BONUS: Reproducible scientific workflows

Overview

Teaching: 10 min
Exercises: 0 min

Questions

Objectives

• Get an idea of the interplay between containers and workflow engines

Scientific workflow engines

Let’s see how Singularity containers can be used in conjunction with a popular workflow engine.

Scientific workflow engines are particularly useful for data-intensive domains including (and not restricted to) bioinformatics and radioastronomy, where data analysis and processing is made up of a number of tasks to be repeatedly executed across large datasets. Some of the most popular ones, including Nextflow and Snakemake, provide interfaces to container engines. The combination of container and workflow engines can be very effective in enforcing reproducible, portable, scalable science.

Now, let’s try and use Singularity and Nextflow to run a demo RNA sequencing pipeline based on RNAseq-NF.

Run a workflow using Singularity and Nextflow

Let’s cd into the appropriate directory:

cd /data/bio-intro-containers/exercises/nextflow

For convenience, the content of the pipeline RNAseq-NF is already made available in this directory. There are two critical files in here, namely main.nf, that contains the translation of the scientific pipeline in the Nextflow language, and nextflow.config, that contains several profiles for running with different software/hardware setups. Here we are going to use the profile called singularity.

It’s time to launch the pipeline with Nextflow:

nextflow run main.nf -profile singularity

We’ll get some information on the pipeline, along with the notice that the appropriate container is being downloaded:

N E X T F L O W  ~  version 19.10.0
Pulling marcodelapierre/rnaseq-nf ...
 downloaded from https://github.com/marcodelapierre/rnaseq-nf.git
Launching `marcodelapierre/rnaseq-nf` [hopeful_almeida] - revision: 91dd162c00 [master]
 R N A S E Q - N F   P I P E L I N E
 ===================================
 transcriptome: /data/work/.nextflow/assets/marcodelapierre/rnaseq-nf/data/ggal/ggal_1_48850000_49020000.Ggal71.500bpflank.fa
 reads        : /data/work/.nextflow/assets/marcodelapierre/rnaseq-nf/data/ggal/ggal_gut_{1,2}.fq
 outdir       : results

WARN: Singularity cache directory has not been defined -- Remote image will be stored in the path: /data/work/singularity-test/nxf/work/singularity
Pulling Singularity image docker://nextflow/rnaseq-nf:latest [cache /data/work/singularity-test/nxf/work/singularity/nextflow-rnaseq-nf-latest.img]

It will take a bunch of minutes to download the container image, then the pipeline will run:

[9e/a8a999] Submitted process > fastqc (FASTQC on ggal_gut)
[6a/4ec5ee] Submitted process > index (ggal_1_48850000_49020000)
[91/109c65] Submitted process > quant (ggal_gut)
[ab/081287] Submitted process > multiqc

Done! Open the following report in your browser --> results/multiqc_report.html

The final output of this pipeline is an HTML report of a quality control task, which you might eventually want to download and open up in your browser.

However, the key question here is: how could the sole flag -profile singularity trigger the containerised execution? This is the relevant snippet from the nextflow.config file:

  singularity {
    process.container = 'nextflow/rnaseq-nf:latest'
    singularity.enabled = true
    singularity.autoMounts = true
  }

The image name is specified using the process.container keyword. Also, singularity.autoMounts is required to have the directory paths with the input files automatically bind mounted in the container. Finally, singularity.enabled triggers the use of Singularity.

Based on this configuration file, Nextflow is able to handle all of the relevant Singularity commands by itself, i.e. pull and exec with the appropriate flags, such as -B for bind mounting host directories. In this case, as a user you don’t need to know in detail the Singularity syntax, but just the name of the container!

More information on configuring Nextflow to run Singularity containers can be found at Singularity containers.

Key Points

• Some workflow engines offer transparent APIs for running containerised applications

• If you need to run data analysis pipelines, the combination of containers and workflow engines can really make your life easier!