Microbial source tracking (mST) methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach provided a means to use deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an ever-increasing variety of topics, from beach contamination to human behavior. In this study, we expanded the SourceTracker approach to shotgun metagenomic datasets, and tested the performance and stability of source estimates using sink samples from coastal marine environments. We also implemented additional features for determining the stability of source proportion estimates and developing new techniques using splitting metagenomic data for domain-specific analysis (e.g., bacteria, archaea, eukarya). The added feature allows users to visualize the precision of SourceTracker and assess ways to optimize performance. Results obtain from our meta-Source Tracker (mST) approach showed mST was highly effective at predicting the composition of known sources using shotgun metagenomic libraries. However, we also showed that different taxonomic domains sometimes presented highly divergent pictures of source origins, suggesting that applying mST to separate domains provides a deeper understanding into the microbial origins of intricate, mixed-source environments and suggests that certain domains may be preferable for tracking specific sources of contamination.