Nextflow work files are under the work folder

• Log files are .nextflow.log* and output.log

Nextflow files (located in the nextflow-src folder) are easy to modify. Depending on your environment (ssh access / docker image with JupyterLab or VNC, with and without Visual Studio code), various source code editors can be used.

After modifying the source code, you can run a validation iteration with the same command as before:

Modifying the Docker image is the next step.

Nextflow (and ICA) allow the Docker images to be specified at different places:

• in config files such as nextflow-src/nextflow.config

• in nextflow code files:

grep container may help locate the correct files:

Use case: Update some of the software (mimalloc) by compiling a new version

With the appropriate permissions, you can then "docker login" and "docker push" the new image.

With the appropriate permissions, you can then "docker login" and "docker push" the new image.

Update the nextflow code and/or configs to use the new image

Validate your changes in Bench:

After generating a few ICA-specific files (JSON input specs for Flow launch UI + list of inputs for next step's validation launch), the tool identifies which previous versions of the same pipeline have already been deployed (in ICA Flow, pipeline versioning is done by including the version number in the pipeline name, so that's what is checked here).

It then asks if we want to update the latest version or create a new one.

At the end, the URL of the pipeline is displayed. If you are using a terminal that supports it, Ctrl+click or middle-click can open this URL in your browser.

This launches an analysis in ICA Flow, using the same inputs as the pipeline's "test" profile.

Some of the input files will have been copied to your ICA project to allow the launch to take place. They are stored in the folder /data/project/bench-pipeline-dev/temp-data.

Introduction

The Pipeline Development Kit in Bench makes it easy to create Nextflow pipelines for ICA Flow. This kit consists of a number of development tools which are installed in /data/.software (regardless of which Bench image is selected) and provides the following features:

• Import to Bench

  • From public nf-core pipelines

  • From existing ICA Flow Nextflow pipelines

• Run in Bench

• Modify and re-run in Bench, providing fast development iterations

• Deploy to Flow

• Launch validation in Flow

• Recommended workspace size: Nf-core Nextflow pipelines typically require 4 or more cores to run.

• The pipeline development tools require

  • Conda which is automatically installed by “pipeline-dev” if conda-miniconda.installer.ica-userspace.sh is present in the image.

Pipeline development tools work best when the following items are defined:

• Nextflow profiles:

  • test profile, specifying inputs appropriate for a validation run

  • docker profile, instructing NextFlow to use Docker

ICA Flow adds one additional constraint. The output directory out is the only one automatically copied to the Project data when an ICA Flow Analysis completes. The -outdir parameter recommended by nf-core should therefore be set to--outdir=out when running as a Flow pipeline.

These are installed in /data/.software (which should be in your $PATH), the pipeline-dev script is the front-end to the other pipeline-dev-* tools.

Pipeline-dev fulfils a number of roles:

• Checks that the environment contains the required tools (conda, nextflow, etc) and offers to install them if needed.

• Checks that the fast data mounts are present (/data/mounts/project etc.) – it is useful to check regularly, as they get unmounted when a workspace is stopped and restarted.

• Redirects stdout and stderr to .pipeline-dev.log, with the history of log files kept as .pipeline-dev.log.<log date>

A pipeline-dev project relies on the following Folder structure, which is auto-generated when using the pipeline-dev import* tools.

• Project base folder

  • nextflow-src: Platform-agnostic Nextflow code, for example the github contents of an nf-core pipeline, or your usual nextflow source code.

    • main.nf

Optional parameters --local / --sge can be added to force the execution on the local workspace node, or on the workspace cluster (when available). Otherwise, the presence of a cluster is automatically detected and used.

The script then launches nextflow. The full nextflow command line is printed and launched.

In case of errors, full logs are saved as .pipeline-dev.log

Nextflow can run processes with and without Docker images. In the context of pipeline development, the pipeline-dev tools assume Docker images are used, in particular during execution with the nextflow --profile docker.

In NextFlow, Docker images can be specified at the process level

• This is done with the container "<image_name:version>" directive, which can be specified

  • in nextflow config files (preferred method when following the nf-core best practices)

  • or at the start of each process definition.

