Snakemaker: Seamlessly transforming ad-hoc analyses into sustainable Snakemake workflows with generative AI
This addresses a critical gap in computational reproducibility for bioinformatics researchers, though it is incremental as it builds on existing Snakemake workflows.
The paper tackles the challenge of reproducibility and sustainability in bioinformatics by introducing Snakemaker, a tool that uses generative AI to convert unstructured code and IPython Notebooks into modular Snakemake workflows, enabling researchers to build sustainable data analysis pipelines.
Reproducibility and sustainability present significant challenges in bioinformatics software development, where rapidly evolving tools and complex workflows often result in short-lived or difficult-to-adapt pipelines. This paper introduces Snakemaker, a tool that leverages generative AI to facilitate researchers build sustainable data analysis pipelines by converting unstructured code into well-defined Snakemake workflows. Snakemaker non-invasively tracks the work performed in the terminal by the researcher, analyzes execution patterns, and generates Snakemake workflows that can be integrated into existing pipelines. Snakemaker also supports the transformation of monolithic Ipython Notebooks into modular Snakemake pipelines, resolving the global state of the notebook into discrete, file-based interactions between rules. An integrated chat assistant provides users with fine-grained control through natural language instructions. Snakemaker generates high-quality Snakemake workflows by adhering to the best practices, including Conda environment tracking, generic rule generation and loop unrolling. By lowering the barrier between prototype and production-quality code, Snakemaker addresses a critical gap in computational reproducibility for bioinformatics research.