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Standardizing RNA-seq Analysis of Fungal Pathogens Using BRC-Analytics and Agentic AI: A Candidozyma auris Case

Anton Nekrutenko¹, Danielle Callan², Marius Van Den Beek¹

¹Dept. of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA.

Biorxiv : the Preprint Server for Biology

|January 9, 2026

Summary

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.

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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.