Rapid Development of Omics Data Analysis Applications through Vibe Coding
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914087507591168 |
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| author | Meyer, Jesse G. |
| author_facet | Meyer, Jesse G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09804 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rapid Development of Omics Data Analysis Applications through Vibe Coding Meyer, Jesse G. Other Quantitative Biology Software Engineering 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. |
| title | Rapid Development of Omics Data Analysis Applications through Vibe Coding |
| topic | Other Quantitative Biology Software Engineering |
| url | https://arxiv.org/abs/2510.09804 |