Resources such as #cpu and memory can be specified as described See or our for details about Nextflow-Docker syntax.

Bench can push/pull/create/modify Docker images, as described in .

This command does the following:

1. Generate the JSON file describing the ICA Flow user interface.

   • If ica-flow-config/inputForm.json doesn’t exist: generate it from nextflow-src/nextflow_swagger.json .

2. Generate the JSON file containing the

Output Example:

The pipeline name, id and URL are printed out, and if your environment allows, Ctrl+Click/Option+Click/Right click can open the URL in a browser.

Opening the URL of the pipeline and clicking on Start Analysis shows the generated user interface:

The ica-flow-config/launchPayload_inputFormValues.json file generated in the previous step is submitted to ICA Flow to start an analysis with the same validation inputs as “nextflow --profile test”.

Output Example:

The analysis name, id and URL are printed out, and if your environment allows, Ctrl+Click/Option+Click/Right click can open the URL in a browser.

Introduction

This tutorial shows you how to start a new pipeline from scratch

• prepare linux tool + validation inputs

• wrap in Nextflow

• wrap the pipeline in Bench

Start Bench workspace

• For this tutorial, any instance size will work, even the smallest standard-small.

• Select the single user workspace permissions (aka "Access limited to workspace owner "), which allows us to deploy pipelines.

• A small amount of disk space (10GB) will be enough.

We are going to wrap the "gzip" linux compression tool with inputs:

• 1 file

• compression level: integer between 1 and 9

Here is an example of NextFlow code that wraps the bzip2 command and publishes the final output in the “out” folder:

Save this file as nextflow-src/main.nf, and check that it works:

We now need to:

• Use Docker

• Follow some nf-core best practices to make our source+test compatible with the pipeline-dev tools

In NextFlow, Docker images can be specified at the process level

• Each process may use a different docker image

• It is highly recommended to always specify an image. If no Docker image is specified, Nextflow will report this. In ICA, a basic image will be used but with no guarantee that the necessary tools are available.

Specifying the Docker image is done with the container '<image_name:version>' directive, which can be specified

• at the start of each process definition

• or in nextflow config files (preferred when following nf-core guidelines)

For example, create nextflow-src/nextflow.config:

We can now run with nextflow's -with-docker option:

Following some nf-core to make our source+test compatible with the pipeline-dev tools:

Here is an example of “test” profile that can be added to nextflow-src/nextflow.config to define some input values appropriate for a validation run:

With this profile defined, we can now run the same test as before with this command:

A “docker” profile is also present in all nf-core pipelines. Our pipeline-dev tools will make use of it, so let’s define it:

We can now run the same test as before with this command:

We also have enough structure in place to start using the pipeline-dev command:

In order to deploy our pipeline to ICA, we need to generate the user interface input form.

This is done by using nf-core's recommended nextflow_schema.json.

For our simple example, we generate a minimal one by hand (done by using one of the nf-core pipelines as example):

In the next step, this gets converted to the ica-flow-config/inputForm.json file.

We just need to create a final file, which we had skipped until now: Our project description file, which can be created via the command pipeline-dev project-info --init:

We can now run:

After generating the ICA-Flow-specific files in the ica-flow-config folder (JSON input specs for Flow launch UI + list of inputs for next step's validation launch), the tool identifies which previous versions of the same pipeline have already been deployed (in ICA Flow, pipeline versioning is done by including the version number in the pipeline name).

It then asks if we want to update the latest version or create a new one.

Choose "3" and enter a name of your choice to avoid conflicts with all the others users following this same tutorial.

At the end, the URL of the pipeline is displayed. If you are using a terminal that supports it, Ctrl+click or middle-click can open this URL in your browser.

This launches an analysis in ICA Flow, using the same inputs as the pipeline's "test" profile.

Some of the input files will have been copied to your ICA project in order for the analysis launch to work. They are stored in the folder /data/project/bench-pipeline-dev/temp-data.