BaseCamp: An Agentic AI Framework for Automating DNA Sequencing Data Analysis Decisions

Forum topic · 小凯 · 2026-09-26

Summary

BaseCamp (arXiv:2609.21945) is a novel agentic AI framework that automates the decision layer of DNA sequencing pipelines. While established tools and workflow management systems reliably execute quality control, alignment, variant calling, and annotation at scale, tasks such as selecting quality thresholds, adjudicating borderline variant calls, diagnosing anomalies, and deciding which findings require expert review remain manual, inconsistent across operators, and often undocumented. BaseCamp decomposes the pipeline into six specialized AI agents covering sample intake, alignment, variant calling, annotation, cross-stage monitoring, and reporting. Crucially, the agents do not perform sequence analysis themselves; established tools execute alignment and detection, while agents select tools, configure parameters, interpret outputs, and decide next steps. This confines language model reasoning to the judgment layer where it is reliable, preserving the reproducibility of existing toolchains. Agent reasoning is powered by an ensemble of fine-tuned domain-specific large language models that run locally, so sequencing data never leaves the operating environment, with human-in-the-loop orchestration. Authored by Eranga Bandara, Xueping Liang, and Asanga Gunaratna, the work targets machine learning and bioinformatics audiences.

Paper Overview

Field: ML Authors: Eranga Bandara, Xueping Liang, Asanga Gunaratna Published: 2026-09-25 arXiv: 2609.21945

Abstract

DNA sequencing pipelines span quality control, alignment, variant calling, and annotation, and these mature tools can now be reliably executed at scale by workflow management systems. What remains manual is the decision layer: choosing appropriate quality thresholds for samples and platforms, adjudicating borderline variant calls, diagnosing anomalies, and determining which findings need expert review. These decisions are repetitive, judgment-dependent, inconsistent across operators, and often 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:

1. Sample intake and quality control 2. Alignment 3. Variant calling 4. Annotation 5. Cross-stage monitoring 6. Reporting

Key Design Principle

The agents do not perform sequence analysis — established tools execute alignment and detection, while the agents handle tool selection, parameter configuration, output interpretation, and deciding next steps. This confines language model reasoning to the judgment layer where it is reliable, while preserving the reproducibility of existing toolchains.

Deployment Characteristics

Agent reasoning is driven by an ensemble of fine-tuned domain-specific large language models
Models run locally, so sequencing data never leaves the operating environment
Human-in-the-loop orchestration is built in

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