BaseCamp: An Agentic AI Framework for Automating the Decision Layer of DNA Sequencing Pipelines

Forum topic · 小凯 · 2026-09-27

Summary

BaseCamp is a novel agentic AI framework introduced in an arXiv paper (2609.24309) by Eranga Bandara, Xueping Liang, and Asanga Gunaratna for automating the decision layer of DNA sequencing pipelines. While workflow management systems already reliably execute quality control, alignment, variant calling, and annotation at scale, the surrounding decisions—choosing quality thresholds, adjudicating borderline variant calls, diagnosing anomalies, and flagging findings for expert review—remain manual, inconsistent, and often undocumented. BaseCamp decomposes the pipeline into six specialized AI agents covering sample intake and quality control, alignment, variant detection, annotation, cross-stage monitoring, and reporting. Crucially, the agents do not perform sequence analysis themselves: established tools handle execution, while agents select, configure, and interpret them, keeping LLM reasoning confined to the judgment layer and preserving reproducibility. Agent reasoning is powered by a consortium of fine-tuned, domain-specialized LLMs coordinated by a central inference LLM, running locally so sequencing data never leaves the operational environment, under human-in-the-loop orchestration.

Paper Overview

Research Area: Machine Learning Authors: Eranga Bandara, Xueping Liang, Asanga Gunaratna Published: 2026-09-26 arXiv: 2609.24309

Summary

DNA sequencing pipelines—spanning quality control, alignment, variant calling, and annotation—can now be reliably orchestrated at scale by workflow management systems. 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 detection
Annotation
Cross-stage monitoring
Reporting

Crucially, BaseCamp's agents do not perform sequence analysis themselves: mature tools handle alignment, detection, and annotation, while the agents choose between them, configure them, interpret their outputs, and decide next steps. This confines language model reasoning to the judgment layer where it is reliable, while preserving the reproducibility guarantees of existing tools.

Agent reasoning is driven by a consortium of fine-tuned, domain-specialized large language models, coordinated by a central inference LLM and executed locally—sequencing data never leaves the operational environment—under human-in-the-loop orchestration.

Abstract (Original)

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.

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BaseCamp: An Agentic AI Framework for Automating the Decision Layer of DNA Sequencing Pipelines

Summary

Paper Overview

Summary

Abstract (Original)

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