2021/03/24 by Einar Gabbassov, Miguel Moreno-Molina, Iñaki Comas +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Medicine · #Biology #Computational biology #Computer science #Data science #Gene #Genetics #Genome #Genomics and Phylogenetic Studies #Geography #Medicine #Microbiology #Mycobacterium research and diagnosis #Mycobacterium tuberculosis #Mycobacterium tuberculosis complex #Public health #Realm #Tuberculosis #Tuberculosis Research and Epidemiology #Whole genome sequencing
paper · pdf · doi:10.1099/mgen.0.000607
openalex publication_date 2021/03/24 · openalex created_date 2021/07/05 · openalex updated_date 2026/08/05
The occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited. In this paper we introduce SplitStrains , a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains. We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology.