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wdl2cwl: A Python tool for converting WDL workflows to CWL |
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August 2022 |
paper.bib |
Computational workflows are used in data analysis, enabling innovation and decision-making in various fields of science. There are several competing workflow description systems, WDL (Workflow description Language) and CWL (Common Workflow Language) are two examples of popularly used systems. WDL offers a way to specify data processiing workflows with a human-readable and writeable syntax. WDL makes it straightforward to define complex analysis tasks, chain them together in workflows, and parallelize their execution [@wdl]. CWL on the other hand, is a way to describe command line tools and connect them together to create workflows. Because CWL is a specification and not a specific piece of software, tools and workflows described using CWL are portable across a variety of platforms that support the CWL standard [@cwl].
Groups using WDL or CWL can find it difficult to learn the other workflow definition language if they need to run their workflows in other platforms. It becomes even harder when the workflows contain domain specific knowledge mixed with the definitions, and when analysts and scientists need to consider future collaboration of other groups on the workflows definitions.
wdl2cwl is a Python package for converting workflow written in WDL to workflows in CWL.
The wdl2cwl package uses the miniwdl package [@miniwdl], an open source, Python package,
developer toolkit and local runner for running WDL files. The wdl2cwl package was
designed to provide a class-based and developer-friendly interface for extracting
the various sections of a WDL file, dynamically create the various objects
needed to create a valid CWL file ( this is done using the cwl-utils package [@cwl_utils],
an open source, Python package for parsing CWL files) and performing error checking
for valid imports, input declarations, expressions and other useful operations on CWL files.
wdl2cwl was designed to be used by both CWL/WDL professionals and researchers, as well
as students of bioinformatics and data analytics. It can help CWL analysts to avoid
platform dependency issues (like trying to reproduce a WDL workflow on a platform that
only allows CWL). It can help analysts by eliminating the difficulty of learning a new
workflow language before they an use a WDL worflow. It can be used to create a more
widely available workflow that can be used by analysts of either languages.
We acknowledge the work by the developers of miniwdl, CWL and WDL.