With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow. In response to this need, we introduce Auto Bioinformatics Analysis (AutoBA), an autonomous AI agent based on a large language model designed explicitly for conventional omics data analysis. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBA's robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome sequencing (WGS), RNA sequencing (RNA-seq), single-cell RNA-seq, ChIP-seq, and spatial transcriptomics. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA deploys the analysis locally, preserving data privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents a convenient tool, offering robustness and adaptability for complex omics data analysis.

翻译：随着组学数据的快速增长和演变，对能够处理这些分析的简化且适应性工具的需求持续增加。为应对这一需求，我们引入了自动生物信息学分析（AutoBA），这是一种基于大型语言模型的自主AI智能体，专门设计用于常规组学数据分析。AutoBA通过仅需最少的用户输入来简化分析流程，同时为各种生物信息学任务提供详细的逐步计划。经过专家生物信息学家的严格验证，AutoBA的稳健性和适应性在全基因组测序（WGS）、RNA测序（RNA-seq）、单细胞RNA-seq、ChIP-seq及空间转录组学等一系列组学分析案例中得到了证实。AutoBA基于输入数据变化自行设计分析流程的独特能力进一步凸显了其多功能性。与在线生物信息学服务相比，AutoBA在本地部署分析，从而保护数据隐私。此外，与预定义流程不同，AutoBA具备与新兴生物信息学工具同步的适应性。总体而言，AutoBA代表了一种便捷工具，为复杂组学数据分析提供了稳健性和适应性。