Pathonoia

Pathonoia detects viruses and bacteria in bulk RNA-seq data using a k-mer-based approach, aggregates read-level evidence to improve specificity and sensitivity, and correlates microbial gene expression with host gene expression to investigate microbe–host interactions.

Key Features:

• High Precision and Recall: Pathonoia leverages a k-mer-based method for species identification and aggregates evidence across all reads within a sample to enhance specificity and sensitivity in detecting viruses and bacteria from RNA-seq data.
• Microbe-Host Interaction Analysis: Pathonoia correlates microbial gene expression with host gene expression to explore potential microbe–host cell interactions and their influence on human disease.
• Performance Superiority: The method demonstrated superior detection specificity on both in silico and real datasets compared to competing algorithms.

Scientific Applications:

• Microbial involvement in human disease: Pathonoia can be used to study how microbial infections influence or exacerbate human diseases by correlating microbial and host transcriptional signals.
• Tissue-specific investigation: Pathonoia has been applied to bulk RNA-seq case studies of human liver and brain tissues to uncover microbial associations with disease conditions in these organs.

Methodology:

K-mer based species identification followed by aggregation of evidence across reads and correlation of microbial gene expression with host gene expression.