Identifying mixed Mycobacterium tuberculosis infections from whole genome sequence data.

ABSTRACT:

Background

Mixed, polyclonal Mycobacterium tuberculosis infection occurs in natural populations. Developing an effective method for detecting such cases is important in measuring the success of treatment and reconstruction of transmission between patients. Using whole genome sequence (WGS) data, we assess two methods for detecting mixed infection: (i) a combination of the number of heterozygous sites and the proportion of heterozygous sites to total SNPs, and (ii) Bayesian model-based clustering of allele frequencies from sequencing reads at heterozygous sites.

Results

In silico and in vitro artificially mixed and known pure M. tuberculosis samples were analysed to determine the specificity and sensitivity of each method. We found that both approaches were effective in disti

SUBMITTER: Sobkowiak B 

PROVIDER: S-EPMC6092779 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature