Development of an Agentic AI Model for NGS Downstream Analysis Targeting Researchers with Limited Biological Background
Abstract
Next-Generation Sequencing (NGS) has become a cornerstone of genomic research, yet the complexity of downstream analysis-ranging from differential expression gene (DEG) identification to biological interpretations-remains a significant barrier for researchers lacking specialized computational and biological expertise. While recent studies have introduced AI agents for RNA-seq analysis, most focus on general workflows without offering tailored interpretations or guidance for novices. To address this gap, we developed an Agentic AI model designed to automate NGS downstream analysis, provide literature-backed interpretations, and autonomously recommend advanced analytical methods. Built on the Llama 3 70B Large Language Model (LLM) and a Retrieval-Augmented Generation (RAG) framework, the model is deployed as an interactive Streamlit web application. The system integrates standard bioinformatics tools (Biopython, GSEApy, gProfiler) to execute core analyses, including DEG identification, clustering, and pathway enrichment. Uniquely, the agent utilizes RAG to query PubMed via Entrez, synthesizing biological insights and validating hypotheses with current literature. In a case study using cancer-related dataset, the model successfully identified significant DEGs, visualized clinical correlations, and derived evidence-based insights (e.g., linking BRAF mutations to prognosis), subsequently executing advanced survival modeling upon user selection. This framework democratizes bioinformatics by enabling researchers with limited backgrounds to seamlessly transition from basic data processing to advanced hypothesis testing and validation.
Summary
This paper introduces an Agentic AI model designed to automate and simplify NGS downstream analysis for researchers lacking extensive bioinformatics expertise. The core challenge addressed is the complexity of analyzing NGS data, from identifying differentially expressed genes (DEGs) to interpreting biological significance. The model leverages the Llama 3 70B Large Language Model (LLM) within a Retrieval-Augmented Generation (RAG) framework, deployed as an interactive Streamlit web application. It integrates standard bioinformatics tools like Biopython, GSEApy, and gProfiler to perform DEG identification, clustering, and pathway enrichment. A key innovation is the use of RAG to query PubMed through Entrez, providing literature-backed interpretations and validating hypotheses. In a case study using cancer data, the model successfully identified DEGs, visualized clinical correlations, and linked BRAF mutations to prognosis, even executing advanced survival modeling based on user selection. The agentic nature of the AI model allows it to autonomously plan, execute, and reflect on analyses based on user-provided gene expression data and clinical information. The system uses statistical tests to identify relevant clinical variables for the user to define specific prompts that guide analysis. The model then interprets the results using the LLM, enriching its interpretations with context from scientific literature retrieved via the RAG pipeline. This framework extends existing AI agent approaches by incorporating adaptive RAG-based literature search and user-selected advanced workflows, effectively democratizing bioinformatics by enabling researchers with limited backgrounds to perform complex analyses and hypothesis testing.
Key Insights
- •The model uses Llama 3 70B and RAG to provide literature-backed interpretations of NGS data, addressing the need for biological context in downstream analysis.
- •The integration of standard bioinformatics tools (Biopython, GSEApy, gProfiler) allows for a seamless transition from basic data processing to advanced analysis within the AI agent framework.
- •The RAG pipeline queries PubMed via Entrez and uses sentence-transformer models to embed abstracts in a FAISS vector database for efficient retrieval and summarization, enhancing the LLM's reasoning capabilities.
- •The case study identified ~40 significant DEGs (p < 0.05) and linked BRAF mutations to poor prognosis in lung cancer (p < 0.05 in survival analysis), demonstrating the model's ability to derive meaningful biological insights.
- •The system recommends and executes advanced analyses, such as survival modeling (using statsmodels CoxPHFitter), based on user-defined prompts and DEG-driven hypotheses, enabling hypothesis testing.
- •A limitation is the reliance on the quality of the input data (gene expression matrices and clinical information) and the accuracy of the PubMed search results.
- •The model builds upon previous works like GenoMAS and AutoBA by incorporating adaptive RAG-based literature search and user-selected advanced workflows, providing a more comprehensive solution for NGS downstream analysis.
Practical Implications
- •This model can be used by researchers with limited bioinformatics expertise to perform complex NGS data analysis, accelerating discoveries in precision medicine and other areas.
- •The Streamlit web application provides an accessible and user-friendly interface for interacting with the AI agent, lowering the barrier to entry for NGS data analysis.
- •The literature-backed interpretations and advanced analysis recommendations can help researchers generate hypotheses, validate findings, and design further experiments.
- •Future research directions include incorporating gene network-based drug response prediction for target discovery and supporting multi-omics data integration.
- •The model's ability to automate complex tasks and provide interpretable insights can significantly reduce the time and resources required for NGS data analysis, increasing research efficiency.