RaptGen-UI: an integrated platform for exploring and analyzing the sequence landscape of HT-SELEX experiments.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2025-05-23 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf120

Ryota Nakano, Natsuki Iwano, Akiko Ichinose, Michiaki Hamada

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Abstract

Summary: RaptGen-UI provides intuitive graphical user-interface of the system exploring and analyzing the sequence landscape of high-throughput (HT)-SELEX (Systematic Evolution of Ligands by EXponential enrichment) experiments through machine learning-driven visualization with optimization capabilities. This software enables wet-lab researchers to efficiently analyze HT-SELEX dataset and optimize RNA aptamers without requiring extensive computational expertise. The containerized architecture ensures secure local deployment and supports both of high-performance Graphics Processing Unit (GPU) acceleration and CPU-only environments, making it suitable for various research settings.

Availability and implementation: This software is a web-based application running locally on the user's PC. The frontend is constructed using Next.js and Plotly.js with TypeScript, while the backend is developed using FastAPI, Celery, PostgreSQL RDBMS, and Redis with Python. Each module is encapsulated within Docker containers and deployed via Docker Compose. The system supports both CUDA GPU and CPU-only environments. Source code and documentation are freely available at https://github.com/hmdlab/RaptGen-UI.

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RaptGen-UI：探索和分析HT-SELEX实验序列景观的集成平台。

RaptGen-UI通过机器学习驱动的可视化和优化功能，为系统提供直观的图形用户界面，探索和分析高通量(HT)-SELEX （Systematic Evolution of Ligands by EXponential enrichment）实验的序列景观。该软件使湿实验室研究人员能够有效地分析HT-SELEX数据集和优化RNA适配体，而不需要广泛的计算专业知识。容器化架构确保了安全的本地部署，并支持高性能图形处理单元（GPU）加速和仅cpu环境，使其适合各种研究设置。可用性和实现：该软件是一个基于web的应用程序，在用户的PC上本地运行。前端是使用Next.js和Plotly.js与TypeScript构建的，而后端是使用FastAPI，芹菜，PostgreSQL RDBMS和Redis与Python开发的。每个模块都封装在Docker容器中，并通过Docker Compose进行部署。系统支持CUDA GPU和cpu两种环境。源代码和文档可在https://github.com/hmdlab/RaptGen-UI免费获得。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量