Converting Snakemake workflows to Nextflow with nf-core modules using a fine-tuned LLM
A useful application of AI in bioinformatics is the automatic transformation of Snakemake workflows into Nextflow, leveraging nf-core modules and subworkflows where available. As bioinformatics pipelines become increasingly complex, the ability to seamlessly transition between workflow languages is essential, particularly in collaborative environments where diverse teams may prefer different tools. A fine-tuned LLaMA model or an OpenAI custom GPT could be trained to interpret the structure and logic of a Snakemake workflow on GitHub and convert the workflow to Nextflow. This model could identify where existing nf-core modules and subworkflows can replace custom scripts, simplifying the process and ensuring adherence to best practices in pipeline design. Such a system would reduce the burden of manually translating workflows, enabling researchers to take advantage of Nextflow’s scalability and reproducibility without significant overhead.
To implement this, one could fine-tune an existing language model on a dataset of paired Snakemake and Nextflow workflows, with special emphasis on nf-core standards. The model would need to be able to understand both the structure of workflow definitions and the underlying bioinformatics tools being invoked. Additionally, the model should query nf-core’s module registry to determine where standard subworkflows can be integrated, further enhancing workflow consistency and reducing redundancy.
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