At its core, a workflow is a systematic sequence of processes, operations, or steps that are carried out in a particular order to achieve a specific goal. This goal might range from automating business processes to executing bioinformatics analyses. In the context of Workbench, a workflow is a defined set of tasks designed to process, analyze, and manage data in a structured manner.
In Workbench, a single workflow comprises metadata such as a name, identifier, description, and a set of versions. Each version has a name, identifier and a description and represents an immutable snapshot of descriptor files (or the "instructions") that a user can run through Workbench in order to analyze their data.
The combination of the workflow ID and the version ID constitutes a unique identifier within your Workbench account, allowing workflows to be addressable. Workflow executions are linked to a workflow and its version, allowing you to keep track of which version was used for a specific run.
You can access workflows by navigating to the Workflow page from the workbench user interface.
Breaking Down the Workflow
1. Chain of Commands
Workflows are essentially a series of commands, orchestrated to be executed in a predefined sequence. The ordered execution ensures that the data is processed and analyzed methodically, often with the output of one step serving as the input for the subsequent step.
2. Managing Computational Complexity
Handling complex workflows, especially in bioinformatics, is not a trivial task. It involves coordinating numerous steps, each having distinct computational demands. Some steps might require substantial computational resources, while others may necessitate specific software or environment settings.
3. Role of Workflow Languages
To bring structure and clarity to these intricate workflows, we employ workflow languages. These languages offer a formalized approach to:
Task Definition: Workflow languages allow the definition of "tasks". Each task can be associated with particular computational settings, ensuring that it runs in the optimal environment for its specific requirements.
Docker Containers: A notable feature of modern workflow languages is the ability to associate tasks with Docker containers. This encapsulation ensures that the task has all the necessary software and dependencies, providing a consistent and reproducible environment for execution.
Chaining Tasks: One of the main strengths of workflow languages is the ability to seamlessly chain tasks together. By directing the output of one task to serve as the input for the next, workflow languages enable smooth and efficient data processing pipelines.
Workflows are fundamental to facilitating structured and systematic data processing and analysis. By leveraging the power of workflow languages and tools like Workbench, professionals can design, execute, and manage intricate workflows with ease and precision. This ensures not only efficiency and scalability, but also reproducibility, which is crucial for scientific and business applications.