Build workflows collaboratively using reusable and shareable packages

Mar. 31, 2021

The problem

Recent advances in bioinformatics workflow development solutions, such as Nextflow, WDL and CWL mostly focus on addressing challenges in reproducibility, portability and transparency. It has been a great success. However, support for workflow code reuse and sharing is significantly lagging behind, which prevents the community from adopting the widely practised Don’t Repeat Yourself (DRY) principle.

Our solution


To address the aforementioned limitations and other issues, the International Cancer Genome Consortium Accelerating Research in Genomic Oncology (ICGC ARGO) has experimented with a modular approach over a year ago. ARGO has since established a set of best practices promoting five principles: reproducibility, portability, composability, findability and testability. Directly related to code reuse is composability, for that the code for workflow steps are to be written in self-contained and well tested packages that can later be imported into a workflow codebase. We have successfully utilized the approach in the development of five production workflows. Not only can we reuse packages across multiple workflows with code residing in different repositories, but also make it possible for anyone in the bioinformatics community to reuse them as part of their own workflows. This would ultimately allow ARGO workflows to be developed collaboratively by its members from around the globe.

First iteration

Software code reuse is nothing new, general purpose programming languages support importing code externally written as dependencies, which take the form as packages, libraries or modules depending on the language. In this post we use the term package.

In order to share packages conveniently and reliably, usually these three things are required to happen:

  1. release the package with a version so that it has a stable reference.
  2. bundle all artifacts of a package into an archive format for easy retrieval, typically a tarball is sufficient.
  3. a place to host the packages, usually this is referred as a package registry.

To support these, general purpose languages have got a wide range of tools, such as package managers, dedicated package registries etc. For example, Python’s PyPI, JavaScript’s npm etc. Unfortunately, when we started to design the code reuse approach in workflow development, none of these existed for workflow languages. The good news was that some GitHub supported features could be used to support what’s needed. More on this in just a bit.

Over to the package importing side, major workflow languages have some support for importing code blocks as shown below:

  • Nextflow (DSL2): include <file://>
  • WDL: import <file:// or http://>
  • CWL: run <file:// or http://>

Although the import is limited to only a single file, it’s sufficient to address our main use case, ie, making the code of a single step tool reusable and shareable. As ARGO uses Nextflow exclusively, the examples used in this post are based on Nextflow. However, the ideas should be applicable to other workflow languages.

Here I use an example to demonstrate what we did to create a tool package and import it into the workflow script. A tool package is called payload-gen-variant-calling, here is release of the package. This tool generates metadata JSON associated with variant calling, it’s needed in all of our three variant calling workflows. It makes perfect sense to write the code once and import it wherever needed.

As shown below in the table, we created the package release as a normal GitHub release with a special version tag pattern: <pkg_name>.<pkg_version>. The release tag serves as a stable reference to the package; the package code is a single file, no need to create a tarball; the code is directly downloadable from GitHub (which sort of serves as a package registry) as raw content with a stable URL. All three items mentioned earlier as prerequisites are fulfilled via features supported by GitHub, so the package is released and ready to be imported.

ArtifactContent URL
package code
package release
package import URL

On the importing workflow side, the import statement is straightforward, this is how it looks like in GATK Mutect2 workflow. Since Nextflow only imports from local files, the package file needs to be installed (downloaded and added to the correct location) beforehand and checked into the workflow Git repo. This is also desirable as it makes the workflow code entirely self-contained. Package installation is done by running a simple Python script.

After experimenting and prototyping for a couple of months, we were satisfied with the approach and started to use it to develop the ARGO production workflows. By the end of 2020, about 50 packages have been developed and released independently. Four workflows have been developed using these packages as building blocks.

Second iteration

As pointed out in this article, to use software packages one may not need a package manager, which was what we did to get started. However, compared to doing everything manually, a package manager can streamline and automate a long list of activities (mostly chores), ranging from template generation, automated build, testing to releasing etc, resulting in greatly improved productivity and reliability.

Equipped with the successful experience of our modular approach for workflow code packaging and a lot of inspiration by npm - JavaScript’s package manager, from the beginning of 2021 we started to develop our own WorkFlow Package Manager - WFPM.

A command line interface (CLI) tool called WFPM CLI is developed to provide assistance throughout the entire workflow development life cycle, ensuring conformation to the established ARGO best practices. It starts with auto-generated templates which include starter workflow code, code for testing, and GitHub Actions code for automated continuous integration (CI) and continuous delivery (CD). As part of the release process, package artifacts (such as scripts, configuration, test fixtures etc.) are bundled together in a tarball and made available as a release asset. This addresses the previous limitation of one single file package, consequently it makes it possible to create a multiple-step workflow as an importable subworkflow package.

Workflow developers can freely import packages as dependencies to build new workflows, which in turn are also packages that can be released and imported by others. Common software engineering practices such as CI testing, code review and release management can be seamlessly accommodated in the process. Once a new version of a package is released, it is locked down with hash codes of the Git commit and release assets (package tarball and metadata in pkg-release.json), so that the package becomes immutable and permanently available at GitHub. This provides ultimate reproducibility and guarantees the package can be reliably imported by others.

With the assistance from WFPM CLI, the fifth ARGO workflow for generating open access somatic variants from raw calls was recently developed. The new workflow is composed of four packages, including the workflow itself totaling five packages as shown below.

Package typePackage releasePackage URI


As proven in our experience, proper workflow code packaging is the essential first step towards enabling code reuse and sharing. In essence, the approach we have taken is similar to a scaled down version of npm. Most noticeably, we don’t need a centralized package registry. Released WFPM packages can be hosted at online source version control systems, such as GitHub, GitLab etc.

With features, such as template generation, automated testing, package releasing, package installation etc, offered by the WFPM CLI tool, we expect WFPM CLI to significantly lower the barriers to adopt the DRY principle avoiding code duplication, promote sharing packages and developing workflows collaboratively within the ARGO community and beyond. Similar to building something amazing, together, with simple LEGO Bricks as illustrated below.

Build something amazing, together!

Photo credit: Toronto Grand Prix Tourist Blog

More information about WFPM can be found on its documentation site at:

Junjun Zhang, Bioinformatics Team Lead
True hybrid expertise in both biology world and information technology world. Enjoy working with biologists and software developers, serving as a bridge bringing best software solutions to complex biological data management challenges. Fascinated and inspired by ingenious open source software solutions, such as Elasticsearch, wishing to come up with his own one day.