用AI把零散分析自动转为可持续的生物信息学工作流
Snakemaker: Seamlessly transforming ad-hoc analyses into sustainable Snakemake workflows with generative AI
- 通过跟踪终端操作，将无结构代码转化为规范Snakemake流程
- 支持笔记本一键转模块化工作流，解决全局状态问题
- 内置聊天助手，自然语言即可控制流程生成
生物信息学软件开发面临可复现性和可持续性挑战，快速迭代的工具与复杂工作流常导致流程短命或难以适配。本文提出Snakemaker，利用生成式AI将非结构化代码自动转换为规范的Snakemake工作流。该工具非侵入式地追踪研究者在终端的操作，分析执行模式，生成可集成到现有流程中的工作流。它还能将单体的Jupyter Notebook转化为模块化的Snakemake流程，将笔记本的全局状态拆解为规则间的文件级交互。内置聊天助手支持用户通过自然语言指令实现细粒度控制。生成的工作流遵循最佳实践，包括Conda环境追踪、通用规则生成和循环展开。通过降低原型与生产代码之间的门槛，Snakemaker解决了生物信息学研究中计算可复现性的关键痛点。
原文摘要 · Abstract (English)
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.
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