BaseCamp --- An Agentic AI Framework for Automating DNA Sequencing Data Pipelines

2026-09-23

AI Summary

This study addresses the repetitive and inconsistent decision-making processes in DNA sequencing pipelines, particularly regarding quality threshold setting and variant calling. To this end, it proposes a multi-agent collaborative framework comprising six specialized AI agents powered by a consortium of locally deployed, fine-tuned domain-specific large language models (LLMs). Incorporating a human-in-the-loop mechanism, the framework automates pipeline configuration and judgment tasks. Its core innovation lies in strictly constraining LLM reasoning to the decision layer without replacing underlying computations, thereby establishing an auditable filtering ledger and enabling cross-stage anomaly detection. Experimental results demonstrate that agent-generated configurations align closely with expert practices, significantly enhancing pipeline transparency and anomaly detection capabilities.

Abstract

DNA sequencing pipelines, spanning quality control, alignment, variant calling, and annotation, are now reliably executed by workflow management systems that orchestrate established bioinformatics tools at scale. What remains manual is the decision layer surrounding that execution: selecting quality thresholds appropriate to a sample and platform, adjudicating borderline variant calls, diagnosing anomalies, and determining which findings warrant expert review. These decisions are repetitive, judgment-intensive, inconsistent across operators, and frequently undocumented. This paper introduces BaseCamp, a novel agentic AI framework for automating the decision layer of DNA sequencing pipelines. The framework decomposes the pipeline into six specialized AI agents, covering sample intake and quality control, alignment, variant calling, annotation, cross-stage monitoring, and reporting. Critically, BaseCamp agents do not perform sequence analysis: established tools execute alignment, calling, and annotation, while the agents select among them, configure them, interpret their output, and decide what follows. This confines language model reasoning to the judgment layer where it is reliable and preserves the reproducibility existing tooling guarantees. Agent reasoning is powered by a consortium of fine-tuned, domain-specialized large language models coordinated by a central reasoning LLM, executing locally so no sequencing data leaves the operating environment, under human-in-the-loop orchestration. Evaluation shows agent-generated configurations are concordant with expert practice, that an explicit filtering ledger renders inspectable what filtering otherwise removes without trace, and that cross-stage anomaly detection surfaces conditions execution monitoring misses. BaseCamp offers a generalizable blueprint for agentic automation of scientific data pipelines.

Problem

• DNA sequencing pipelines
• decision layer automation
• agentic AI
• variant calling
• bioinformatics

Innovation

• Agentic AI
• DNA Sequencing Pipelines
• Large Language Models
• Human-in-the-loop
• Cross-stage Anomaly Detection