Control of Artifactual Variation in Reported Intersample Relatedness during Clinical Use of a Mycobacterium tuberculosis Sequencing Pipeline

<jats:title>ABSTRACT</jats:title> <jats:p>Contact tracing requires reliable identification of closely related bacterial isolates. When we noticed the reporting of artifactual variation between <jats:named-content content-type="genus-species">Mycobacterium tuberculosis</jats:named-content> isolates during routine next-generation sequencing of <jats:named-content content-type="genus-species">Mycobacterium</jats:named-content> spp., we investigated its basis in 2,018 consecutive <jats:named-content content-type="genus-species">M. tuberculosis</jats:named-content> isolates. In the routine process used, clinical samples were decontaminated and inoculated into broth cultures; from positive broth cultures DNA was extracted and sequenced, reads were mapped, and consensus sequences were determined. We investigated the process of consensus sequence determination, which selects the most common nucleotide at each position. Having determined the high-quality read depth and depth of minor variants across 8,006 <jats:named-content content-type="genus-species">M. tuberculosis</jats:named-content> genomic regions, we quantified the relationship between the minor variant depth and the amount of nonmycobacterial bacterial DNA, which originates from commensal microbes killed during sample decontamination. In the presence of nonmycobacterial bacterial DNA, we found significant increases in minor variant frequencies, of more than 1.5-fold, in 242 regions covering 5.1% of the <jats:named-content content-type="genus-species">M. tuberculosis</jats:named-content> genome. Included within these were four high-variation regions strongly influenced by the amount of nonmycobacterial bacterial DNA. Excluding these four regions from pairwise distance comparisons reduced biologically implausible variation from 5.2% to 0% in an independent validation set derived from 226 individuals. Thus, we demonstrated an approach identifying critical genomic regions contributing to clinically relevant artifactual variation in bacterial similarity searches. The approach described monitors the outputs of the complex multistep laboratory and bioinformatics process, allows periodic process adjustments, and will have application to quality control of routine bacterial genomics.</jats:p>