Figure 6. Plot showing the total number of draft and complete bacterial genomes in GenBank and 323 the percentage that are complete as a function of year. These plots were made by parsing the 324 “prokaryotes.txt ” that NCBI 325 provides as an inventory of all bacterial genomes. Data accessed on May 24, 2019. For viral 326 genomes, the plots are based on the viral genome summary table: 327 https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?VirusLineage_ss=Viruses,%20taxid:10239 328 &SeqType_s=Nucleotide . The data were accessed on August 22, 2019. 332 exclusivity and environmental background databases. For computational efficiency, we 333 recommend populating the exclusivity database with near-neighbor sequences (i.e. organisms 334 that are phylogenetically distinct but closely related to those in the inclusivity dataset). All other 335 distantly related organisms that may be present in the sample matrix and might cause false 336 positives, can be placed into the background database. Further, we recommend that the 337 background database consists of unrelated genomes that cover the normal flora that can be 338 present in a clinical matrix or other potential interfering microorganism contaminants (e.g., non- 339 target soil microorganisms in an environmental sample, etc.). For both the exclusivity and 340 background databases, sequence quality is generally not an issue and it is recommended to 341 include partial sequences as well as complete genomes. A common practice is to check primers 342 for reactivity with all known organisms (such as the GenBank non-redundant (nr) or nucleotide 343 (nt) databases) using a program such as BLAST or primer BLAST to detect all off-target hits and 344 amplicons. However, it is not recommended to use such exhaustive databases during the design 345 329 330 4.3.3 Exclusivity and Background Databases 331 For the purposes of checking for false positive amplifications, it is useful to construct
19