The DNA Signature Prediction Pipeline, described in detail elsewhere (7) (8) (9) (10) , finds sequence regions that are conserved among target genomes by creating a consensus based on a multiple sequence alignment. WGASA is the software used in the analyses here to create an alignment and will be discussed below. Next, the DNA Signature Pipeline identifies regions that are unique in the target sequence consensus relative to all other non-target bacterial and viral sequences that we have in a >1 Gb database, which is frequently updated from the NCBI GenBank sequence database (11) and other sources (e.g. our collaborators at the CDC, USDA and other public sources, such as TIGR, Sanger Institute and the Joint Genome Institute). From the conserved, unique regions, signatures are selected based on the requirements of a particular technology, in this case, TaqMan PCR. These signature candidates may then proceed for further in silico screening (BLAST analyses to look for undesired inexact matches) before undergoing laboratory screening. For an SAP run, first a pool of target genome and a pool of NN genomes are collected. Then many random subsamples of target and NN genomes are selected from the pool, and each subsample is run through either the DNA signature pipeline or the protein signature pipeline, which identify regions conserved among target genomes and unique relative to non-target genomes, where unique regions are evaluated by comparing to a large sequence database of all currently available bacterial and viral complete genomes or the non-redundant protein database, excluding NNs from the NN pool that are not in that random subsample. Thus, each run of the SAP requires many runs of the DNA or protein signature pipelines with different random samples, generating a range of outcomes that are plotted on range plots.