Reproducible and shareable bioinformatics pipelines from natural-language prompts

Abstract

Large language models (LLMs) are increasingly used to generate bioinformatics pipelines and to carry out analyses from natural-language prompts. However, the resulting analyses are often difficult to reproduce across sessions, owing to the non-deterministic nature of LLM-driven conversations and heterogeneity of local execution environments, and cannot run on remote high-performance computing (HPC) servers or be shared and reused. We present Autopipe, a platform that guides any Model Context Protocol (MCP) - compatible LLM to produce, execute, and publish source-preserved, re-executable containerized pipelines. Autopipe enables users to execute bioinformatics pipelines on any on-premises remote servers - supported by comprehensive setup documentation aimed at researchers without prior server-administration experience - and to visualize results through an extensible web-based viewer. The Autopipe platform comprises four components: a desktop application with an embedded MCP server for pipeline management and remote execution, an online registry for pipeline and plugin discovery, a web-based result viewer, and a CLI tool for customizing viewer plugins. Autopipe turns conversational analysis into re-executable and shareable workflows. Autopipe is freely available at https://autopipe.org/.