The bioinformatics annotation pipeline developed for this method was specifically designed to have high prediction performance. To this end, we implemented a taxonomy annotation based on sequence searches of 100% identity over the entire length of the 16S rRNA gene V4 region from the preliminary targets in our database (S1 Doc). Curated databases were generated for each of the taxa in our preliminary target list using the performance metrics sensitivity, specificity, precision, and negative predictive value as optimizing parameters. In other words, the bioinformatics pipeline was optimized to ensure that a positive result truly means the target is present in the sample and a negative result is only obtained when no target is present in the sample. After optimizing the confusion matrices for all preliminary targets, 28 out of 46 targets passed our stringent threshold of 90% for each of the parameters (Fig 2). The resulting target list is composed of 5 known pathogens, 3 beneficial bacteria, and 20 additional microorganisms related to various gut afflictions (S2 Table), including commensal bacteria and one archaeon. On average the sensitivity, specificity, precision, and negative prediction value of the microorganisms on our target list are 99.0%, 100%, 98.9%, and 100%, for the species, and 97.4%, 100%, 98.5%, and 100% for the genera.