Workflows

Overview

A workflow is a systematic sequence of processes designed to achieve a specific goal, from automating business processes to executing bioinformatics analyses. In Workbench, workflows provide structured methods for processing and analyzing data.

Workflow Structure

Each workflow in Workbench consists of metadata, including its name, identifier, and description, along with a set of versions. Each version represents an immutable snapshot of descriptor files - the "instructions" that Workbench uses to analyze data.

The combination of workflow ID and version ID creates a unique identifier within your Workbench account. This allows workflows to be addressable and helps track which version was used for specific executions.

Components and Management

Command Chains

Workflows orchestrate commands in a predefined sequence. This ordered execution ensures methodical data processing, with outputs from one step often serving as inputs for subsequent steps.

Computational Management

Modern workflows coordinate multiple steps with varying requirements:

• Different computational resources

• Specific software environments

• Distinct processing needs

Workflow Languages

Workflow languages bring structure and clarity to complex processes through:

Task Definition

Each task specifies its computational requirements, ensuring optimal execution environments and resource allocation.

Container Integration

Docker containers provide:

• Consistent software environments

• Required dependencies

• Reproducible execution conditions

Task Chaining

Languages enable seamless connections between tasks, creating efficient data processing pipelines where outputs flow automatically to subsequent steps.

Conclusion

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.