Pathogen Detection in RNA-Seq Data with Pathonoia

Motivation: Recent evidence suggests that bacterial and viral infections may cause or exacerbate many human diseases. One method of choice to detect microbes in tissue is RNA sequencing. While the detection of specific microbes using RNA sequencing offers good sensitivity and specificity, untargeted...

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Bibliographic Details
Published inbioRxiv
Main Authors Anna-Maria Liebhoff, Menden, Kevin, Laschtowitz, Alena, Franke, Andre, Schramm, Christoph, Bonn, Stefan
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 21.01.2022
Subjects
Algorithms
Cell interactions
Gene expression
Ribonucleic acid
RNA
RNA viruses
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Summary:Motivation: Recent evidence suggests that bacterial and viral infections may cause or exacerbate many human diseases. One method of choice to detect microbes in tissue is RNA sequencing. While the detection of specific microbes using RNA sequencing offers good sensitivity and specificity, untargeted approaches suffer from very high false positive rates and a lack of sensitivity for lowly abundant organisms. Results: We introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host cell interactions by correlating the microbial to host gene expression. Pathonoia outperforms competing algorithms in microbial detection specificity, both on in silico and real datasets. Lastly, we present two case studies in human liver and brain in which microbial infection might exacerbate disease. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/kepsi/Pathonoia
DOI:10.1101/2022.01.19.476681