Building Containerized Workflows Using the BioDepot-Workflow-Builder

Abstract

We present the BioDepot-workflow-builder (Bwb), a software tool that allows users to create and execute reproducible bioinformatics workflows using a drag-and-drop interface. Graphical widgets represent Docker containers executing a modular task. Widgets are linked graphically to build bioinformatics workflows that can be reproducibly deployed across different local and cloud platforms. Each widget contains a form-based user interface to facilitate parameter entry and a console to display intermediate results. Bwb provides tools for rapid customization of widgets, containers, and workflows. Saved workflows can be shared using Bwb's native format or exported as shell scripts.

Keywords: Docker; RNA sequencing; bioinformatics workflows; reproducibility of research; software development.

Figure 1.

Bwb interface: The basic elements of the Bwb interface are shown. When the Bwb application is launched and the user connects to the application using a browser, a window consisting of a canvas and a Tool Dock appears. Modules (widgets) are dragged from the Tool Dock and dropped onto the canvas and linked together to form a workflow, in this case, the kallisto-sleuth (Pimentel et al., 2017) workflow. Right-clicking on a module brings up a set of forms that allow the user to edit the properties of the widget such as the parameters to be queried and the command(s) to be executed. Double clicking allows the user to enter parameter values and execute the workflow.

Figure 2.

Kallisto-sleuth workflow: This workflow illustrates how Bwb is used to execute the kallisto-sleuth workflow. The leftmost widgets in the workflow download support files and set up the indices, whereas the later downstream widgets are responsible for pseudo-alignment and determination of differentially expressed genes (DEGs). When a widget completes its task, it sends signals to downstream connected widgets to trigger their execution. Execution can be started at any widget. For example, after downloading support files and generating indices once, the user can start at the pipeline at the kallisto pseudo alignment if he or she wishes to analyze a new set of reads. In this workflow, the final widget displays a Firefox window inside Bwb to allow interaction with the Jupyter notebook containing the R script that calls Sleuth. The user can re-run the code cells, adjust parameters and visualize results without leaving the Bwb sandbox. When the user is satisfied, the notebook can be saved to the host computer.