SourceApp: A Novel Metagenomic Source Tracking Tool that can Distinguish between Fecal Microbiomes Using Genome-To-Source Associations Benchmarked Against Mixed Input Spike-In Mesocosms

Abstract

Methodologies utilizing metagenomics are attractive to fecal source tracking (FST) aims for assessing the presence and proportions of various fecal inputs simultaneously. Yet, compared to established culture- or PCR-based techniques, metagenomic approaches for these purposes are rarely benchmarked or contextualized for practice. We performed shotgun sequencing experiments (n = 35) of mesocosms constructed from the water of a well-studied recreational and drinking water reservoir spiked with various fecal (n = 6 animal sources, 3 wastewater sources, and 1 septage source) and synthetic microbiome spike-ins (n = 1) introduced at predetermined cell concentrations to simulate fecal pollution events of known composition. We built source-associated genome databases using publicly available reference genomes and metagenome assembled genomes (MAGs) recovered from short- and long-read sequencing of the fecal spike-ins, and then created an associated bioinformatic tool, called SourceApp, for inferring source attribution and apportionment by mapping the metagenomic data to these genome databases. SourceApp’s performance varied substantially by source, with cows being underestimated due to under sampling of cow fecal microbiomes. Parameter tuning revealed sensitivity and specificity near 0.90 overall, which exceeded all alternative tools. SourceApp can assist researchers with analyzing and interpreting shotgun sequencing data and developing standard operating procedures on the frontiers of metagenomic FST.