Pathogen Detection in RNA-Seq Data with Pathonoia

Abstract

MotivationRecent 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. ResultsWe 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. AvailabilityA Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub https://github.com/kepsi/Pathonoia. Contactaliebho1@jhu.edu and s.bonn@uke.de Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/476681v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@16775f2org.highwire.dtl.DTLVardef@17099f4org.highwire.dtl.DTLVardef@18b8dfeorg.highwire.dtl.DTLVardef@117498b_HPS_FORMAT_FIGEXP M_FIG C_FIG