通过 Vibe Coding 快速开发组学数据分析应用
Rapid Development of Omics Data Analysis Applications through Vibe Coding
AI总结：
本文展示了通过“vibe coding”利用LLM和自主编码智能体，仅用四个自然语言提示在十分钟内零手动编码开发出功能齐全的蛋白质组学数据分析网站，大幅降低了计算生物学软件开发门槛。
摘要：
构建定制化数据分析平台传统上需要大量的软件工程专业知识，这限制了许多研究人员的可及性。在此，我展示了现代大型语言模型（LLMs）和自主编码智能体可以通过一种称为“vibe coding”的过程大幅降低这一门槛。这是一种迭代式的、对话式的软件创建方式，用户用自然语言描述目标，AI智能体实时生成、测试和完善可执行代码。作为概念验证，我使用Vibe coding创建了一个功能齐全的蛋白质组学数据分析网站，能够执行标准任务，包括数据归一化、差异表达测试和火山图可视化。整个应用程序，包括用户界面、后端逻辑和数据上传流水线，在不到十分钟内仅使用四个自然语言提示开发完成，无需任何手动编码，成本低于2美元。该领域以往的工作通常需要高度训练的程序员投入数万美元的研究努力。我详细介绍了逐步生成过程，并评估了生成代码的功能。此演示突显了vibe coding如何使领域专家能够快速构建复杂分析工具的原型，从而改变计算生物学软件开发的速度和可及性。
英文摘要：
Building custom data analysis platforms traditionally requires extensive software engineering expertise, limiting accessibility for many researchers. Here, I demonstrate that modern large language models (LLMs) and autonomous coding agents can dramatically lower this barrier through a process called 'vibe coding', an iterative, conversational style of software creation where users describe goals in natural language and AI agents generate, test, and refine executable code in real-time. As a proof of concept, I used Vibe coding to create a fully functional proteomics data analysis website capable of performing standard tasks, including data normalization, differential expression testing, and volcano plot visualization. The entire application, including user interface, backend logic, and data upload pipeline, was developed in less than ten minutes using only four natural-language prompts, without any manual coding, at a cost of under $2. Previous works in this area typically require tens of thousands of dollars in research effort from highly trained programmers. I detail the step-by-step generation process and evaluate the resulting code's functionality. This demonstration highlights how vibe coding enables domain experts to rapidly prototype sophisticated analytical tools, transforming the pace and accessibility of computational biology software development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
