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Standardizing RNA-seq Analysis of Fungal Pathogens Using BRC-Analytics and Agentic AI: A Candidozyma auris Case Study
Anton Nekrutenko1, Danielle Callan2, Marius Van Den Beek1
1Dept. of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA.
Abstract:
Candidozyma auris has emerged as a critical global health threat due to multidrug resistance and healthcare-associated transmission. While RNA-seq has become the primary tool for studying C. auris pathogenesis, inconsistent use of reference genomes and bioinformatics tools complicate cross-study comparisons. Here we demonstrate how BRC-Analytics, a platform for pathogen genomics, combined with an agentic AI assistant, enables reproducible RNA-seq analysis. By re-analyzing data from two publications we achieved near-perfect correlation with published results despite annotation version differences. We addressed provenance challenges associated with using AI agents with Galaxy by forcing them to invoke Galaxy's native tools rather than manipulating data directly. For custom analyses outside Galaxy's toolset, we provide standalone JupyterLite notebooks that reproduce our analysis without AI involvement. This framework-combining AI-assisted automation with rigorous provenance tracking-establishes a template for standardized, reproducible fungal pathogen genomics. To the best of our knowledge, this is the first example of integration between public data repositories, reproducible analysis workflows, and agentic AI tools. Our subsequent efforts will focus on improving the seamlessness of this integration.
Insights
BRC-Analytics and AI tools enable reproducible RNA-seq analysis for the fungal pathogen Candida auris. This platform ensures accurate, comparable results for studying drug resistance and transmission, establishing a new standard for pathogen genomics.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Candida auris is a critical global health threat due to multidrug resistance and healthcare-associated transmission.
- RNA-sequencing (RNA-seq) is vital for studying C. auris pathogenesis, but inconsistent bioinformatics approaches hinder cross-study comparisons.
Purpose of the Study:
- To demonstrate a reproducible RNA-seq analysis framework for C. auris using BRC-Analytics and an AI assistant.
- To address challenges in data provenance and ensure comparability across studies despite variations in reference genomes and annotation versions.
Main Methods:
- Re-analysis of existing C. auris RNA-seq datasets using BRC-Analytics and an agentic AI assistant.
- Integration with Galaxy workflows to ensure native tool invocation and provenance tracking.
- Development of standalone JupyterLite notebooks for custom analyses outside Galaxy.
Main Results:
- Achieved near-perfect correlation with published RNA-seq results, validating the reproducibility of the framework.
- Demonstrated successful management of provenance challenges associated with AI agent use in bioinformatics.
- Established a template for standardized, reproducible fungal pathogen genomics analysis.
Conclusions:
- The BRC-Analytics platform combined with AI offers a robust solution for reproducible RNA-seq analysis of C. auris.
- This integrated framework sets a precedent for combining public data repositories, reproducible workflows, and AI tools in pathogen genomics.
- Further work will focus on enhancing the seamless integration of these components for broader application.
