Automated Bioinformatics Analysis via AutoBA

Juexiao Zhou, Bin Zhang, Xiuying Chen, Haoyang Li, Xiaopeng Xu +2 more
9/6/2023
q-bio.GNcs.AIcs.LG

No official code found. Best implementation: JoshuaChou2018/AutoBA (234 stars, MIT, last commit 2024-11-04).

Abstract

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.

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Code Implementations(1)

MIT

An AI Agent for Fully Automated Multi-omic Analyses

23438Shell, PythonSep 2, 20231 years agoMIT

Cite this paper

@article{zhou2023automated,
  title  = {Automated Bioinformatics Analysis via AutoBA},
  author = {Juexiao Zhou and Bin Zhang and Xiuying Chen and Haoyang Li and Xiaopeng Xu and Siyuan Chen and Xin Gao},
  year   = {2023},
  eprint = {2309.03242},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.03242v1}
}

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