Overview

Teaching: 0 min
Exercises: 0 min

Questions

• What are computational workflows, and why are they useful?

• What is Common Workflow Language?

• How are CWL workflows written?

• How do CWL workflows compare to shell workflows?

• What are the advantages of using CWL workflows?

Objectives

• Recognise the advantages of using a computational workflow

• Understand why you might use CWL instead of a shell script

Computational Workflows

Computational workflows are widely used for data analysis, enabling rapid innovation and decision making. Workflow thinking is a form of “conceptualizing processes as recipes and protocols, structured as workflow or dataflow graphs with computational steps, and subsequently developing tools and approaches for formalizing, analyzing and communicating these process descriptions”¹. Distinct from general business workflows, computational workflows have their primary focus on data and dataflows. The workflow managers which are designed for such computational workflows should aid their users with this focus. The helpful properties that an ideal computational workflow would possess, to make this possible, are summarised below.

Handy Properties of Computational Workflows²

	Composition & Abstraction Using the best code written by 3rd parties Handle heterogeneity Shield Complexity & incompatibility Shareable reusable, re-mixable methods
	Sharing & Adaptability Shared method, publishable know-how BYOD / parameters Different implementations Changes in execution infrastructure
	Automation Repetitive reproducible pipelines Simulation sweeps Manage data and control flow Optimised monitoring & recovery Automated deployment
	Reporting & Accreditation Provenance logging & data lineage Auto-documentation Result comparison
	Scalability & Infrastructure Access Accessing infrastructures, datasets and tools Optimised computation and data handling Parallelisation Secure sensitive data access & management Interoperating datasets & permission handling
	Portability Dependency handling Containerisation & packaging Moving between on premise & cloud

Computational workflows, and their managers, should abstracting away complexities of the underlying computational environment. Doing so enables the use of heterogeneous tools provided by multiple third parties, as well as the scalability and portability of the workflows, so users can make use of the resources available to them. The workflow manager should make easy the automation, monitoring and provenance tracking of the dataflow. In addition the computational workflow should be in a shareable format, published with open access. These features all will contribute to any data produced using the computational workflow being FAIR (Findable, Accessible, Interoperable, and Reusable)³.

However as the rise in popularity of workflows has been matched by a rise in the number of disparate workflow managers that are available, each with their own syntax or methods for describing the tools and workflows, reducing portability and interoperability of these workflows. The Common Workflow Language (CWL) standard has been developed to address these problems, and to serve the general computational workflow needs described above.

Common Workflow Language

CWL is a free and open standard for describing command-line tool based workflows³. These standards provide a common, but reduced, set of abstractions that are both used in practice and implemented in many popular workflow managers. The CWL language is declarative, enabling computational workflows to be constructed from diverse software tools, executing each through their command-line interface.

Previously researchers might write shell scripts to link together these command-line tools. Although these scripts might provide a direct means of accessing the tools, writing and maintaining them requires specific knowledge of the system that they will be used on. Shell scripts are not easily portable, and so researchers can easily end up spending more time maintaining the scripts than carrying out their research. The aim of CWL is to reduce that barrier of usage of these tools to researchers.

CWL workflows are written in a subset of YAML, with a syntax that does not restrict the amount of detail provided for a tool or workflow. The execution model is explicit, all required elements of a tool’s runtime environment must be specified by the CWL tool-description author. On top of these basic requirements they can also add hints or requirements to the tool-description, helping to guide users (and workflow engines) on what resources are needed for a tool.

The CWL standards explicitly support the use of software container technologies, helping ensure that the execution of tools is reproducible. Data locations are explicitly defined, and working directories kept separate for each tool invocation. These standards ensure the portability of tools and workflows, allowing the same workflows to be run on your local machine, or in a HPC or cloud environment, with minimal changes required.

RNA sequencing example

In this tutorial a bio-informatics RNA-sequencing analysis is used as an example. However, there is no specific knowledge needed for this tutorial. RNA-sequencing is a technique which examines the quantity and sequences of RNA in a sample using next-generation sequencing. The RNA reads are analyzed to measure the relative numbers of different RNA molecules in the sample. This analysis is differential gene expression.

The process looks like this:

During this tutorial, only the middle analytical steps will be performed. The adapter trimming is skipped. These steps will be done:

• Quality control (FASTQC)
• Alignment (mapping)
• Counting reads associated with genes

The different tools necessary for this analysis are already available. In this tutorial a workflow will be set up to connect these tools and generate the desired output files.

Key Points

• CWL is a standard for describing workflows based on command-line tools

• CWL workflows are written in a subset of YAML

• A CWL workflow is more portable than a shell script

• CWL supports software containers, supporting reproducibility on different machines