In order to make specific virus-positive calls, we implemented a set of rules to minimize false positives while maintaining sensitivity. In order to reduce the number of false positive sequences that may share identity equally with both viral and non-viral genomes, we restricted our analysis to those queries whose best alignments were only to animal viral sequences. In a number of datasets, we detected human klassevirus 1, a virus identified and studied in our lab [26] , human poliovirus, used in our Virochip sensitivity experiments, sequences from mosquito densoviruses, also studied in the lab, as well as Moloney murine leukemia virus (MMLV), the polymerase of which was used in the sequence library preparation. We believe these reads represent lab contaminants, and others studies that prepared sequence libraries in the same location have reported similar findings [41] . To account for these contaminants, positive calls were only made on viruses for which there were more supporting reads than there were reads to any known contami-nant. Finally, in order to avoid making calls based on potentially spurious alignments, we considered only those viruses for which there were at least 10 reads supporting their presence.