How to set up scalable Galaxy workflows?

Introduction to Galaxy Workflows

• The Galaxy platform is an open-source tool for data-intensive biomedical research. Workflows in Galaxy automate complex computational analyses, making them reproducible and scalable.

 

• Setting up a scalable Galaxy workflow involves understanding how to configure and optimize your analyses for efficiency and reproducibility.

Set Up a Galaxy Account

• Register for an account on a public Galaxy server like usegalaxy.org, or set up a local Galaxy instance if working with private data.

 

• Familiarize yourself with the Galaxy user interface, including data libraries, histories, and tool panels.

Select and Upload Data

• Identify the datasets you will need for your analysis. Common data types include sequence files, tabular data, and reference genomes.

 

• Upload your datasets into the Galaxy history using the upload tool. For larger datasets, consider using FTP or external links to handle file transfers efficiently.

Choose and Install Tools

• Use the Galaxy Toolshed to search for and install tools necessary for your workflow. Ensure that selected tools are compatible with your data types.

 

• Regularly update installed tools to benefit from optimizations and new features that enhance workflow performance.

Create a Workflow

• Navigate to the Workflow Editor under the Galaxy interface. Drag and drop tools from the tool panel to start building your workflow.

 

• Link tools by connecting output datasets of one tool to the input of another, forming a complete analysis pipeline.

Parameterize the Workflow

• Configure each tool within the workflow for optimal performance. This step often involves selecting appropriate parameters based on input data characteristics.

 

• Consider adding step parameters to expose options that can be modified without changing the core workflow structure, increasing flexibility and reusability.

Test the Workflow

• Run the workflow with test datasets to identify potential errors and performance bottlenecks. Review generated history for any failed or stalled jobs.

 

• Iteratively modify the workflow and its parameters to ensure accuracy and efficiency before full-scale deployment.

Optimize and Scale

• Investigate resources such as CPU, memory, and storage to ensure the infrastructure can handle increased data loads and complexity.

 

• Utilize Galaxy's job configuration settings to manage distributed execution and parallel processing, significantly enhancing scalability.

Document and Share the Workflow

• Annotate each tool and step in the workflow to ensure clear understanding for collaborators or future usage.

 

• Export and share the Workflow, making it accessible to colleagues or the community via Galaxy share functionalities or external repositories like GitHub.