Documentation

This tutorial is a continuation of Getting Started With Docker. Please complete that tutorial prior to doing this one.

Getting Started with Nextflow

Tutorial Goals

  • Learn about Nextflow
  • Create a basic Nextflow Workflow which uses a Docker image
  • Run the Workflow locally
  • Describe a sample parameterization of the Workflow
  • Push the Workflow onto GitHub

Describe Your Workflow with Nextflow

Note: that because there is no concept of Nextflow as a tool, we will register BAMStats as a workflow. To learn about what a workflow is, see Getting Started With Dockstore Workflows. Once you are done that page then come back here.

Nextflow is a bit different from CWL and WDL. Instead of having one type of descriptor file, dockstore uses two different kinds of files: A config file, nextflow.config and a descriptor file, often, main.nf.

Configuration File

The nextflow.config file is used to store configuration information, such as Docker image and memory required to run. Below is the config file for our BAMStats workflow. The config file groups together similar information and settings within scopes, such as manifest and docker.

manifest {
    description = 'Generate some stats on a BAM file'
    author = 'Andrew Duncan'
}

params {
	bam_input = 'rna.SRR948778.bam'
	mem_gb = '4'
}

process.container = 'quay.io/collaboratory/dockstore-tool-bamstats:1.25-6_1.0'
docker {
    enabled = true
    docker.runOptions = '-u $(id -u):$(id -g)'
}

The manifest scope includes high level information about the tool. In this case we have a description of the tool and the author name. Note that we use the author and description fields to populate metadata on Dockstore.

manifest {
    description = 'Generate some stats on a BAM file'
    author = 'Andrew Duncan'
}

The next section is the params section. Here we define parameters required by our workflow, including the memory in GB needed and the path to the input BAM file. Note that we can override these parameters directly in the main.nf file, or on the command line using the format --parameter_name parameter_value.

params {
	bam_input = 'rna.SRR948778.bam'
	mem_gb = '4'
}

We next define the Docker container used in the process scope. Note that we do this differently than the other fields. This is another valid syntax for setting values within a scope.

process.container = 'quay.io/collaboratory/dockstore-tool-bamstats:1.25-6_1.0'

The last scope of the file is the docker scope. This scope does not define the container that we use, instead it defined other Docker related configurations. Note that we have to specify that we want Docker enabled. Just setting the process.container is not enough!

docker {
    enabled = true
    docker.runOptions = '-u $(id -u):$(id -g)'
}

There are many more scopes and options within the scopes. To learn more see the official documentation.

Descriptor File

The main.nf is similar to the Dockstore.cwl and Dockstore.wdl we used for CWL and WDL respectively. It includes a description of a tool, including the inputs, outputs, and command run. Below is the main.nf file for BAMStats.

#!/usr/bin/env nextflow

bamFile = file(params.bam_input)

process bamstats {
    input:
    file bam_input from bamFile
    val mem_gb from params.mem_gb

    output:
    file 'bamstats_report.zip'

    """
    bash /usr/local/bin/bamstats $mem_gb $bam_input
    """
}

First we tell Nextflow that the bam_input parameter is a file. We do this outside of the process.

bamFile = file(params.bam_input)

Next we will look at the process scope. It is made of the input, output and command sections.

In the input section we define the input bam_input as a file from the object bamFile. We also define the input mem_gb to be the value of the parameter mem_gb.

input:
file bam_input from bamFile
val mem_gb from params.mem_gb

We then define the output of the process as the file bamstats_report.zip. Note that we do not do anything with this output. If we were to have a multi process workflow, where another process depended on this output file, we would have to do more to setup that connection. However, this is not within the scope of this tutorial.

output:
file 'bamstats_report.zip'

The final section is the command section. This section defines what command is run by the process. We run the bamstats command line tool and pass it the mem_gb and bam_input parameters that we define in the input section of the process.

"""
bash /usr/local/bin/bamstats $mem_gb $bam_input
"""

Testing Locally

Note: As of 1.5.0 you cannot run Nextflow workflows from the CLI. Instead you must run them directly from the Nextflow command line tool. The only drawback of this is that it does not support Dockstore file provisioning. It does have its own form of file provisioning, but we will not go into that here.

So at this point, you’ve created a Docker-based tool and have described how to call that tool using Nextflow. Let’s test running the BAMStats using the Nextflow command line and descriptor rather than just directly calling it via Docker. This will test that the Nextflow correctly describes how to run your tool.

