pipeio-flow-create

Scaffolding a new pipeline flow involves surveying existing patterns, creating a flow directory with defaults, writing a config.yml and Snakefile aligned to conventions, and setting up datalad subdatasets for reproducible data management.

You can set up datalad subdatasets within a pipeline flow by creating and registering them during the scaffolding process, ensuring your data inputs and outputs are version-controlled and discoverable across the pipeline.

The best way to structure a config.yml for a new pipeline is to follow established pipeio conventions, using scaffolding templates that provide required defaults and ensure the configuration aligns with the Snakefile for discoverability.

Yes, you can use datalad with Snakemake by creating and registering datalad subdatasets within your pipeline flow, which provides version-controlled data management and ensures end-to-end reproducibility for your workflow.

You verify a newly created Snakemake pipeline by running a dry-run verification immediately after scaffolding the project and writing the Snakefile, ensuring all configurations and scripts are correctly aligned before execution.

A limitation is that scaffolding relies on surveying existing patterns, meaning a lack of established flows to reference may hinder generating accurate defaults, and manual adjustments to the Snakefile or scripts might be needed for non-standard configurations.