SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data. Issue 6 (24th June 2021)

• Record Type: Journal Article
• Authors: Gabbassov, Einar; Moreno-Molina, Miguel; Comas, Iñaki; Libbrecht, Maxwell; Chindelevitch, Leonid
• Is Part Of: Microbial genomics. Volume 7:Issue 6(2021)
• Publication Date: 2021-06-24
• Subjects: hetero-resistance -- public health microbiology -- maximum likelihood -- mixed infection -- Mycobacterium tuberculosis -- multiple-strain infection; Microbial genomics -- Periodicals; 572.8629
• DOI: 10.1099/mgen.0.000607
• Languages: English
• ISSNs: 2057-5858

Abstract

Abstract : 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.