First thing I’ll do is create a completely local dataset and update the configuration file:

$> wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/NA12878/alignment/NA12878.chrom20.ILLUMINA.bwa.CEU.low_coverage.20121211.bam
# alternative location if the above URL doesn't work
$> wget https://s3.amazonaws.com/oconnor-test-bucket/sample-data/NA12878.chrom20.ILLUMINA.bwa.CEU.low_coverage.20121211.bam

This downloads to my current directory. I could choose another location, it really doesn’t matter. We then must update the nextflow.config with the new file.

manifest {
    description = 'Generate some stats on a BAM file'
    author = 'Andrew Duncan'
}

params {
	bam_input = 'NA12878.chrom20.ILLUMINA.bwa.CEU.low_coverage.20121211.bam'
	mem_gb = '4'
}

process.container = 'quay.io/collaboratory/dockstore-tool-bamstats:1.25-6_1.0'
docker {
    enabled = true
    docker.runOptions = '-u $(id -u):$(id -g)'
}

Now we just need the Nextflow CLI to run the tool. Assuming you have Java 8+ installed, run the following command to install Nextflow.

curl -s https://get.nextflow.io | bash 

We recommend that you add this command to your PATH so that you can run it from anywhere.

We can now run the tool with the following command

$> ./nextflow run main.nf
N E X T F L O W  ~  version 0.31.0
Launching `main.nf` [voluminous_dubinsky] - revision: 5a7f382891
[warm up] executor > local
[c4/6f9ebf] Submitted process > bamstats

The logs for the tool run will be visible in the .nextflow.log file in the current directory. It specifies the working dir of the tool and where we can find the output. In our case the output was stored at work/c4/6f9ebfe4e2c30d9685cf3cfbbf7643/bamstats_report.zip.

Adding a Test Parameter File

Nextflow does not have the concept of a test parameter file. Instead you either pass parameters on the command line when you run it, or you can specify parameters in the nextflow.config or main.nf.

Releasing on GitHub

At this point, we’ve successfully created our tool in Docker, tested it, written a workflow language descriptor that describes how to run it, and tested running this via the Dockstore command line. All of this work has been done locally; so if we encounter problems along the way, it is fast to perform debug cycles. At this point, we’re confident that the tool is bug free and ready to share with others. It’s time to release 1.25-6_1.1

Releasing will tag your GitHub repository with a version tag so you can always get back to this particular release. I’m going to use the tag 1.25-6_1.1 which I’ll need to update in my Docker image tag and also my CWL/WDL/Nextflow file. Note that if you’re following the tutorial using a forked version of the bamstats repo, your organization name should be different. GitHub makes it very easy to release:

Release

I click on “releases” in my forked version of the GitHub project page and then follow the directions to create a new release. Simple as that!

Tip: HubFlow is an excellent way to manage the lifecycle of releases on GitHub. Take a look!

Building on Quay.io

Now that you’ve perfected the Dockerfile, built the image on your local host, tested running the Docker container and tool packaged inside, and have released this version on GitHub, it’s time to push the image to a place where others can use it. For this you can use Docker Hub or GitLab but we prefer Quay.io since it integrates really nicely with Dockstore.

You can manually docker push the image you have already built but the most reliable and transparent thing you can do is link your GitHub repository (and the Dockerfile contained within) to Quay.io. This will cause Quay to automatically build the Docker image every time there is a change.

Log onto Quay.io now and setup a new repository (click the “+” icon).

New Quay Repo

For your sanity, you should match the name to what you were using previously. So in this case, it’s my username then the same repo name as in GitHub denis-yuen/dockstore-tool-bamstats. Also, Dockstore will only work with Public repositories currently. Notice I’m selecting “Link to a GitHub Repository Push.” This is because we want Quay to automatically build our Docker image every time we update the repository on GitHub. Very slick!

Build Trigger

Click through to select the organization and repo that will act as the source for your image. Here I select the GitHub repo for denis-yuen/dockstore-tool-bamstats but this should be the username or organization you used in your tutorial run-through.

It will then ask if there are particular branches you want to build; I typically just let it build everything.

So every time you do a commit to your GitHub repo, Quay automatically builds and tags a Docker image. If this is overkill for you, consider setting up a regular expression to trigger builds at this step.

Build Trigger

It will also ask you where your Dockerfile is located and where your build context is (normally the root).

At this point, you can confirm your settings and “Create Trigger” followed by “Run Trigger Now” to actually perform the initial build of the Docker images. You’ll need to click on the little gear icon next to your build trigger to accomplish this.

Manual Trigger

Manually trigger it with a version name of 1.25-6_1.1 for this tutorial. Normally, I let the build trigger build a new tag for each new release on GitHub. “latest” on Quay.io is built any time I check-in on any branch. This can be useful for development but is discouraged in favour of a tagged version number for formal releases of your tool.

In my example, I should see a 1.25-6_1.1 listed for this Quay.io Docker repository:

Build Tags

And I do, so this Docker image has been built successfully by Quay and is ready for sharing with the community.

Next Steps

Follow the next tutorial to create an account on Dockstore and link third party services.

See Also