Pathonoia- A new tool to detect pathogens in RNA-seq data.

Detecting viruses and bacteria in RNA-seq data with less false positive rate is a difficult task. A new tool is introduced to detect pathogens in RNA-seq data with high precision and recall known as Pathonoia [1].

Pathonoia uses k-mer based method for species identification. It provides a nice framework that shows microbe-host interactions.

How Pathonoia works?

• Pathonoia algorithm uses Kraken2 to align RNA-seq reads and to generate k-mer assignments and a taxonomic classification for each read.
• It then combines all k-mer assignments of a sample into a non-read-count-based abundance metric.
• Further, users are allowed to subject the output to downstream analysis that includes differential abundance analysis and differential expression analysis.
• It finally outputs the potential effect of the organism in sample.

Pathonoia is capable of supporting novel hypotheses on microbial infections [1]. It is written in Python language and is freely available on GitHub.

For further reading, click here.

References

1. Liebhoff, AM., Menden, K., Laschtowitz, A. et al. (2023). Pathogen detection in RNA-seq data with Pathonoia. BMC Bioinformatics 24, 53.
2. Breitwieser, F. P., Baker, D. N., & Salzberg, S. L. (2018). KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome biology, 19(1), 1-10.