ChatDNA: An End-to-end Genetics Analysis Pipeline with Embedding Databases and Prompt Learning

DNA sequencing technology has revolutionized precision medicine by enabling personalized treatments, the early detection and prevention of diseases. However, existing methods for generating DNA analysis reports face challenges related to precision, completeness, and automation. Recent advances in large language models (LLMs) have substantially impacted modern biology and medicine. In this paper, we introduce a novel pipeline called ChatDNA, which uses a large medical language model based on vector databases and prompt learning. This approach facilitates the end-to-end generation of DNA sequencing data analysis reports, achieving an accuracy rate of 71.64% without quantization and 68.31% with INT8 quantization. Furthermore, the deployment of the quantized model on an integrated device equipped with a DNA sequencer allows for real-time, on-site DNA analysis, enhancing both the accessibility and efficiency of precision medicine. ChatDNA significantly improves the precision, depth, and automation of report generation, laying a solid foundation for AI-driven personalized precision medicine.