A new method for detecting mixed Mycobacterium tuberculosis infection and reconstructing constituent strains provides insights into transmission.

ABSTRACT:

Background

Mixed infection with multiple strains of the same pathogen in a single host can present clinical and analytical challenges. Whole genome sequence (WGS) data can identify signals of multiple strains in samples, though the precision of previous methods can be improved. Here, we present MixInfect2, a new tool to accurately detect mixed samples from Mycobacterium tuberculosis short-read WGS data. We then evaluate three approaches for reconstructing the underlying mixed constituent strain sequences. This allows these samples to be included in downstream analysis to gain insights into the epidemiology and transmission of mixed infections.

Methods

We employed a Gaussian mixture model to cluster allele frequencies at mixed sites (hSNPs) in each sample to identify signals

SUBMITTER: Sobkowiak B 

PROVIDER: S-EPMC11771024 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature