SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

16 p´ginas, 12 figuras, 1 tabla. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Supplementary data files can be found at 10.6084/m9.figshare.14562321.
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
This work has been funded in part by a CANSSI Collaborative Research Team grant, 'Statistical methods for challenging problems in public health microbiology' and a Genome Canada grant, 'Machine Learning Methods to Predict Drug Resistance in Pathogenic Bacteria'. LC acknowledges funding from a Sloan Foundation fellowship (FG-2016–6392) and the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, and is part of the EDCTP2 programme supported by the European Union. ML acknowledges funding from a NSERC Discovery grant.