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Using Nextflow#

What is Nextflow and how does it fit into the CLIMB-BIG-DATA platform?#

In their words, "Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages."

We've made Nextflow a first class citizen in CLIMB-BIG-DATA. You'll find an up-to-date stable version pre-installed in your notebook server and, more importantly, pre-configured to take advantage of our scalable Kubernetes infrastructure.

How can I start using Nextflow?#

Create a notebook server and start a terminal session. Now type:

jovyan:~$ nextflow -v
nextflow version

Your version may be newer than the above, but the point is that Nextflow is pre-installed and you should usually use this binary rather than downloading another. You don't need to follow the installation instructions in the Nextflow docs.

Tutorial using nf-core#

nf-core is a community-curated set of analysis pipelines that use Nextflow. We'll try running nf-core/rnaseq as an example, to demonstrate some features of how Nextflow is configured to work on CLIMB.

We'll be using -profile test, which includes links to appropriate data. As a result, we'll only need to specify --outdir for now.

jovyan:~$ nextflow run nf-core/rnaseq -profile test --outdir nfout
[c5/3af707] process > NFCORE_RNASEQ:RNASEQ:QUANTIFY_STAR_SALMON:SALMON_QUANT (RAP1_UNINDUCED_REP1)                 [ 50%] 1 of 2
[-        ] process > NFCORE_RNASEQ:RNASEQ:QUANTIFY_STAR_SALMON:SALMON_TX2GENE                                     -
[-        ] process > NFCORE_RNASEQ:RNASEQ:QUANTIFY_STAR_SALMON:SALMON_TXIMPORT                                    -
[-        ] process > NFCORE_RNASEQ:RNASEQ:QUANTIFY_STAR_SALMON:SALMON_SE_GENE                                     -
[-        ] process > NFCORE_RNASEQ:RNASEQ:QUANTIFY_STAR_SALMON:SALMON_SE_GENE_SCALED                              -
[-        ] process > NFCORE_RNASEQ:RNASEQ:QUANTIFY_STAR_SALMON:SALMON_SE_TRANSCRIPT                               -
[-        ] process > NFCORE_RNASEQ:RNASEQ:DESEQ2_QC_STAR_SALMON                                                   -

What is going on?#

The pipeline is executing interdependent process in parallel, using the Kubernetes executor. That is to say, rather than running inside your notebook container directly, new Kubernetes pods are being created on the fly and spinning up containers for each process.

Open another terminal tab alongside, while the workflow is still executing, and try:

jovyan:~$ kubectl get pods
NAME                                         READY   STATUS              RESTARTS   AGE
jupyter-demouser-2eclimb-2dbig-2ddata-2dd   1/1     Running             0          12m
nf-0e32425fc6d3dd42c9a3cbb8dd3ccc8c          0/1     Pending             0          4s
nf-6171723bfd88a03f1417e2aead99f180          0/1     Terminating         0          12s
nf-0e69c114a366b717ad115431277c01d7          1/1     Running             0          9s
nf-31f1f1f534f51863f2e19320ca7447e0          0/1     ContainerCreating   0          4s
nf-75dc1d907794e300ff2117a49be85c63          0/1     Pending             0          4s
nf-8491621dd73c44811c49bee448771ae5          0/1     Completed           0          13s
nf-9aad711fc9476c27e510b9402e3089d5          0/1     ContainerCreating   0          3s
nf-12656c698dd83e7dc2a31e3c88818227          0/1     ContainerCreating   0          4s
nf-a57472aaef0073c9e77a0a7e6001a849          0/1     ContainerCreating   0          3s
nf-f8d94c52e9120b7b8ba117d530f77c3a          0/1     Completed

kubectl is the Kubernetes command line tool. It's also pre-installed in the CLIMB notebook environment, and pre-configured with credentials that map to a ServiceUser for your team.

This service user has certain privileges within your namespace, or in other words an isolated part of our cluster created specifically for your team. The command kubectl get pods above is returning a list of pods currently running in your namespace. You'll see your notebook server (jupyter-demouser-2eclimb-2dbig-2ddata-2dd) and a number of Nextflow pods that are running workflow process containers. These will be in various states as they execute and then disappear.

Once the workflow has finished, run the above command again:

jovyan:~$ kubectl get pods
NAME                                         READY   STATUS    RESTARTS   AGE
jupyter-demouser-2eclimb-2dbig-2ddata-2dd   1/1     Running   0          17m

... and you're back to just your notebook server (you may also see those belonging to others in your Bryn team).

Where did my output data go?#

Once the workflow completes you'll see something like:

-[nf-core/rnaseq] Pipeline completed successfully with skipped sampl(es)- -[nf-core/rnaseq] Please check MultiQC report: 1/5 samples failed strandedness check.-
Completed at: 16-Apr-2023 13:26:51
Duration : 5m 50s
CPU hours : 0.4
Succeeded : 196

We specified nfout as out outdir and you'll see the directory in the file browser on the left hand side of the JupyterLab interface. Take a look in nfout/pipeline_info/ inside your file browser. Try double clicking on the various HTML, YAML and CSV files here and you'll see that they open in new tabs for immediate reading.

One thing to note here, when opening HTML files such as execution_report_[date].html, JavaScript is disabled in the tab by default. Right click the file and select Open in New Browser Tab from the context window to see the full report.

Where are Nextflow assets and temporary/intermediate (workdir) outputs stored?#

By default, the CLIMB Nextflow config sets Nextflow 'home' to /shared/team/nxf_work/$JUPYTERHUB_USER. You'll see a number of subdirectories exist at that location, including assets, which will now contain the rnaseq workflow we just used, and work: where the intermediate outputs are located.

jovyan:~$ ls /shared/team/nxf_work/demouser.climb-big-data-d/
assets  capsule  framework  plugins  secrets  tmp  work

CLIMB Nextflow config defaults#

We have tried to make it as easy as possible to use Nextflow on CLIMB, and to make full use of available resources via our Kubernetes infrastructure. Out-of-the box, we set a number of configuration defaults.

  • Nextflow home is set to /shared/team/nxf_work/$JUPYTERHUB_USER
  • WorkDir is set to /shared/team/nxf_work/$JUPYTERHUB_USER/work
  • Executor is set to k8s (plus some supporting config)
  • /shared/team and /shared/public (read only) are mounted as PVCs to all Nextflow pods
  • A K8s ServiceUser is pre-mounted (no credentials setup required)
  • S3 bucket path-style access is enabled, with set as the endpoint
  • S3 keys have also been injected from Bryn

How to run NextFlow locally with Mamba.#

When working with limited computing resources or tasks that don't require additional cores, launching new ones might not be the most efficient choice. In such cases, NextFlow offers a solution by allowing you to utilize the cores already assigned to your notebook for execution.

To achieve this, NextFlow provides the option to specify the Mamba profile and set the process executor to local. By doing so, you can optimize resource usage and minimize unnecessary overhead.

To run NextFlow with Mamba for nf-core pipelines, follow these steps:

nextflow run <> -profile mamba -process.executor=local
Replace `<>` with the filename of the nf-core Pipeline you want to execute. The options `-profile` and `-process.executor` should be specified to ensure proper configuration.
If Mamba encounters issues with older Pipelines, you can use the -profile conda option. However, note that this may be slower:

nextflow run <> -profile conda -process.executor=local