June 2026

Conference Paper

KBase Research Agent: Automated Multi-Agent Workflow Construction for Reproducible Genome Analysis

By:
Gupta, Prachi; Chivian, Dylan; Cottingham Jr, Robert W; Riehl, William; Neely, Christopher; Henry, Christopher; Dehal, Paramvir; Cashman, Mikaela; Canon, Shane; Arkin, Adam
Page Number:
1-12
Book Title:
PASC '26: Proceedings of the Platform for Advanced Scientific Computing Conference
Publication Date:
June 2026
Conference Name:
Platform for Advanced Scientific Computing (PASC26) Conference
Conference Location:
Bern, Switzerland
Conference Sponsor:
CSCS, Association for Computing Machinery, Sighpc
View DOI Listing:
https://doi.org/10.1145/3815572.3815744

Abstract

Constructing multi-step bioinformatics workflows, from read quality control through genome assembly to functional annotation, requires expertise in both biology and computational tool selection, creating a bottleneck for scalable and reproducible analysis. We present the KBase Research Agent, a multi-agent system for automating such workflows within the DOE Systems Biology Knowledgebase (KBase). Given a set of sequencing reads and a research objective, the agent constructs an analysis plan grounded in KBase documentation and a Knowledge Graph (KG) of the KBase application catalog, then selects, parameterizes, validates and executes appropriate KBase applications to carry out the workflow. The resulting analysis is preserved as a reproducible KBase Narrative. We evaluate the system's planning and execution quality against ground truth constructed from reference workflows derived from peer-reviewed Microbiology Resource Announcements. We further apply the agent to 100 previously unanalyzed bacterial isolate genomes from the JGI IMG/M database, where it autonomously performed read quality control, genome assembly, taxonomic classification with GTDB-Tk, and downstream analysis producing annotated genomes, reproducible Narratives, and draft manuscripts without human intervention. Across these experiments, the KBase Research Agent demonstrates the feasibility of domain-grounded, end-to-end scientific workflow automation in a production bioinformatics platform.


Related Researchers