Operon is an AI-powered, dedicated integrated development environment (IDE) for bioinformatics, developed by the Swarup Lab at UC Irvine. It is designed to allow biologists to perform sequencing data analysis, remote work on HPC clusters, and literature searches—tasks they routinely perform—within a single desktop application. As a lightweight native app combining Tauri 2 (Rust backend) and React 18 frontend, it integrates VS Code's Monaco Editor and xterm.js terminal while incorporating Claude AI as its core engine. Just as GPT can understand and generate any text, Operon is an "AI copilot for biologists" that allows AI to directly write and execute code for any bioinformatics workflow and interpret the results. Previously, to build an analysis pipeline, a biology researcher had to switch between more than four tools: opening a general-purpose IDE like VS Code or JupyterLab, SSHing into an HPC cluster via a separate terminal, searching PubMed in a browser, and opening a separate ChatGPT window to ask questions about code. Operon integrates all of this into a single window, and it includes 665 curated bioinformatics protocols (approximately 30 categories, including single-cell RNA-seq, spatial transcriptomics, CRISPR screens, ChIP-seq, and proteomics), allowing researchers to select a protocol and have the AI execute it without having to write the analysis methodology from scratch. In particular, the AI agent directly executes commands on a remotely connected HPC node via SSH, and the tmux session is maintained even after the app restarts, ensuring the continuity of large-scale sequencing tasks. There are three representative use cases. First, in single-cell RNA-seq analysis, a researcher can use Operon's Agent mode to automatically run the Scanpy pipeline on an HPC node and view the resulting UMAP plot directly within the app. Second, by selecting a built-in protocol for ATAC-seq data, the AI sequentially generates and executes code from peak calling to motif enrichment. Third, in Report mode, the analysis results are automatically organized into a structured scientific report, and relevant papers are cited through PubMed searches, significantly reducing the distance from analysis to a draft paper. In addition to direct connection to Anthropic, the AI backend also supports local models such as Ollama, vLLM, and LM Studio, allowing for flexible deployment according to institutional security policies.

AI-powered, dedicated integrated development environment (IDE) for bioinformatics, designed for biologists.

Automated Single-cell RNA-seq Analysis: In Agent mode, the AI sequentially generates and executes a Scanpy/Seurat pipeline on HPC nodes via SSH, covering quality filtering, normalization, clustering, and UMAP visualization. Instantly view the resulting plots in the in-app editor, and in Report mode, automatically generate an analytical summary report with PubMed citations.