MultiStructRNA：用于多算法RNA二级结构预测、集成分析与可视化的Python包
MultiStructRNA: a Python package for multi-algorithm RNA secondary structure prediction, ensemble analysis, and visualization
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中文总结 AI 辅助
MultiStructRNA是一款统一Python工具包，整合多种RNA二级结构预测算法，解决工具碎片化等问题，支持集成分析与可视化，简化RNA结构分析，助力相关工作流程应用。
中文摘要 AI 辅助
我们推出MultiStructRNA，这是一款用于RNA二级结构预测、集成分析与可视化的统一Python工具包。尽管RNA二级结构对RNA生物学及治疗设计至关重要，但工具碎片化、输入输出格式不兼容、可视化支持有限等问题常阻碍其实际应用。MultiStructRNA通过单一高级API解决这些挑战，该API可协调多种预测算法，将结果统一为一致模式，并通过适用于交互式笔记本和生产流程的对象模型提供可复现、感知集成的指标。MultiStructRNA支持无需更改工作流程即可无缝切换预测方法，同时支持笔记本内可视化与可导出可视化。其设计具备可扩展性，支持高通量分析，简化方法间比较，同时标准化下游特征提取。当前版本还包含可选的智能体可读工作流脚本，记录了依赖设置、后端适配器约定、SHAPE数据协调、结构解读及比较序列分析等内容。通过在通用框架内整合多种RNA二级结构包，MultiStructRNA简化了结构分析，推动其在RNA设计、优化及机器学习工作流程中的应用。
英文摘要
We introduce MultiStructRNA, a unified Python toolkit for RNA secondary structure prediction, ensemble analysis, and visualization. Although RNA secondary structure is central to RNA biology and therapeutic design, practical adoption is often hindered by fragmented tooling, incompatible input and output formats, and limited visualization support. MultiStructRNA addresses these challenges through a single high-level API that orchestrates multiple prediction algorithms, harmonizes results into a consistent schema, and provides reproducible, ensemble-aware metrics through an object model suited to both interactive notebooks and production pipelines. MultiStructRNA enables seamless switching between prediction methods without requiring workflow changes and supports both in-notebook and exportable visualizations. Designed for scalability, it supports high-throughput analyses and simplifies comparison across methods while standardizing downstream feature extraction. The current release also includes optional agent-readable workflow recipes that document dependency setup, backend-adapter conventions, SHAPE-data reconciliation, structure interpretation, and comparative sequence analyses. By integrating diverse RNA secondary structure packages within a common framework, MultiStructRNA streamlines structure analysis and facilitates its use in RNA design, optimization, and machine learning workflows.
