Fig. 1

Pathonoia toolkit. A The algorithm analyzes unaligned RNA-seq reads, based on Kraken 2. Kraken generates k-mer assignments and a taxonomic classification for each read (grey box). Pathonoia uses all k-mer assignments of a sample and combines them into a non-read-count based abundance metric \(A_{O}\). B Pathonoia and the downstream analysis template are available on GitHub. C The analysis workflow for a dataset. A transcriptome alignment yields gene counts and unaligned reads which are analyzed by Pathonoia (A). A differential abundance analysis reports organisms that are more frequent in one sample group compared to another (examples in Fig. 3B, F). An “organism of interest” (OoI) can be selected for understanding its role in a sample group. Samples with (\(A_{OoI}>0\)) and without (\(A_{OoI}=0\)) the OoI are compared in a differential gene expression analysis using the gene counts. A gene set enrichment analysis of de-regulated genes may uncover the pathways affected by the OoI